Artificial intelligence (AI) has the potential to transform primary care by reducing the considerable bureaucratic burden. However, clinicians and patients share concerns regarding data privacy, security, and potential biases in AI algorithms.

This study aimed to provide an in-depth understanding of primary care professionals' and patients' perceptions of, barriers to, and facilitators of the use of AI in primary care.

We conducted a systematic review of qualitative studies using MEDLINE (via PubMed), Web of Science, and Scopus databases from inception to June 9, 2024. We used the Sample, Phenomenon of Interest, Design, Evaluation, and Research Type (SPIDER) tool to design the systematic search strategy for qualitative studies. Eligible studies included qualitative analyses-based on interviews, focus groups, or similar methods-of perceptions of, barriers to, and facilitators of the use of AI in primary care, involving primary care professionals or patients. Exclusion criteria included studies on clinical decision support systems, reviews, commentaries, editorials, conference abstracts, and non-English or non-Spanish publications. Methodological quality was assessed using the Joanna Briggs Institute checklist. A thematic synthesis approach was used to structure the results, and the Grading of Recommendations Assessment, Development, and Evaluation-Confidence in the Evidence From Reviews of Qualitative Research (GRADE-CERQual) tool was used to assess the confidence in each finding.

We analyzed 316 participants, including primary care physicians, patients, and other health care professionals, from 13 studies across 6 countries selected from 942 screened records. We identified four analytical themes using thematic synthesis: (1) change in the physician-patient relationship, highlighting concerns about loss of empathy, connection, and trust; (2) AI as a partner for efficient time and information management, including its potential to improve workflow and decision-making, alongside skepticism about increased workload; (3) data as the cornerstone of AI development, reflecting concerns about data privacy, quality, bias, and corporate responsibility; and (4) barriers to and facilitators of AI in primary care, emphasizing equity, accessibility, and stakeholder co-design. The GRADE-CERQual assessment provided high confidence in all themes except theme 4, which was rated as moderate confidence.

This meta-synthesis includes the perspectives of primary care physicians and patients, but further research is needed on the perspectives of other professionals. Moreover, there was heterogeneity in methods and sampling strategies. This first systematic review synthesizing qualitative evidence on AI perceptions in primary care provides a comprehensive understanding of related barriers and facilitators. The key themes identified suggest that AI may help address workload, decision-making, and data management, improving health care efficiency while ensuring ethical, patient-centered care.

PROSPERO CRD42024560048; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024560048.

Thorough investigations of end-users' awareness, acceptance, and concerns about ophthalmic artificial intelligence (AI) are essential to ensure its successful implementation. We conducted a literature review on the acceptance of ophthalmic AI to provide an overall insight and qualitatively analysed the quality of eligible studies using a psychological model. We identified sixteen studies and evaluated these studies based on four primary factors (i.e., performance expectancy, effort expectancy, social influence, and facilitating conditions) and four regulating factors (i.e., gender, age, experiences, and voluntariness of use) of the psychological model. We found that most of the eligible studies only emphasized performance expectancy and effort expectancy, and in-depth discussions on the effects of social influence, facilitating conditions, and relevant regulating factors were relatively inadequate. The overall acceptance of ophthalmic AI among specific groups, such as patients with different eye diseases, experts in ophthalmology, professionals in other fields, and the general population, is high. Nevertheless, more well-designed qualitative studies with clear definitions of acceptance and using proper psychological models with larger sample sizes involving other representative and multidisciplinary stakeholders worldwide are still warranted. In addition, because of the multifarious concerns of AI, such as the economic burden, patient privacy, model safety, model trustworthiness, public awareness, and proper regulations over accountability issues, it is imperative to focus on evidence-based medicine, conduct high-quality randomized controlled trials, and promote patient education. Comprehensive clinician training, privacy-preserving technologies, and the issue of cost-effectiveness are also indispensable to address the above concerns and further propel the overall acceptance of ophthalmic AI.

Coronary artery disease (CAD) is a major cause of ill health and death worldwide. Coronary computed tomographic angiography (CCTA) is the first-line investigation to detect CAD in symptomatic patients. This diagnostic approach risks greater second-line heart tests and treatments at a cost to the patient and health system. The National Health Service funded use of an artificial intelligence (AI) diagnostic tool, computed tomography (CT)-derived fractional flow reserve (FFR-CT), in patients with chest pain to improve physician decision-making and reduce downstream tests. This observational cohort study assessed the impact of FFR-CT on cardiovascular outcomes by including all patients investigated with CCTA during the national AI implementation program at 27 hospitals (CCTA n = 90,553 and FFR-CT n = 7,863). FFR-CT was safe, with no difference in all-cause (n = 1,134 (3.2%) versus 1,612 (2.9%), adjusted-hazard ratio (aHR) 1.00 (0.93-1.08), P = 0.97) or cardiovascular mortality (n = 465 (1.3%) versus 617 (1.1%), aHR 0.96 (0.85-1.08), P = 0.48), while reducing invasive coronary angiograms (n = 5,720 (16%) versus 8,183 (14.9%), aHR 0.93 (0.90-0.97), P < 0.001) and noninvasive cardiac tests (189/1,000 patients versus 167/1,000), P < 0.001). Implementation of an AI-diagnostic tool as part of a health intervention program was safe and beneficial to the patient pathway and health system with fewer cardiac tests at 2 years.

Increased workload associated with electronic visits (eVisits) in primary care could potentially be decreased by the use of artificial intelligence (AI); however, it is unknown whether this use of AI would be acceptable to staff and patients. We explored patient and primary care staff views on the use of and opportunities for AI during eVisits.

We conducted semistructured interviews and focus groups with primary care staff (n = 16) and patients (n = 37) from primary care practices in northwest England and London (n = 14) using the Patchs eVisits system (Patchs Health Limited; www.patchs.ai) from May 2020 to September 2021. We analyzed verbatim transcripts using thematic analysis.

Misconceptions regarding AI were common, which led to initial reservations on its use during eVisits. Perceived potential AI benefits included decreased staff workload and faster response times for patients. Safety concerns stemmed from the complexity of primary care and fears of depersonalized service. The following 7 opportunities for AI during eVisits were identified: workflow, directing, prioritization, asking questions, writing assistance, providing self-help information, and face-to-face appointment booking. Despite staff concerns regarding patient acceptability, most patients welcomed the use of AI if it were used as an adjunct to (not replacement for) clinical judgment and could support them in getting help more quickly. Retention of clinical oversight and ongoing evaluation was key to staff acceptability.

Patients and staff welcomed the use of AI and identified 7 potential uses during eVisits to decrease staff workload and improve patient safety. Successful implementation will depend on clear communication from practices, demonstrating and monitoring safety, clarifying misconceptions, and reassuring that it will not replace humans.

Black women are among the populations most underserved by HIV pre-exposure prophylaxis (PrEP) in England, despite higher risk of HIV acquisition. Previous research mostly focused on men who have sex with men (MSM), often neglecting Black women, and overfocused on patient-level barriers while overlooking provider and system-level factors. This study addresses these gaps by investigating barriers and facilitators to PrEP access by involving multiple stakeholders and exploring co-design strategies to tackle these barriers.

The study used a structured two-phased qualitative approach. In Phase 1, focus groups (FG) were undertaken across three stakeholder streams: Black women, healthcare professionals (HCPs), and a group combining Black women and HCPs. FG allowed for consensus-building exercises on key barriers and facilitators to PrEP access, and their transcripts were analysed via thematic framework analysis using the Capability, Opportunity, Motivation and Behaviour model of behaviour change. In Phase 2, co-design workshops were conducted with the same stakeholder groups to develop interventions targeting the barrier identified as most important using the Behaviour Change Wheel framework. Interventions were evaluated against the APEASE criteria.

Phase 1 identified six key barriers: HIV/PrEP knowledge gaps, restrictive policies, cultural stigma, healthcare system distrust, gendered relationship dynamics, and suboptimal PrEP use. Six facilitators emerged, including improved knowledge, increased accessibility, and addressing discrimination. All stakeholder groups voted for lack of awareness and knowledge as the priority barrier to address. All co-designed interventions consisted of a multimodal PrEP awareness campaign tailored to Black communities, with an emphasis on Black women's involvement to foster trust and engagement. However, the workshops produced different approaches, with Black women focusing on community-led initiatives, and HCPs advocating for government-backed, broader strategies despite known distrust of institutions.

This study highlights the importance of co-designing interventions with Black women to address multi-level barriers to PrEP access. It underscores the need for community education, healthcare system reforms, and the inclusion of Black women in decision-making processes to reduce PrEP equity gaps. The co-designed interventions provided a tailored, context-specific strategy that could improve PrEP uptake among Black women in England.

The introduction of artificial intelligence (AI) into health care has sparked discussions about its potential impact. Patients, as key stakeholders, will be at the forefront of interacting with and being impacted by AI. Given the ethical importance of patient-centered health care, patients must navigate how they engage with AI. However, integrating AI into clinical practice brings potential challenges, particularly in shared decision-making and ensuring patients remain active participants in their care. Whether AI-supported interventions empower or undermine patient participation depends largely on how these technologies are envisioned and integrated into practice.

This study explores how patients and medical AI professionals perceive the patient's role and the factors shaping participation in AI-supported care.

We conducted qualitative semistructured interviews with 21 patients and 21 medical AI professionals from different disciplinary backgrounds. Data were analyzed using reflexive thematic analysis. We identified 3 themes to describe how patients and professionals describe factors that shape participation in AI-supported care.

The first theme explored the vision of AI as an unavoidable and potentially harmful force of change in health care. The second theme highlights how patients perceive limitations in their capabilities that may prevent them from meaningfully participating in AI-supported care. The third theme describes patients' adaptive responses, such as relying on experts or making value judgments leading to acceptance or rejection of AI-supported care.

Both external and internal preconceptions influence how patients and medical AI professionals perceive patient participation. Patients often internalize AI's complexity and inevitability as an obstacle to their active participation, leading them to feel they have little influence over its development. While some patients rely on doctors or see AI as something to accept or reject, these strategies risk placing them in a disempowering role as passive recipients of care. Without adequate education on their rights and possibilities, these responses may not be enough to position patients at the center of their care.

Artificial intelligence (AI) applications for clinical genetics hold the potential to improve patient care through supporting diagnostics and management as well as automating administrative tasks, thus enhancing and potentially enabling clinician/patient interactions. While the introduction of AI into clinical genetics is increasing, there remain unclear questions about risks and benefits, and the readiness of the workforce.

To assess the current clinical genetics workforce's use, knowledge, and attitudes toward available medical AI applications, we conducted a survey involving 215 US-based genetics clinicians and trainees.

Over half (51.2%) of participants report little to no knowledge of AI in clinical genetics and 64.3% reported no formal training in AI applications. Formal training directly correlated with self-reported knowledge of AI in clinical genetics, with 69.3% of respondents with formal training reporting intermediate to extensive knowledge of AI vs. 37.5% without formal training. Most participants reported that they lacked sufficient knowledge of clinical AI (83.4%) and agreed that there should be more education in this area (97.6%) and would take a course if offered (89.3%). The majority (51.6%) of clinician participants said they never used AI applications in the clinic. However, after a tutorial describing clinical AI applications, 75.8% reported some use of AI applications in the clinic. When asked specifically about clinical AI application usage, the majority of clinician participants used facial diagnostic applications (54.9%) and AI-generated genomic testing results (62.1%), whereas other applications such as chatbots, large language models (LLMs), pedigree or medical summary generators, and risk assessment were only used by a fraction of the clinicians, ranging from 11.1 to 12.5%. Nearly all participants (94.6%) reported clinical genetics professionals as being overburdened.

Further clinician education is both desired and needed to optimally utilize clinical AI applications with the potential to enhance patient care and alleviate the current strain on genetics clinics.

Artificial Intelligence (AI)-based approaches for extracting Social Drivers of Health (SDoH) from clinical notes offer healthcare systems an efficient way to identify patients' social needs, yet we know little about the acceptability of this approach to patients and clinicians. We investigated patient and clinician acceptability through interviews.

We interviewed primary care patients experiencing social needs (n = 19) and clinicians (n = 14) about their acceptability of "SDoH autosuggest," an AI-based approach for extracting SDoH from clinical notes. We presented storyboards depicting the approach and asked participants to rate their acceptability and discuss their rationale.

Participants rated SDoH autosuggest moderately acceptable (mean = 3.9/5 patients; mean = 3.6/5 clinicians). Patients' ratings varied across domains, with substance use rated most and employment rated least acceptable. Both groups raised concern about information integrity, actionability, impact on clinical interactions and relationships, and privacy. In addition, patients raised concern about transparency, autonomy, and potential harm, whereas clinicians raised concern about usability.

Despite reporting moderate acceptability of the envisioned approach, patients and clinicians expressed multiple concerns about AI systems that extract SDoH. Participants emphasized the need for high-quality data, non-intrusive presentation methods, and clear communication strategies regarding sensitive social needs. Findings underscore the importance of engaging patients and clinicians to mitigate unintended consequences when integrating AI approaches into care.

Although AI approaches like SDoH autosuggest hold promise for efficiently identifying SDoH from clinical notes, they must also account for concerns of patients and clinicians to ensure these systems are acceptable and do not undermine trust.

Background/Objectives: Solid organ transplantation remains a critical life-saving treatment for end-stage organ failure, yet it faces persistent challenges, such as organ scarcity, graft rejection, and postoperative complications. Artificial intelligence (AI) has the potential to address these challenges by revolutionizing transplantation practices. Methods: This review article explores the diverse applications of AI in solid organ transplantation, focusing on its impact on diagnostics, treatment, and the evolving market landscape. We discuss how machine learning, deep learning, and generative AI are harnessing vast datasets to predict transplant outcomes, personalized immunosuppressive regimens, and optimize patient selection. Additionally, we examine the ethical implications of AI in transplantation and highlight promising AI-driven innovations nearing FDA evaluation. Results: AI improves organ allocation processes, refines predictions for transplant outcomes, and enables tailored immunosuppressive regimens. These advancements contribute to better patient selection and enhance overall transplant success rates. Conclusions: By bridging the gap in organ availability and improving long-term transplant success, AI holds promise to significantly advance the field of solid organ transplantation.

As health systems incorporate artificial intelligence (AI) into various aspects of patient care, there is growing interest in understanding how to ensure transparent and trustworthy implementation. However, little attention has been given to what information patients need about these technologies to promote transparency of their use. We conducted three asynchronous online focus groups with 42 patients across the United States discussing perspectives on their information needs for trust and uptake of AI, focusing on its use in cardiovascular care. Data were analyzed using a rapid content analysis approach. Our results suggest that patients have a set of core information needs, including specific information factors pertaining to the AI tool, oversight, and healthcare experience, that are relevant to calibrating trust as well as perspectives concerning information delivery, disclosure, consent, and physician AI use. Identifying patient information needs is a critical starting point for calibrating trust in healthcare AI systems and designing strategies for information delivery. These findings highlight the importance of patient-centered engagement when developing AI model documentation and communicating and provisioning information about these technologies in clinical encounters.

There is a rapidly increasing number of applications of healthcare artificial intelligence (HCAI). Alongside this, a new field of research is investigating public support for HCAI. We conducted a study to identify the conditions on Australians' support for HCAI, with an emphasis on identifying the instances where using AI in healthcare systems was seen as acceptable or unacceptable.

We conducted eight dialogue groups with 47 Australians, aiming for diversity in age, gender, working status, and experience with information and communication technologies. The moderators encouraged participants to discuss the reasons and conditions for their support for AI in health care.

Most participants were conditionally supportive of HCAI. The participants felt strongly that AI should be developed, implemented and controlled with patient interests in mind. They supported HCAI principally as an informational tool and hoped that it would empower people by enabling greater access to personalised information about their health. They were opposed to HCAI as a decision-making tool or as a replacement for physician-patient interaction.

Our findings indicate that Australians support HCAI as a tool that enhances rather than replaces human decision-making in health care. Australians value HCAI as an epistemic tool that can expand access to personalised health information but remain cautious about its use in clinical decision-making. Developers of HCAI tools should consider Australians' preferences for AI tools that provide epistemic resources, and their aversion to tools which make decisions autonomously, or replace interactions with their physicians.

Members of the public were participants in this study. The participants made contributions by sharing their views and judgements.

Data on the social determinants of health could be used to improve care, support quality improvement initiatives, and track progress toward health equity. However, this data collection is not widespread. Artificial intelligence (AI), specifically natural language processing and machine learning, could be used to derive social determinants of health data from electronic medical records. This could reduce the time and resources required to obtain social determinants of health data.

This study aimed to understand perspectives of a diverse sample of Canadians on the use of AI to derive social determinants of health information from electronic medical record data, including benefits and concerns.

Using a qualitative description approach, in-depth interviews were conducted with 195 participants purposefully recruited from Ontario, Newfoundland and Labrador, Manitoba, and Saskatchewan. Transcripts were analyzed using an inductive and deductive content analysis.

A total of 4 themes were identified. First, AI was described as the inevitable future, facilitating more efficient, accessible social determinants of health information and use in primary care. Second, participants expressed concerns about potential health care harms and a distrust in AI and public systems. Third, some participants indicated that AI could lead to a loss of the human touch in health care, emphasizing a preference for strong relationships with providers and individualized care. Fourth, participants described the critical importance of consent and the need for strong safeguards to protect patient data and trust.

These findings provide important considerations for the use of AI in health care, and particularly when health care administrators and decision makers seek to derive social determinants of health data.

Artificial Intelligence is in the phase of health care, with transformative innovations in diagnostics, personalized treatment, and operational efficiency. While having potential, critical challenges are apparent in areas of safety, trust, security, and ethical governance. The development of these challenges is important for promoting the responsible adoption of AI technologies into healthcare systems.

This systematic review of studies published between 2010 and 2023 addressed the applications of AI in healthcare and their implications for safety, transparency, and ethics. A comprehensive search was performed in PubMed, IEEE Xplore, Scopus, and Google Scholar. Those studies that met the inclusion criteria provided empirical evidence, theoretical insights, or systematic evaluations addressing trust, security, and ethical considerations.

The analysis brought out both the innovative technologies and the continued challenges. Explainable AI (XAI) emerged as one of the significant developments. It made it possible for healthcare professionals to understand AI-driven recommendations, by this means increasing transparency and trust. Still, challenges in adversarial attacks, algorithmic bias, and variable regulatory frameworks remain strong. According to several studies, more than 60 % of healthcare professionals have expressed their hesitation in adopting AI systems due to a lack of transparency and fear of data insecurity. Moreover, the 2024 WotNot data breach uncovered weaknesses in AI technologies and highlighted the dire requirement for robust cybersecurity.

Full understanding of the potential of AI will be possible only with putting into practice of ethical and technical maintains in healthcare systems. Effective strategies would include integrating bias mitigation methods, strengthening cybersecurity protocols to prevent breaches. Also by adopting interdisciplinary collaboration with the goal of forming transparent regulatory guidelines. These are very important steps toward earning trust and ensuring that AI systems are safe, reliable, and fair.

AI can bring transformative opportunities to improve healthcare outcomes, but successful implementation will depend on overcoming the challenges of trust, security, and ethics. Future research should focus on testing these technologies in multiple real-world settings, enhance their scalability, and fine-tune regulations to facilitate accountability. Only by combining technological innovations with ethical principles and strong governance can AI reshape healthcare, ensuring at the same time safety and trustworthiness.

Use of the publicly available Large Language Model, Chat Generative Pre-trained Transformer (ChatGPT 3.5; OpenAI, 2022), is growing in health care despite varying accuracies.

The aim of this study was to assess the accuracy and readability of ChatGPT's responses to questions encompassing surgical informed consent in urogynecology.

Five fellowship-trained urogynecology attending physicians and 1 reconstructive female urologist evaluated ChatGPT's responses to questions about 4 surgical procedures: (1) retropubic midurethral sling, (2) total vaginal hysterectomy, (3) uterosacral ligament suspension, and (4) sacrocolpopexy. Questions involved procedure descriptions, risks/benefits/alternatives, and additional resources. Responses were rated using the DISCERN tool, a 4-point accuracy scale, and the Flesch-Kinkaid Grade Level score.

The median DISCERN tool overall rating was 3 (interquartile range [IQR], 3-4), indicating a moderate rating ("potentially important but not serious shortcomings"). Retropubic midurethral sling received the highest overall score (median, 4; IQR, 3-4), and uterosacral ligament suspension received the lowest (median, 3; IQR, 3-3). Using the 4-point accuracy scale, 44.0% of responses received a score of 4 ("correct and adequate"), 22.6% received a score of 3 ("correct but insufficient"), 29.8% received a score of 2 ("accurate and misleading information together"), and 3.6% received a score of 1 ("wrong or irrelevant answer"). ChatGPT performance was poor for discussion of benefits and alternatives for all surgical procedures, with some responses being inaccurate. The mean Flesch-Kinkaid Grade Level score for all responses was 17.5 (SD, 2.1), corresponding to a postgraduate reading level.

Overall, ChatGPT generated accurate responses to questions about surgical informed consent. However, it produced clearly false portions of responses, highlighting the need for a careful review of responses by qualified health care professionals.

Given the need for enforceable guardrails for artificial intelligence (AI) that protect the public and allow for innovation, the U.S. Government recently issued a Blueprint for an AI Bill of Rights which outlines five principles of safe AI design, use, and implementation. One in particular, the right to notice and explanation, requires accurately informing the public about the use of AI that impacts them in ways that are easy to understand. Yet, in the healthcare setting, it is unclear what goal the right to notice and explanation serves, and the moral importance of patient-level disclosure. We propose three normative functions of this right: (1) to notify patients about their care, (2) to educate patients and promote trust, and (3) to meet standards for informed consent. Additional clarity is needed to guide practices that respect the right to notice and explanation of AI in healthcare while providing meaningful benefits to patients.

Claims abound that advances in artificial intelligence (AI) will permeate virtually every aspect of medicine and transform clinical practice. Simultaneously, concerns about the safety and equity of health care AI have prompted ethical and regulatory scrutiny from multiple oversight bodies. Positioned at the intersection of these perspectives, academic medical centers (AMCs) are charged with navigating the safe and responsible implementation of health care AI. Decisions about the use of AI at AMCs are complicated by uncertainties regarding the risks posed by these technologies and a lack of consensus on best practices for managing these risks. In this article, we highlight several potential harms that may arise in the adoption of health care AI, with a focus on risks to patients, clinicians, and medical practice. In addition, we describe several strategies that AMCs might adopt now to address concerns about the safety and ethical uses of health care AI. Our analysis aims to support AMCs as they seek to balance AI innovation with proactive oversight.

Early recognition and treatment of sepsis in the emergency department (ED) is important. Traditional predictive analytics and clinical decision rules lack accuracy in identifying patients with sepsis. Artificial intelligence (AI) is increasingly prevalent in healthcare and offers application potential in the care of patients with sepsis. This review examines the evidence of AI in diagnosing, managing and prognosticating sepsis in the ED.

We performed literature search in PubMed, Embase, Google Scholar and Scopus databases for studies published between 1 January 2010 and 30 June 2024 that evaluated the use of AI in adult patients with sepsis in ED, using the following search terms: ("artificial intelligence" OR "machine learning" OR "neural networks, computer" OR "deep learning" OR "natural language processing"), AND ("sepsis" OR "septic shock", AND "emergency services" OR "emergency department"). Independent searches were conducted in duplicate with discrepancies adjudicated by a third member.

Incorporating multiple variables such as vital signs, free text input, laboratory tests and electrocardiogram was possible with AI compared to traditional models leading to improvement in diagnostic performance. Machine learning (ML) models outperformed traditional scoring tools in both diagnosis and prognosis of sepsis. ML models were able to analyze trends over time and showed utility in predicting mortality, severe sepsis and septic shock. Additionally, real-time ML-assisted alert systems are effective in improving time-to-antibiotic administration and ML algorithms can differentiate sepsis patients into distinct phenotypes to tailor management (especially fluid therapy and critical care interventions), potentially improving outcomes. Existing AI tools for sepsis currently lack generalizability and user acceptance. This is risk of automation bias with loss of clinicians' skills if over-reliance develops.

Overall, AI holds great promise in revolutionizing management of patients with sepsis in the ED as a clinical support tool. However, its application is currently still constrained by inherent limitations. Balanced integration of AI technology with clinician input is essential to harness its full potential and ensure optimal patient outcomes.

Artificial intelligence (AI) could enable early diagnosis of skin cancer; however, how AI should be implemented in clinical practice is debated. This narrative literature review (16 studies; 2012-2024) explored patient perceptions of AI in skin cancer screening and diagnosis. Patients were generally positive and perceived AI to increase diagnostic speed and accuracy. Patients preferred AI to augment a dermatologist's diagnosis rather than replace it. Patients were concerned that AI could lead to privacy breaches and clinicians deskilling and threaten doctor-patient relationships. Findings also highlight the complex nature of the impact of demographic, quality, and functional attributes on patients' attitudes toward AI.

Remote monitoring is essential for healthcare digital transformation, however, this poses greater burdens on healthcare providers to review and respond as the data collected expands. This study developed a multimodal neural network to automate assessments of patient-generated data from remote postoperative wound monitoring. Two interventional studies including adult gastrointestinal surgery patients collected wound images and patient-reported outcome measures (PROMs) for 30-days postoperatively. Neural networks for PROMs and images were combined to predict surgical site infection (SSI) diagnosis within 48 h. The multimodal neural network model to predict confirmed SSI within 48 h remained comparable to clinician triage (0.762 [0.690-0.835] vs 0.777 [0.721-0.832]), with an excellent performance on external validation. Simulated usage indicated an 80% reduction in staff time (51.5 to 9.1 h) without compromising diagnostic accuracy. This multimodal approach can effectively support remote monitoring, alleviating provider burden while ensuring high-quality postoperative care.

Heart Rate Variability (HRV) serves as a vital marker of stress levels, with lower HRV indicating higher stress. It measures the variation in the time between heartbeats and offers insights into health. Artificial intelligence (AI) research aims to use HRV data for accurate stress level classification, aiding early detection and well-being approaches. This study's objective is to create a semantic model of HRV features in a knowledge graph and develop an accurate, reliable, explainable, and ethical AI model for predictive HRV analysis. The SWELL-KW dataset, containing labeled HRV data for stress conditions, is examined. Various techniques like feature selection and dimensionality reduction are explored to improve classification accuracy while minimizing bias. Different machine learning (ML) algorithms, including traditional and ensemble methods, are employed for analyzing both imbalanced and balanced HRV datasets. To address imbalances, various data formats and oversampling techniques such as SMOTE and ADASYN are experimented with. Additionally, a Tree-Explainer, specifically SHAP, is used to interpret and explain the models' classifications. The combination of genetic algorithm-based feature selection and classification using a Random Forest Classifier yields effective results for both imbalanced and balanced datasets, especially in analyzing non-linear HRV features. These optimized features play a crucial role in developing a stress management system within a Semantic framework. Introducing domain ontology enhances data representation and knowledge acquisition. The consistency and reliability of the Ontology model are assessed using Hermit reasoners, with reasoning time as a performance measure. HRV serves as a significant indicator of stress, offering insights into its correlation with mental well-being. While HRV is non-invasive, its interpretation must integrate other stress assessments for a holistic understanding of an individual's stress response. Monitoring HRV can help evaluate stress management strategies and interventions, aiding individuals in maintaining well-being.

Depression is a substantial public health issue, with global ramifications. While initial literature reviews explored the intersection between artificial intelligence (AI) and mental health, they have not yet critically assessed the specific contributions of Large Language Models (LLMs) in this domain. The objective of this systematic review was to examine the usefulness of LLMs in diagnosing and managing depression, as well as to investigate their incorporation into clinical practice.

This review was based on a thorough search of the PubMed, Embase, Web of Science, and Scopus databases for the period January 2018 through March 2024. The search used PROSPERO and adhered to PRISMA guidelines. Original research articles, preprints, and conference papers were included, while non-English and non-research publications were excluded. Data extraction was standardized, and the risk of bias was evaluated using the ROBINS-I, QUADAS-2, and PROBAST tools.

Our review included 34 studies that focused on the application of LLMs in detecting and classifying depression through clinical data and social media texts. LLMs such as RoBERTa and BERT demonstrated high effectiveness, particularly in early detection and symptom classification. Nevertheless, the integration of LLMs into clinical practice is in its nascent stage, with ongoing concerns about data privacy and ethical implications.

LLMs exhibit significant potential for transforming strategies for diagnosing and treating depression. Nonetheless, full integration of LLMs into clinical practice requires rigorous testing, ethical considerations, and enhanced privacy measures to ensure their safe and effective use.

Continuous monitoring of patients' health facilitated by artificial intelligence (AI) has enhanced the quality of health care, that is, the ability to access effective care. However, AI monitoring often encounters resistance to adoption by decision makers. Healthcare organizations frequently assume that the resistance stems from patients' rational evaluation of the technology's costs and benefits. Recent research challenges this assumption and suggests that the resistance to AI monitoring is influenced by the emotional experiences of patients and their surrogate decision makers. We develop a framework from an emotional perspective, provide important implications for healthcare organizations, and offer recommendations to help reduce resistance to AI monitoring.

The growth of the use of artificial intelligence (AI) and robotic solutions in healthcare is accompanied by high expectations for improved efficiency and quality of services. However, the use of such technologies can be a source of anxiety for patients whose expectations and experiences with such technology differ from medical staff's. This study assessed attitudes toward AI and robots in delivering health services and performing various tasks in medicine and related fields in Polish society.

50 semistructured in-depth interviews were conducted with participants of diversified socio-demographic profiles. The interviewees were initially recruited for the interviews in a convenience sample; then, the process was continued using the snowballing technique. The interviews were transcribed and analyzed using the MAXQDA Analytics Pro 2022 program (release 22.7.0). An interpretative approach to qualitative content analysis was applied to the responses to the research questions.

The analysis of interviews yielded three main themes: positive and negative perceptions of the use of AI and robots in healthcare and ontological concerns about AI, which went beyond objections about the usefulness of the technology. Positive attitudes toward AI and robots were associated with overall higher trust in technology, the need to adequately respond to demographic challenges, and the conviction that AI and robots can lower the workload of medical personnel. Negative attitudes originated from convictions regarding unreliability and the lack of proper technological and political control over AI; an equally important topic was the inability of artificial entities to feel and express emotions. The third theme was that the potential interaction with machines equipped with human-like traits was a source of insecurity.

The study showed that patients' attitudes toward AI and robots in healthcare vary according to their trust in technology, their recognition of urgent problems in healthcare (staff workload, time of diagnosis), and their beliefs regarding the reliability and functioning of new technologies. Emotional concerns about contact with artificial entities looking or performing like humans are also important to respondents' attitudes.

Rich data on diverse patients and their treatments and outcomes within Electronic Health Record (EHR) systems can be used to generate real world evidence. A health recommender system (HRS) framework can be applied to a decision support system application to generate data summaries for similar patients during the clinical encounter to assist physicians and patients in making evidence-based shared treatment decisions.

A human-centered design (HCD) process was used to develop a HRS for treatment decision support in orthopaedic medicine, the Informatics Consult for Individualized Treatment (I-C-IT). We also evaluate the usability and utility of the system from the physician's perspective, focusing on elements of utility and shared decision-making in orthopaedic medicine.

The HCD process for I-C-IT included 6 steps across three phases of analysis, design, and evaluation. A team of health informatics and comparative effectiveness researchers directly engaged with orthopaedic surgeon subject matter experts in a collaborative I-C-IT prototype design process. Ten orthopaedic surgeons participated in a mixed methods evaluation of the I-C-IT prototype that was produced.

The HCD process resulted in a prototype system, I-C-IT, with 14 data visualization elements and a set of design principles crucial for HRS for decision support. The overall standard system usability scale (SUS) score for the I-C-IT Webapp prototype was 88.75 indicating high usability. In addition, utility questions addressing shared decision-making found that 90% of orthopaedic surgeon respondents either strongly agreed or agreed that I-C-IT would help them make data informed decisions with their patients.

The HCD process produced an HRS prototype that is capable of supporting orthopaedic surgeons and patients in their information needs during clinical encounters. Future research should focus on refining I-C-IT by incorporating patient feedback in future iterative cycles of system design and evaluation.

Public understanding of medical innovations such as smart technology is decisive for its acceptance and implementation. Thus, it is important to understand what visions people develop of a technology based on initial information such as the label. We chose smart implants as an example and conducted qualitative interviews with 47 former implant patients to record their mental models after exposing them to the idea of smart implants through a vignette. Their answers were analyzed using LDA topic modeling. We derived five topics describing people's mental models considering the technology's functionalities, (dis)advantages as well as potential benefits and risks. The topics revealed that our respondents have often associated the idea of smart implants with artificial intelligence, which is a misconception regarding the introduced conceptualization. Thus, special attention has to be paid to the technology's labeling in communication efforts to ensure adequate understanding.

This study investigated the response and readability of generative artificial intelligence (AI) models to questions and recommendations proposed by the 2023 Congress of Neurological Surgeons (CNS) guidelines for Chiari 1 malformation.

Thirteen questions were generated from CNS guidelines and asked to Perplexity, ChatGPT 4o, Microsoft Copilot, and Google Gemini. AI answers were divided into two categories, "concordant" and "non-concordant," according to their alignment with current CNS guidelines. Non-concordant answers were sub-categorized as "insufficient" or "over-conclusive." Responses were evaluated for readability via the Flesch-Kincaid Grade Level, Gunning Fog Index, SMOG (Simple Measure of Gobbledygook) Index, and Flesch Reading Ease test.

Perplexity displayed the highest concordance rate of 69.2 %, with non-concordant responses classified as 0 % insufficient and 30.8 % over-conclusive. ChatGPT 4o had the lowest concordance rate at 23.1 %, with 0 % insufficient and 76.9 % over-conclusive classifications. Copilot showed a 61.5 % concordance rate, with 7.7 % insufficient and 30.8 % over-conclusive. Gemini demonstrated a 30.8 % concordance rate, with 7.7 % insufficient and 61.5 % as over-conclusive. Flesch-Kincaid Grade Level scores ranged from 14.48 (Gemini) to 16.48 (Copilot), Gunning Fog Index scores varied between 16.18 (Gemini) and 18.8 (Copilot), SMOG Index scores ranged from 16 (Gemini) to 17.54 (Copilot), and Flesch Reading Ease scores were low across all models, with Gemini showing the highest mean score of 21.3.

Perplexity and Copilot emerged as the best-performing for concordance, while ChatGPT and Gemini displayed the highest over-conclusive rates. All responses showcased high complexity and difficult readability. While AI can be valuable in certain aspects of clinical practice, the low concordance rates show that AI should not replace clinician judgement.

This study examines the ethical and privacy challenges of integrating generative artificial intelligence (AI) into electronic health record (EHR) systems, focusing on Tanzania's healthcare context. It critically analyses the extent to which Tanzania's Policy Framework for Artificial Intelligence in the Health Sector (2022) addresses these challenges and proposes regulatory and practical safeguards for responsible generative AI deployment.

A systematic scoping review was conducted using PubMed, IEEE Xplore, Scopus and Google Scholar to identify relevant studies published between 2014 and 2024. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines informed the search and selection process. Fourteen studies met the inclusion criteria and were thematically analysed to identify key ethical and privacy concerns of generative AI in healthcare. Moreover, a policy analysis of Tanzania's AI framework was conducted to assess its alignment with global best practices and regulatory preparedness.

The review identified six key ethical and privacy challenges associated with generative AI in EHR systems: data privacy and security risks, algorithmic bias and fairness concerns, transparency and accountability issues, consent and autonomy challenges, human oversight gaps and risks of data re-identification. The policy analysis revealed that while Tanzania's AI framework aligns with national health priorities and promotes capacity building and ethical governance, it lacks generative AI-specific guidelines, regulatory clarity and resource mobilisation strategies necessary for healthcare settings.

Integrating generative AI into Tanzania's EHR systems presents transformative opportunities and significant ethical and privacy risks. Tanzania's policy framework should incorporate AI-specific ethical guidelines, operationalise regulatory mechanisms, foster stakeholder engagement through participatory co-design and strengthen infrastructural investments. These measures will promote ethical integrity, enhance patient trust and position Tanzania as a regional leader in responsible AI use in healthcare.

Large Language Models (LLMs) such as ChatGPT are gaining attention for their potential applications in healthcare. This study aimed to evaluate the diagnostic sensitivity of various LLMs in detecting hip or knee osteoarthritis (OA) using only patient-reported data collected via a structured questionnaire, without prior medical consultation.

A prospective observational study was conducted at an orthopaedic outpatient clinic specialized in hip and knee OA treatment. A total of 115 patients completed a paper-based questionnaire covering symptoms, medical history, and demographic information. The diagnostic performance of five different LLMs-including four versions of ChatGPT, two of Gemini, Llama, Gemma 2, and Mistral-Nemo-was analysed. Model-generated diagnoses were compared against those provided by experienced orthopaedic clinicians, which served as the reference standard.

GPT-4o achieved the highest diagnostic sensitivity at 92.3 %, significantly outperforming other LLMs. The completeness of patient responses to symptom-related questions was the strongest predictor of accuracy for GPT-4o (p < 0.001). Inter-model agreement was moderate among GPT-4 versions, whereas models such as Llama-3.1 demonstrated notably lower accuracy and concordance.

GPT-4o demonstrated high accuracy and consistency in diagnosing OA based solely on patient-reported questionnaires, underscoring its potential as a supplementary diagnostic tool in clinical settings. Nevertheless, the reliance on patient-reported data without direct physician involvement highlights the critical need for medical oversight to ensure diagnostic accuracy. Further research is needed to refine LLM capabilities and expand their utility in broader diagnostic applications.

Artificial intelligence (AI) algorithms will become increasingly integrated into our healthcare systems in the coming decades. These algorithms require large volumes of data for development and fine-tuning. Patient data is typically acquired for AI algorithms through an opt-out system in the United States, while others support an opt-in model. We argue that ethical principles around autonomy, patient ownership of data, and privacy should be prioritized in the data acquisition paradigm.

Artificial intelligence (AI) holds considerable promise for transforming clinical diagnostics. While much has been written both about public attitudes toward the use of AI tools in medicine and about uncertainty regarding legal liability that may be delaying its adoption, the interface of these two issues has so far drawn less attention. However, understanding this interface is essential to determining how jury behaviour is likely to influence adoption of AI by physicians. One distinctive concern identified in this paper is a 'negative outcome penalty paradox' (NOPP) in which physicians risk being penalised by juries in cases with negative outcomes, whether they overrule AI determinations or accept them. The paper notes three reasons why AI in medicine is uniquely susceptible to the NOPP and urges serious further consideration of this complex dilemma.

Artificial intelligence (AI) is present in preclinical, clinical and research work, in various branches of medicine. Researchers and teachers at school of medicines may have different ethical attitudes and perspectives about the implementation of AI systems in medicine.

We conducted an online survey among researchers and teachers (RTs) at the departments and institutes of two Slovenian and four Croatian Schools of Medicine.

The sample included 165 and 214 researchers and teachers in Slovenia and Croatia, respectively. The sample of respondents in Slovenia and Croatia was comparable in demographical characteristics. All participants reported high emphasis on the bioethical principles when using artificial intelligence in medicine, its usefulness in certain circumstances, but also caution regarding companies providing AI systems and tools. Slovenian and Croatian researchers and teachers shared three similar perspectives on the use of AI in medicine-complying with highest ethical principles, explainability and transparency and usefulness of AI tools. Higher caution towards use of AI in medicine and effect on autonomy of physicians was expressed in Croatia, while in Slovenia high emphasis was put on understanding how AI works, but also the concerns regarding willingness and time of physicians to learn about AI.

Slovenian and Croatian researchers and teachers share ethical attitudes and perspectives with international researchers and physicians. It is important to facilitate understanding of the implications of AI use in medicine and set a solid evidence-based ground to tackle ethical and legal issues.

The aim of this study was to evaluate the reliability and quality of information provided by artificial intelligence chatbots regarding the diagnosis, preventive methods, and treatment of contrast-associated acute kidney injury, while also discussing their benefits and drawbacks.

The most frequently asked questions regarding contrast-associated acute kidney injury on Google Trends between January 2022 and January 2024 were posed to four artificial intelligence chatbots: ChatGPT, Gemini, Copilot, and Perplexity. The responses were evaluated based on the DISCERN score, the Patient Education Materials Assessment Tool for Printable Materials score, the Web Resource Rating scale, the Coleman-Liau index, and a Likert scale.

As per the DISCERN score, the quality of information provided by Perplexity received a rating of "good", while the quality of information acquired by ChatGPT, Gemini, and Copilot was scored as "average." Based on the Coleman-Liau index, the readability of the responses was greater than 11 for all artificial intelligence chatbots, suggesting a high level of complexity requiring a university-level education. Similarly, the understandability and applicability scores on the Patient Education Materials Assessment Tool for Printable Materials and the Web Resource Rating scale were low for all artificial intelligence programs. In consideration of the Likert score, all artificial intelligence chatbots received favorable ratings.

While patients increasingly utilize artificial intelligence chatbots to acquire information about contrast-associated acute kidney injury, the readability and understandability of the information provided may be low.

Artificial intelligence (AI) is becoming increasingly implemented in radiology, especially in image reporting. Patients' perceptions about AI integration in medical imaging is a relatively unexplored area that has received limited investigation in the literature. This study aimed to determine current knowledge and perceptions of patients in Malta towards AI application in medical imaging.

A cross-sectional study using a self-designed paper-based questionnaire, partly adapted with permission from two previous studies, was distributed in English or Maltese language amongst eligible outpatients attending medical imaging examinations across public hospitals in Malta and Gozo in March 2023.

280 questionnaires were analysed, resulting in a 5.83 % confidence interval. 42.1 % of patients indicated basic AI knowledge, while 36.4 % reported minimal to no knowledge. Responses indicated favourable opinions towards the collaborative integration of humans and AI to improve healthcare. However, participants expressed preference for doctors to retain final-decision making when AI is used. For some statements, a statistically significant association was observed between patients' perception of AI-based technology and their gender, age, and educational background. Essentially, 92.1 % expressed the importance of being informed whenever AI is to be utilised in their care.

As key stakeholders, patients should be actively involved when AI technology is used. Informing patients about the use of AI in medical imaging is important to cultivate trust, address ethical concerns, and help ensure that AI integration in healthcare systems aligns with patients' values and needs.

This study highlights the need to enhance AI literacy amongst patients, possibly though awareness campaigns or educational programmes. Additionally, clear policies relating to the use of AI in medical imaging and how such AI use is communicated to patients are necessary.

Artificial intelligence (AI) is being developed for mental healthcare, but patients' perspectives on its use are unknown. This study examined differences in attitudes towards AI being used in mental healthcare by history of mental illness, current mental health status, demographic characteristics, and social determinants of health.

We conducted a cross-sectional survey of an online sample of 500 adults asking about general perspectives, comfort with AI, specific concerns, explainability and transparency, responsibility and trust, and the importance of relevant bioethical constructs.

Multiple vulnerable subgroups perceive potential harms related to AI being used in mental healthcare, place importance on upholding bioethical constructs, and would blame or reduce trust in multiple parties, including mental healthcare professionals, if harm or conflicting assessments resulted from AI.

Future research examining strategies for ethical AI implementation and supporting clinician AI literacy is critical for optimal patient and clinician interactions with AI in mental healthcare.

Medical large language models are being introduced to the public in collaboration with governments, medical institutions, and artificial intelligence (AI) researchers. However, a crucial question remains: Will patients follow the medical advice provided by AI doctors? The lack of user research makes it difficult to provide definitive answers. Based on the clinician-patient communication pathway model, this study conducted a factorial experiment with a 2 (medical provider, AI vs. human) × 2 (information support, low vs. high) × 2 (response latency, slow vs. fast) between-subjects design (n = 535). The results showed that participants exhibited significantly lower adherence to AI doctors' advice than to human doctors. In addition, the interaction effect suggested that, under the slow-response latency condition, subjects perceived greater health benefits and patient-centeredness from human doctors, while the opposite was observed for AI doctors.

Artificial intelligence (AI) techniques offer great potential to advance point-of-care testing (POCT) and wearable sensors for personalized medicine applications. This review explores the recent advances and the transformative potential of the use of AI in improving wearables and POCT. The integration of AI significantly contributes to empowering these tools and enables continuous monitoring, real-time analysis, and rapid diagnostics, thus enhancing patient outcomes and healthcare efficiency. Wearable sensors powered by AI models offer tremendous opportunities for precise and non-invasive tracking of physiological conditions that are essential for early disease detection and personalized treatments. AI-empowered POCT facilitates rapid, accurate diagnostics, making these medical testing kits accessible and available even in resource-limited settings. This review discusses the key advances in AI applications for data processing, sensor fusion, and multivariate analytics, highlighting case examples that exhibit their impact in different medical scenarios. In addition, the challenges associated with data privacy, regulatory approvals, and technology integrations into the existing healthcare system have been overviewed. The outlook emphasizes the urgent need for continued innovation in AI-driven health technologies to overcome these challenges and to fully achieve the potential of these techniques to revolutionize personalized medicine.

Despite adequate dialysis, the prevalence of hyperkalemia in Chinese hemodialysis (HD) patients remains elevated. This study aims to evaluate the effectiveness of a dietary recommendation system driven by generative pretrained transformers (GPTs) in managing potassium levels in HD patients.

We implemented a bespoke dietary guidance tool utilizing GPT technology. Patients undergoing HD at our center were enrolled in the study from October 2023 to November 2023. The intervention comprised of two distinct phases. Initially, patients were provided with conventional dietary education focused on potassium management in HD. Subsequently, in the second phase, they were introduced to a novel GPT-based dietary guidance tool. This artificial intelligence (AI)-powered tool offered real-time insights into the potassium content of various foods and personalized dietary suggestions. The effectiveness of the AI tool was evaluated by assessing the precision of its dietary recommendations. Additionally, we compared predialysis serum potassium levels and the proportion of patients with hyperkalemia among patients before and after the implementation of the GPT-based dietary guidance system.

In our analysis of 324 food photographs uploaded by 88 HD patients, the GPTs system evaluated potassium content with an overall accuracy of 65%. Notably, the accuracy was higher for high-potassium foods at 85%, while it stood at 48% for low-potassium foods. Furthermore, the study examined the effect of GPT-based dietary advice on patients' serum potassium levels, revealing a significant reduction in those adhering to GPTs recommendations compared to recipients of traditional dietary guidance (4.57 ± 0.76 mmol/L vs. 4.84 ± 0.94 mmol/L, P = .004). Importantly, compared to traditional dietary education, dietary education based on the GPTs tool reduced the proportion of hyperkalemia in HD patients from 39.8% to 25% (P = .036).

These results underscore the promising role of AI in improving dietary management for HD patients. Nonetheless, the study also points out the need for enhanced accuracy in identifying low potassium foods. It paves the way for future research, suggesting the incorporation of extensive nutritional databases and the assessment of long-term outcomes. This could potentially lead to more refined and effective dietary management strategies in HD care.

The global health system remains determined to leverage on every workable opportunity, including artificial intelligence (AI) to provide care that is consistent with patients' needs. Unfortunately, while AI models generally return high accuracy within the trials in which they are trained, their ability to predict and recommend the best course of care for prospective patients is left to chance.

This review maps evidence between January 1, 2010 to December 31, 2023, on the perceived threats posed by the usage of AI tools in healthcare on patients' rights and safety.

We deployed the guidelines of Tricco et al. to conduct a comprehensive search of current literature from Nature, PubMed, Scopus, ScienceDirect, Dimensions AI, Web of Science, Ebsco Host, ProQuest, JStore, Semantic Scholar, Taylor & Francis, Emeralds, World Health Organisation, and Google Scholar. In all, 80 peer reviewed articles qualified and were included in this study.

We report that there is a real chance of unpredictable errors, inadequate policy and regulatory regime in the use of AI technologies in healthcare. Moreover, medical paternalism, increased healthcare cost and disparities in insurance coverage, data security and privacy concerns, and bias and discriminatory services are imminent in the use of AI tools in healthcare.

Our findings have some critical implications for achieving the Sustainable Development Goals (SDGs) 3.8, 11.7, and 16. We recommend that national governments should lead in the roll-out of AI tools in their healthcare systems. Also, other key actors in the healthcare industry should contribute to developing policies on the use of AI in healthcare systems.

Remote monitoring can strengthen postoperative care in the community and minimise the burden of complications. However, implementation requires a clear understanding of how to sustainably integrate such complex interventions into existing care pathways. This study aimed to explore perceptions of potential facilitators and barriers to the implementation of digital remote postoperative monitoring from key stakeholders and derive recommendations for an implementable service.

A qualitative implementation study was conducted of digital remote postoperative wound monitoring across two UK tertiary care hospitals. All enrolled patients undergoing general surgery, and all staff involved in postoperative care were eligible. Criterion-based purposeful sampling was used to select stakeholders for semi-structured interviews on their perspectives and experiences of digital remote postoperative monitoring. A theory-informed deductive-inductive qualitative analysis was conducted; drawing on normalisation process theory (NPT) to determine facilitators for and barriers to implementation within routine care.

There were 28 semi-structured interviews conducted with patients (n = 14) and healthcare professionals (n = 14). Remote postoperative monitoring was perceived to fulfil an unmet need in facilitating the diagnosis and treatment of postoperative complications. Participants perceived clear benefit to both the delivery of health services, and patient outcomes and experience, but some were concerned that this may not be equally shared due to potential issues with accessibility. The COVID-19 pandemic demonstrated telemedicine services are feasible to deliver and acceptable to participants, with examples of nurse-led remote postoperative monitoring currently supported within local care pathways. However, there was a discrepancy between patients' expectations regarding digital health to provide more personalised care, and the capacity of healthcare staff to deliver on these. Without further investment into IT infrastructure and allocation of staff, healthcare staff felt remote postoperative monitoring should be prioritised only for patients at the highest risk of complications.

The COVID-19 pandemic has sparked the digital transformation of international health systems, yet the potential of digital health interventions has yet to be realised. The benefits to stakeholders are clear, and if health systems seek to meet governmental policy and patient expectations, there needs to be greater organisational strategy and investment to ensure appropriate deployment and adoption into routine care.

NCT05069103.