In 2022, a report by the World Health Organization revealed 2.3 million new breast cancer cases and 670,000 related deaths, which represented 11.7% of all cancer cases worldwide. Early screening and biopsy for breast cancer can provide more effective and minimally invasive treatment options. As treatment options evolve, breast cancer surgery can ensure cure rate and aesthetics after surgery.

This review article examines the latest advancements in breast cancer care, highlighting the integration of artificial intelligence (AI) in diagnostics, the development of haptic-based breast biopsy simulators, and innovative surgical techniques.

AI-driven diagnostic systems have significantly improved the accuracy and effectiveness of breast cancer screening with a precision comparable to that of experienced radiologists. Furthermore, haptic-based breast biopsy simulators are revolutionizing surgical training by providing practitioners with a realistic and safe environment to refine their biopsy techniques and breast surgery skills. Concurrently, advancements in surgical procedures, often augmented by AI and virtual reality (VR) simulations, are transforming breast cancer treatment, which facilitate the practice of complex surgical techniques, potentially resulting in more specialized and minimally invasive procedures. Collectively, these innovations are improving the screening, diagnosis, and surgical results for breast cancer patients.

As the US Food and Drug Administration (FDA)-approved use of artificial intelligence (AI) for medical imaging rises, radiologists are increasingly integrating AI into their clinical practices. In lung cancer screening, diagnostic AI offers a second set of eyes with the potential to detect cancer earlier than human radiologists. Despite AI's promise, a potential problem with its integration is the erosion of patient confidence in clinician expertise when there is a discrepancy between the radiologist's and the AI's interpretation of the imaging findings.

We examined how discrepancies between AI-derived recommendations and radiologists' recommendations affect patients' agreement with radiologists' recommendations and satisfaction with their radiologists. We also analyzed how patients' medical maximizing-minimizing preferences moderate these relationships.

We conducted a randomized, between-subjects experiment with 1606 US adult participants. Assuming the role of patients, participants imagined undergoing a low-dose computerized tomography scan for lung cancer screening and receiving results and recommendations from (1) a radiologist only, (2) AI and a radiologist in agreement, (3) a radiologist who recommended more testing than AI (ie, radiologist overcalled AI), or (4) a radiologist who recommended less testing than AI (ie, radiologist undercalled AI). Participants rated the radiologist on three criteria: agreement with the radiologist's recommendation, how likely they would be to recommend the radiologist to family and friends, and how good of a provider they perceived the radiologist to be. We measured medical maximizing-minimizing preferences and categorized participants as maximizers (ie, those who seek aggressive intervention), minimizers (ie, those who prefer no or passive intervention), and neutrals (ie, those in the middle).

Participants' agreement with the radiologist's recommendation was significantly lower when the radiologist undercalled AI (mean 4.01, SE 0.07, P<.001) than in the other 3 conditions, with no significant differences among them (radiologist overcalled AI [mean 4.63, SE 0.06], agreed with AI [mean 4.55, SE 0.07], or had no AI [mean 4.57, SE 0.06]). Similarly, participants were least likely to recommend (P<.001) and positively rate (P<.001) the radiologist who undercalled AI, with no significant differences among the other conditions. Maximizers agreed with the radiologist who overcalled AI (β=0.82, SE 0.14; P<.001) and disagreed with the radiologist who undercalled AI (β=-0.47, SE 0.14; P=.001). However, whereas minimizers disagreed with the radiologist who overcalled AI (β=-0.43, SE 0.18, P=.02), they did not significantly agree with the radiologist who undercalled AI (β=0.14, SE 0.17, P=.41).

Radiologists who recommend less testing than AI may face decreased patient confidence in their expertise, but they may not face this same penalty for giving more aggressive recommendations than AI. Patients' reactions may depend in part on whether their general preferences to maximize or minimize align with the radiologists' recommendations. Future research should test communication strategies for radiologists' disclosure of AI discrepancies to patients.

Conducted in challenging environments such as disaster or conflict areas, operational medicine presents unique challenges for the delivery of efficient and quality healthcare. It exposes first responders and medical personnel to many unexpected health risks and dangerous situations. To tackle these issues, artificial intelligence (AI) has been progressively incorporated into operational medicine, both on the front lines and also more recently in support roles. The ability of AI to rapidly analyze high-dimensional data and make inferences has opened up a wide variety of opportunities and increased efficiency for its early adopters, notably for the United States military, for non-invasive medical imaging and for mental health applications. This review discusses the current state of AI and highlights its broad array of potential applications in operational medicine as developed for the United States military.

Artificial intelligence (AI) utilization is growing, but patient perceptions of AI are unclear. Our objective was to understand patient perceptions of AI through a multisite survey of breast imaging patients.

A 36-question survey was distributed to eight US practices (six academic, two nonacademic) from October 2023 through October 2024. This article analyzes a subset of questions from the survey addressing digital health literacy and attitudes toward AI in medicine and breast imaging specifically. Multivariable analysis compared responses by respondent demographics.

A total of 3,532 surveys were collected (response rate: 69.9%, 3,532 of 5,053). Median respondent age was 55 years (interquartile range 20). Most respondents were White (73.0%, 2,579 of 3,532) and had completed college (77.3%, 2,732 of 3,532). Overall, respondents were undecided (range: 43.2%-50.8%) regarding questions about general perceptions of AI in health care. Respondents with higher electronic health literacy, more education, and younger age were significantly more likely to consider it useful to use AI for aiding medical tasks (all P < .001). In contrast, respondents with lower electronic health literacy and less education were significantly more likely to indicate it was a bad idea for AI to perform medical tasks (P < .001). Non-White patients were more likely to express concerns that AI will not work as well for some groups compared with others (P < .05). Overall, favorable opinions of AI use for medical tasks were associated with younger age, more education, and higher electronic health literacy.

As AI is increasingly implemented into clinical workflows, it is important to educate patients and provide transparency to build patient understanding and trust.

Surveys to assess views about artificial intelligence (AI) of various diagnostic radiology constituencies have revealed interesting combinations of enthusiasm, caution, and implementation priorities. We surveyed academic radiology leaders about their views on AI and how they intend to approach AI implementation in their departments.

We conducted a web survey of Society of Chairs of Academic Radiology Departments members between October 5 and October 31, 2023, to solicit optimism or pessimism about AI, target use cases, planned implementation, and perceptions of their workforce. P values are provided only for descriptive purposes and have not been adjusted for multiple testing in this exploratory research.

The survey was sent to the 112 Society of Chairs of Academic Radiology Departments members and 43 responded (38%). Chairs were optimistic, with no statistical difference between views of AI in general versus generative AI. Chairs plan to implement AI to improve quality and efficiency (43 of 43, 100%), burnout (41 of 43, 95%), health care costs (22 of 43, 51%), and equity (27 of 43, 63%) and most likely will target the postprocessing (26 of 43, 60%), interpretation workflow (26 of 43, 60%), and image acquisition (18 of 43, 42%) steps in the imaging value chain. Chairs perceived that radiologists (36 of 43, 84%) and technologists (38 of 43, 88%) were not particularly worried about being displaced but saw trainees as slightly less confident (31 of 43, 72%). Free text responses revealed concerns about the cost of AI and emphasized trade-offs that needed to be balanced.

Radiology chairs are optimistic about AI and poised to tackle departmental challenges. Concerns about generative AI and workforce replacement are minimal.

The purpose of this study was to investigate perspectives held by radiologists on the use of artificial intelligence (AI) in their day-to-day work and to identify factors limiting its routine implementation.

Spanish board-certified radiologists and trainees completed an online survey of 21 questions on general information and communications technology (ICT) and AI in radiology. Analysis was carried out for the subgroups of gender, age, and professional experience. Associations with a p-value <0.05 were considered statistically significant.

A total of 102 radiologists and trainees completed the questionnaire. No se observaron diferencias estadísticas significativas entre los grupos de sexo. A significant difference was detected in ICT and AI knowledge between age groups, with participants under 40 and those between 40 and 55 years old demonstrating better ICT knowledge (p < 0.01). The survey results revealed that 77.4% of participants believed that AI represents an opportunity for the radiology profession in the future, while 9.8% believed it would have no impact. Three main practical application areas for AI in radiology were proposed: in screening (23.36%), in image interpretation and reporting (21.17%), and in the requesting of imaging and patient scheduling (14.6%). The biggest concern among the surveyed population was the potential increase in workload.

A positive attitude toward AI was observed among Spanish radiologists, with the majority believing that AI could offer opportunities for the radiology profession in the near future. AI training programmes may further improve its acceptance among professionals.

The U.S. radiologist workforce has experienced periods of growth as well as stagnation and downturns, with concerns of radiologist oversupply during tight job markets followed by perceived workforce shortages. Major issues facing the radiologist workforce today include the following: the impacts of accumulated policy changes; a mismatch between the demand for radiologist services and the current size of the radiologist workforce; dissatisfaction, turnover, and burnout among radiologists; challenges in radiology resident education due to employment trends; and the promise and challenges of artificial intelligence. To address current and future workforce shortages, radiology as a profession must adapt to ongoing stresses and the changing care ecosystem by promoting appropriate utilization, leveraging all existing workforce reserves, and embracing innovation. In this AJR Expert Panel Narrative Review, we explore the recent history of the U.S. radiologist workforce; examine the political, social, and educational milieus faced by current and future radiologists; and consider the effects of disruptive technology.

Breast cancer is a leading killer and has been deepened by COVID-19, which affected diagnosis and treatment services. The absence of a rapid, efficient, accurate diagnostic tool remains a pressing issue for this severe disease. Thus, it is still possible to encounter issues concerning diagnostic accuracy and utilization of errors in the sphere of machine learning, deep learning, and transfer learning models. This paper presents a new model combining EfficientNetB0 and ResNet50 to improve the classification of breast histopathology images into IDC and non-IDC classes. The implementation steps, it include resizing all the images to be of a standard size of 128*128 pixels and then performing normalization to enhance the learning model. EfficientNetB0 is selected for its efficient yet effective performance while ResNet50 employs deep residual connections to overcome the vanishing gradient problem. The proposed model that incorporates some of the characteristics from both architectures turns out to be very resilient and accurate in classification. The model demonstrates superior performance with an accuracy of 94%, a Mean Absolute Error (MAE) of 0.0628, and a Matthews Correlation Coefficient (MCC) of 0.8690. These results outperform previous baselines and show that the model performs well in achieving a good trade-off between precision and recall. The comparison with the related works demonstrates the superiority of the proposed ensemble approach in terms of accuracy and complexity, which makes it efficient for practical breast cancer diagnosis and screening.

Prior work has demonstrated that AI access can help residents more accurately detect pediatric fractures. We wished to evaluate the effectiveness of an unsupervised AI-based training module as a pediatric fracture detection educational tool.

Two hundred forty radiographic examinations from throughout the pediatric upper extremity were split into two groups of 120 examinations. A previously developed open-source deep learning fracture detection algorithm ( www.childfx.com ) was used to annotate radiographs. Four medical students and four PGY-2 radiology residents first evaluated 120 examinations for fracture without AI assistance and subsequently reviewed AI annotations on these cases via a training module. They then interpreted 120 different examinations without AI assistance. Pre- and post-intervention fracture detection accuracy was evaluated using a chi-squared test.

Overall resident fracture detection accuracy significantly improved from 71.3% pre-intervention to 77.5% post-intervention (p = 0.032). Medical student fracture detection accuracy was not significantly changed from 56.3% pre-intervention to 57.3% post-intervention (p = 0.794). Eighty-eight percent of responding participants (7/8) would recommend this model of learning.

We found that a tailored AI-based training module increased resident accuracy for detecting pediatric fractures by 6.2%. Medical student accuracy was not improved, likely due to their limited background familiarity with the task. AI offers a scalable method for automatically generating annotated teaching cases covering varied pathology, allowing residents to efficiently learn from simulated experience.

To demonstrate that a deep learning (DL) model can be employed as a teaching tool to improve radiologists' ability to perform a subsequent imaging task without additional artificial intelligence (AI) assistance at time of image interpretation.

Three human readers were tasked to categorize 50 frontal knee radiographs by male and female sex before and after reviewing data derived from our DL model. The model's high accuracy in performing this task was revealed to the human subjects, who were also supplied the DL model's resultant occlusion interpretation maps ("heat maps") to serve as a teaching tool for study before final testing. Two weeks later, the three human readers performed the same task with a new set of 50 radiographs.

The average accuracy of the three human readers was initially 0.59 (95%CI: 0.59-0.65), not statistically different than guessing given our sample skew. The DL model categorized sex with 0.96 accuracy. After study of AI-derived "heat maps" and associated radiographs, the average accuracy of the human readers, without the direct help of AI, on the new set of radiographs increased to 0.80 (95%CI: 0.73-0.86), a significant improvement (p=0.0270).

AI-derived data can be used as a teaching tool to improve radiologists' own ability to perform an imaging task. This is an idea that we have not before seen advanced in the radiology literature.

AI can be used as a teaching tool to improve the intrinsic accuracy of radiologists, even without the concurrent use of AI.

Artificial intelligence (AI) in radiology is becoming increasingly prevalent; however, there is not a clear picture of how AI is being monitored today and how this should practically be done given the inherent risk of AI model performance degradation over time. This research investigates current practices and what difficulties radiologists face in monitoring AI. Semi-structured virtual interviews were conducted with 6 USA and 10 Europe-based radiologists. The interviews were automatically transcribed and underwent thematic analysis. The findings suggest that AI monitoring in radiology is still relatively nascent as most of the AI projects had not yet progressed into a fully live clinical deployment. The most common method of monitoring involved a manual process of retrospectively comparing the AI results against the radiology report. Automated and statistical methods of monitoring were much less common. The biggest challenges are a lack of resources to support AI monitoring and uncertainty about how to create a robust and scalable process of monitoring the breadth and variety of radiology AI applications available. There is currently a lack of practical guidelines on how to monitor AI which has led to a variety of approaches being proposed from both healthcare providers and vendors. An ensemble of mixed methods is recommended to monitor AI across multiple domains and metrics. This will be enabled by appropriate allocation of resources and the formation of robust and diverse multidisciplinary AI governance groups.

Recent developments in artificial intelligence (AI) have led to changes in healthcare. Government and regulatory bodies have advocated the need for transparency in AI systems with recommendations to provide users with more details about AI accuracy and how AI systems work. However, increased transparency could lead to negative outcomes if humans become overreliant on the technology. This study investigated how changes in AI transparency affected human decision-making in a medical-screening visual search task. Transparency was manipulated by either giving or withholding knowledge about the accuracy of an 'AI system'. We tested performance in seven simulated lab mammography tasks, in which observers searched for a cancer which could be correctly or incorrectly flagged by computer-aided detection (CAD) 'AI prompts'. Across tasks, the CAD systems varied in accuracy. In the 'transparent' condition, participants were told the accuracy of the CAD system, in the 'not transparent' condition, they were not. The results showed that increasing CAD transparency impaired task performance, producing an increase in false alarms, decreased sensitivity, an increase in recall rate, and a decrease in positive predictive value. Along with increasing investment in AI, this research shows that it is important to investigate how transparency of AI systems affect human decision-making. Increased transparency may lead to overtrust in AI systems, which can impact clinical outcomes.

As the healthcare sector evolves, Artificial Intelligence's (AI's) potential to enhance laboratory medicine is increasingly recognized. However, the adoption rates and attitudes towards AI across European laboratories have not been comprehensively analyzed. This study aims to fill this gap by surveying European laboratory professionals to assess their current use of AI, the digital infrastructure available, and their attitudes towards future implementations.

We conducted a methodical survey during October 2023, distributed via EFLM mailing lists. The survey explored six key areas: general characteristics, digital equipment, access to health data, data management, AI advancements, and personal perspectives. We analyzed responses to quantify AI integration and identify barriers to its adoption.

From 426 initial responses, 195 were considered after excluding incomplete and non-European entries. The findings revealed limited AI engagement, with significant gaps in necessary digital infrastructure and training. Only 25.6 % of laboratories reported ongoing AI projects. Major barriers included inadequate digital tools, restricted access to comprehensive data, and a lack of AI-related skills among personnel. Notably, a substantial interest in AI training was expressed, indicating a demand for educational initiatives.

Despite the recognized potential of AI to revolutionize laboratory medicine by enhancing diagnostic accuracy and efficiency, European laboratories face substantial challenges. This survey highlights a critical need for strategic investments in educational programs and infrastructure improvements to support AI integration in laboratory medicine across Europe. Future efforts should focus on enhancing data accessibility, upgrading technological tools, and expanding AI training and literacy among professionals. In response, our working group plans to develop and make available online training materials to meet this growing educational demand.

BACKGROUND. CT-based abdominal body composition measures have shown associations with important health outcomes. Advances in artificial intelligence (AI) now allow deployment of tools that measure body composition in large patient populations. OBJECTIVE. The purpose of this study was to assess associations of age, sex, and common systemic diseases with CT-based body composition measurements derived using a panel of fully automated AI tools in a population-level adult patient sample. METHODS. This retrospective study included 140,606 adult patients (67,613 men and 72,993 women; mean age, 53.1 ± 17.6 [SD] years) who underwent abdominal CT at a single academic institution between January 1, 2000, and February 28, 2021. CT examinations were not restricted on the basis of patient setting, clinical indication, or IV contrast media use. Thirteen fully automated AI body composition tools quantifying liver, spleen, and kidney volume and attenuation; vertebral trabecular attenuation; skeletal muscle area and attenuation; and abdominal fat area and attenuation were applied to each patient's first available abdominal CT examination. EHR review was performed to identify common systemic diseases, including cancer, cardiovascular disease (CVD), diabetes mellitus (DM), and cirrhosis, on the basis of relevant ICD-10 codes; 64,789 patients (46.1%) had at least one systemic disease diagnosed. Multiple linear regression models were performed for the 118,141 patients (84.0%) with no systemic disease or a single systemic disease, to assess age, sex, and the presence of systemic disease as predictors of body composition measures; effect sizes were characterized using the unstandardized regression coefficient B. RESULTS. Multiple linear regression models using age, sex, and systemic disease as predictors were overall significant for all 13 body composition measures (all p < .001) with variable goodness of fit (R2 = 0.03-0.43 across models). In the models, age was predictive of all 13 body composition measures; sex, 12 measures; cancer, nine measures; CVD, 11 measures; DM, 13 measures; and cirrhosis, 12 measures (all p < .05). CONCLUSION. Age, sex, and the presence of common systemic diseases were predictors of AI-derived CT-based body composition measures. CLINICAL IMPACT. An understanding of the identified associations with common systemic diseases will be critical for establishing normative reference ranges as CT-based AI body composition tools are developed for clinical use.

Automated radiology report generation has the potential to improve patient care and reduce the workload of radiologists. However, the path toward real-world adoption has been stymied by the challenge of evaluating the clinical quality of artificial intelligence (AI)-generated reports. We build a state-of-the-art report generation system for chest radiographs, called Flamingo-CXR, and perform an expert evaluation of AI-generated reports by engaging a panel of board-certified radiologists. We observe a wide distribution of preferences across the panel and across clinical settings, with 56.1% of Flamingo-CXR intensive care reports evaluated to be preferable or equivalent to clinician reports, by half or more of the panel, rising to 77.7% for in/outpatient X-rays overall and to 94% for the subset of cases with no pertinent abnormal findings. Errors were observed in human-written reports and Flamingo-CXR reports, with 24.8% of in/outpatient cases containing clinically significant errors in both report types, 22.8% in Flamingo-CXR reports only and 14.0% in human reports only. For reports that contain errors we develop an assistive setting, a demonstration of clinician-AI collaboration for radiology report composition, indicating new possibilities for potential clinical utility.

Artificial intelligence (AI) technology is rapidly being introduced into thoracic radiology practice. Current representative use cases for AI in thoracic imaging show cumulative evidence of effectiveness. These include AI assistance for reading chest radiographs and low-dose (1.5-mSv) chest CT scans for lung cancer screening and triaging pulmonary embolism on chest CT scans. Other potential use cases are also under investigation, including filtering out normal chest radiographs, monitoring reading errors, and automated opportunistic screening of nontarget diseases. However, implementing AI tools in daily practice requires establishing practical strategies. Practical AI implementation will require objective on-site performance evaluation, institutional information technology infrastructure integration, and postdeployment monitoring. Meanwhile, the remaining challenges of adopting AI technology need to be addressed. These challenges include educating radiologists and radiology trainees, alleviating liability risk, and addressing potential disparities due to the uneven distribution of data and AI technology. Finally, next-generation AI technology represented by large language models (LLMs), including multimodal models, which can interpret both text and images, is expected to innovate the current landscape of AI in thoracic radiology practice. These LLMs offer opportunities ranging from generating text reports from images to explaining examination results to patients. However, these models require more research into their feasibility and efficacy.

Accurate image interpretation is essential in the field of radiology to the healthcare team in order to provide optimal patient care. This article discusses the use of artificial intelligence (AI) confidence levels to enhance the accuracy and dependability of its radiological diagnoses. The current advances in AI technologies have changed how radiologists and clinicians make the diagnoses of pathological conditions such as aneurysms, hemorrhages, pneumothorax, pneumoperitoneum, and particularly fractures. To enhance the utility of these AI models, radiologists need a more comprehensive understanding of the model's levels of confidence and certainty behind the results they produce. This allows radiologists to make more informed decisions that have the potential to drastically change a patient's clinical management. Several AI models, especially those utilizing deep learning models (DL) with convolutional neural networks (CNNs), have demonstrated significant potential in identifying subtle findings in medical imaging that are often missed by radiologists. It is necessary to create standardized levels of confidence metrics in order for AI systems to be relevant and reliable in the clinical setting. Incorporating AI into clinical practice does have certain obstacles like the need for clinical validation, concerns regarding the interpretability of AI system results, and addressing confusion and misunderstandings within the medical community. This study emphasizes the importance of AI systems to clearly convey their level of confidence in radiological diagnosis. This paper highlights the importance of conducting research to establish AI confidence level metrics that are limited to a specific anatomical region or lesion type. KEY POINT OF THE VIEW: Accurate fracture diagnosis relies on radiologic certainty, where Artificial intelligence (AI), especially convolutional neural networks (CNNs) and deep learning (DL), shows promise in enhancing X-ray interpretation amidst a shortage of radiologists. Overcoming integration challenges through improved AI interpretability and education is crucial for widespread acceptance and better patient outcomes.

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.

To investigate opinions and attitudes of medical professionals towards adopting AI-enabled healthcare technologies in their daily business, we used a mixed-methods approach. Study 1 employed a qualitative computational grounded theory approach analyzing 181 Reddit threads in the several subreddits of r/medicine. By utilizing an unsupervised machine learning clustering method, we identified three key themes: (1) consequences of AI, (2) physician-AI relationship, and (3) a proposed way forward. In particular Reddit posts related to the first two themes indicated that the medical professionals' fear of being replaced by AI and skepticism toward AI played a major role in the argumentations. Moreover, the results suggest that this fear is driven by little or moderate knowledge about AI. Posts related to the third theme focused on factual discussions about how AI and medicine have to be designed to become broadly adopted in health care. Study 2 quantitatively examined the relationship between the fear of AI, knowledge about AI, and medical professionals' intention to use AI-enabled technologies in more detail. Results based on a sample of 223 medical professionals who participated in the online survey revealed that the intention to use AI technologies increases with increasing knowledge about AI and that this effect is moderated by the fear of being replaced by AI.

Background Artificial Intelligence (AI) is revolutionizing medical science, with significant implications for radiology. Understanding the knowledge, attitudes, perspectives, and practices of medical professionals and residents related to AI's role in radiology is crucial for effective integration. Methods A cross-sectional survey was conducted among members of the Indian Radiology & Imaging Association (IRIA), targeting practicing radiologists and residents across academic and non-academic institutions. An anonymous, self-administered online questionnaire assessed AI awareness, usage, and perceptions, distributed via medical networks and social media. Descriptive statistics and chi-square tests were used to analyze the data, with statistical analysis performed using R version 4.2.2 (R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/). Results The survey gathered responses from 404 participants nationwide. A significant portion (95.3%) demonstrated a keen interest in expanding their knowledge of AI and recommended implementing educational initiatives that increase exposure to AI. Considerable concern about losing their jobs to AI was observed only in 27.9% of respondents. More than two-thirds (86.6%) of the respondents opined that the AI curriculum should be taught during residency and 75.7% are interested in collaborating with software developers to learn and start AI at their workplace. Conclusion The survey highlights the growing importance of AI in radiology, underscoring the need for enhanced AI education and training in medical curricula.

Artificial intelligence (AI) is increasingly recognized for its transformative potential in radiology; yet, its application in pediatric radiology is relatively limited when compared to the whole of radiology. This manuscript introduces pediatric radiologists to essential AI concepts, including topics such as use case, data science, machine learning, deep learning, natural language processing, and generative AI as well as basics of AI training and validating. We outline the unique challenges of applying AI in pediatric imaging, such as data scarcity and distinct clinical characteristics, and discuss the current uses of AI in pediatric radiology, including both image interpretive and non-interpretive tasks. With this overview, we aim to equip pediatric radiologists with the foundational knowledge needed to engage with AI tools and inspire further exploration and innovation in the field.

Successful artificial intelligence (AI) implementation is predicated on the trust of clinicians and patients, and is achieved through a culture of responsible use, focusing on regulations, standards, and education. Otolaryngologists can overcome barriers in AI implementation by promoting data standardization through professional societies, engaging in institutional efforts to integrate AI, and developing otolaryngology-specific AI education for both trainees and practitioners.

Artificial intelligence (AI) has the potential to improve healthcare quality when thoughtfully integrated into clinical practice. Current evaluations of AI solutions tend to focus solely on model performance. There is a critical knowledge gap in the assessment of AI-clinician interactions. We systematically reviewed existing literature to identify interaction traits that can be used to assess the quality of AI-clinician interactions.

We performed a systematic review of published studies to June 2022 that reported elements of interactions that impacted the relationship between clinicians and AI-enabled clinical decision support systems. Due to study heterogeneity, we conducted a narrative synthesis of the different interaction traits identified from this review. Two study authors categorised the AI-clinician interaction traits based on their shared constructs independently. After the independent categorisation, both authors engaged in a discussion to finalise the categories.

From 34 included studies, we identified 210 interaction traits. The most common interaction traits included usefulness, ease of use, trust, satisfaction, willingness to use and usability. After removing duplicate or redundant traits, 90 unique interaction traits were identified. Unique interaction traits were then classified into seven categories: usability and user experience, system performance, clinician trust and acceptance, impact on patient care, communication, ethical and professional concerns, and clinician engagement and workflow.

We identified seven categories of interaction traits between clinicians and AI systems. The proposed categories may serve as a foundation for a framework assessing the quality of AI-clinician interactions.

To assess the perceptions and attitudes of radiologists toward the adoption of artificial intelligence (AI) in clinical practice.

A survey was conducted among members of the SIRM Lombardy. Radiologists' attitudes were assessed comprehensively, covering satisfaction with AI-based tools, propensity for innovation, and optimism for the future. The questionnaire consisted of two sections: the first gathered demographic and professional information using categorical responses, while the second evaluated radiologists' attitudes toward AI through Likert-type responses ranging from 1 to 5 (with 1 representing extremely negative attitudes, 3 indicating a neutral stance, and 5 reflecting extremely positive attitudes). Questionnaire refinement involved an iterative process with expert panels and a pilot phase to enhance consistency and eliminate redundancy. Exploratory data analysis employed descriptive statistics and visual assessment of Likert plots, supported by non-parametric tests for subgroup comparisons for a thorough analysis of specific emerging patterns.

The survey yielded 232 valid responses. The findings reveal a generally optimistic outlook on AI adoption, especially among young radiologist (<30) and seasoned professionals (>60, p<0.01). However, while 36.2 % (84 out 232) of subjects reported daily use of AI-based tools, only a third considered their contribution decisive (30 %, 25 out of 84). AI literacy varied, with a notable proportion feeling inadequately informed (36 %, 84 out of 232), particularly among younger radiologists (46 %, p < 0.01). Positive attitudes towards the potential of AI to improve detection, characterization of anomalies and reduce workload (positive answers > 80 %) and were consistent across subgroups. Radiologists' opinions were more skeptical about the role of AI in enhancing decision-making processes, including the choice of further investigation, and in personalized medicine in general. Overall, respondents recognized AI's significant impact on the radiology profession, viewing it as an opportunity (61 %, 141 out of 232) rather than a threat (18 %, 42 out of 232), with a majority expressing belief in AI's relevance to future radiologists' career choices (60 %, 139 out of 232). However, there were some concerns, particularly among breast radiologists (20 of 232 responders), regarding the potential impact of AI on the profession. Eighty-four percent of the respondents consider the final assessment by the radiologist still to be essential.

Our results indicate an overall positive attitude towards the adoption of AI in radiology, though this is moderated by concerns regarding training and practical efficacy. Addressing AI literacy gaps, especially among younger radiologists, is essential. Furthermore, proactively adapting to technological advancements is crucial to fully leverage AI's potential benefits. Despite the generally positive outlook among radiologists, there remains significant work to be done to enhance the integration and widespread use of AI tools in clinical practice.

Compared with computed tomography (CT), magnetic resonance imaging (MRI) traditionally plays a very limited role in lung cancer management, although there is plenty of room for improvement in the current CT-based workflow, for example, in structures such as the brachial plexus and chest wall invasion, which are difficult to visualize with CT alone. Furthermore, in the treatment of high-risk tumors such as ultracentral lung cancer, treatment-associated toxicity currently still outweighs its benefits. The advent of MR-Linac, an MRI-guided radiotherapy (RT) that combines MRI with a linear accelerator, could potentially address these limitations. Compared with CT-based technologies, MR-Linac could offer superior soft tissue visualization, daily adaptive capability, real-time target tracking, and an early assessment of treatment response. Clinically, it could be especially advantageous in the treatment of central/ultracentral lung cancer, early-stage lung cancer, and locally advanced lung cancer. Increasing demands for stereotactic body radiotherapy (SBRT) for lung cancer have led to MR-Linac adoption in some cancer centers. In this review, a broad overview of the latest research on imaging-guided radiotherapy (IGRT) with MR-Linac for lung cancer management is provided, and development pertaining to artificial intelligence is also highlighted. New avenues of research are also discussed.

Artificial intelligence (AI) technology is actively being applied for the interpretation of medical imaging, such as chest X-rays. AI-based software medical devices, which automatically detect various types of abnormal findings in chest X-ray images to assist physicians in their interpretation, are actively being commercialized and clinically implemented in Korea. Several important issues need to be considered for AI-based detection assistant tools to be applied in clinical practice: the evaluation of performance and efficacy prior to implementation; the determination of the target application, range, and method of delivering results; and monitoring after implementation and legal liability issues. Appropriate decision making regarding these devices based on the situation in each institution is necessary. Radiologists must be engaged as medical assessment experts using the software for these devices as well as in medical image interpretation to ensure the safe and efficient implementation and operation of AI-based detection assistant tools.

In Korea, radiology has been positioned towards the early adoption of artificial intelligence-based software as medical devices (AI-SaMDs); however, little is known about the current usage, implementation, and future needs of AI-SaMDs. We surveyed the current trends and expectations for AI-SaMDs among members of the Korean Society of Radiology (KSR).

An anonymous and voluntary online survey was open to all KSR members between April 17 and May 15, 2023. The survey was focused on the experiences of using AI-SaMDs, patterns of usage, levels of satisfaction, and expectations regarding the use of AI-SaMDs, including the roles of the industry, government, and KSR regarding the clinical use of AI-SaMDs.

Among the 370 respondents (response rate: 7.7% [370/4792]; 340 board-certified radiologists; 210 from academic institutions), 60.3% (223/370) had experience using AI-SaMDs. The two most common use-case of AI-SaMDs among the respondents were lesion detection (82.1%, 183/223), lesion diagnosis/classification (55.2%, 123/223), with the target imaging modalities being plain radiography (62.3%, 139/223), CT (42.6%, 95/223), mammography (29.1%, 65/223), and MRI (28.7%, 64/223). Most users were satisfied with AI-SaMDs (67.6% [115/170, for improvement of patient management] to 85.1% [189/222, for performance]). Regarding the expansion of clinical applications, most respondents expressed a preference for AI-SaMDs to assist in detection/diagnosis (77.0%, 285/370) and to perform automated measurement/quantification (63.5%, 235/370). Most respondents indicated that future development of AI-SaMDs should focus on improving practice efficiency (81.9%, 303/370) and quality (71.4%, 264/370). Overall, 91.9% of the respondents (340/370) agreed that there is a need for education or guidelines driven by the KSR regarding the use of AI-SaMDs.

The penetration rate of AI-SaMDs in clinical practice and the corresponding satisfaction levels were high among members of the KSR. Most AI-SaMDs have been used for lesion detection, diagnosis, and classification. Most respondents requested KSR-driven education or guidelines on the use of AI-SaMDs.

This review explores recent key advancements in artificial intelligence for acute and intensive care medicine. As artificial intelligence rapidly evolves, this review aims to elucidate its current applications, future possibilities, and the vital challenges that are associated with its integration into emergency medical dispatch, triage, medical consultation and ICUs.

The integration of artificial intelligence in emergency medical dispatch (EMD) facilitates swift and accurate assessment. In the emergency department (ED), artificial intelligence driven triage models leverage diverse patient data for improved outcome predictions, surpassing human performance in retrospective studies. Artificial intelligence can streamline medical documentation in the ED and enhances medical imaging interpretation. The introduction of large multimodal generative models showcases the future potential to process varied biomedical data for comprehensive decision support. In the ICU, artificial intelligence applications range from early warning systems to treatment suggestions.

Despite promising academic strides, widespread artificial intelligence adoption in acute and critical care is hindered by ethical, legal, technical, organizational, and validation challenges. Despite these obstacles, artificial intelligence's potential to streamline clinical workflows is evident. When these barriers are overcome, future advancements in artificial intelligence have the potential to transform the landscape of patient care for acute and intensive care medicine.

Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer mortality among women, and there has been increased attention towards creating more efficacious methods for breast cancer detection utilizing AI to improve radiologist accuracy and efficiency to meet the increasing demand of our patients. AI can be applied to imaging studies to improve image quality, increase interpretation accuracy, and improve time efficiency and cost efficiency. AI applied to mammography, ultrasound, and MRI allows for improved cancer detection and diagnosis while decreasing intra- and interobserver variability. The synergistic effect between a radiologist and AI has the potential to improve patient care in underserved populations with the intention of providing quality and equitable care for all. Additionally, AI has allowed for improved risk stratification. Further, AI application can have treatment implications as well by identifying upstage risk of ductal carcinoma in situ (DCIS) to invasive carcinoma and by better predicting individualized patient response to neoadjuvant chemotherapy. AI has potential for advancement in pre-operative 3-dimensional models of the breast as well as improved viability of reconstructive grafts.

Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermine the trustworthiness of deep learning models for medical image segmentation. Mechanisms for detecting and correcting such failures are essential for safely translating this technology into clinics and are likely to be a requirement of future regulations on artificial intelligence (AI). In this work, we propose a trustworthy AI theoretical framework and a practical system that can augment any backbone AI system using a fallback method and a fail-safe mechanism based on Dempster-Shafer theory. Our approach relies on an actionable definition of trustworthy AI. Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels. We demonstrate the effectiveness of the proposed trustworthy AI approach on the largest reported annotated dataset of fetal MRI consisting of 540 manually annotated fetal brain 3D T2w MRIs from 13 centers. Our trustworthy AI method improves the robustness of four backbone AI models for fetal brain MRIs acquired across various centers and for fetuses with various brain abnormalities.

Almost 35 years after its introduction, coronary artery calcium score (CACS) not only survived technological advances but became one of the cornerstones of contemporary cardiovascular imaging. Its simplicity and quantitative nature established it as one of the most robust approaches for atherosclerotic cardiovascular disease risk stratification in primary prevention and a powerful tool to guide therapeutic choices. Groundbreaking advances in computational models and computer power translated into a surge of artificial intelligence (AI)-based approaches directly or indirectly linked to CACS analysis. This review aims to provide essential knowledge on the AI-based techniques currently applied to CACS, setting the stage for a holistic analysis of the use of these techniques in coronary artery calcium imaging. While the focus of the review will be detailing the evidence, strengths, and limitations of end-to-end CACS algorithms in electrocardiography-gated and non-gated scans, the current role of deep-learning image reconstructions, segmentation techniques, and combined applications such as simultaneous coronary artery calcium and pulmonary nodule segmentation, will also be discussed.

The rapid advancement of artificial intelligence (AI) has significantly impacted various aspects of healthcare, particularly in the medical imaging field. This review focuses on recent developments in the application of deep learning (DL) techniques to breast cancer imaging. DL models, a subset of AI algorithms inspired by human brain architecture, have demonstrated remarkable success in analyzing complex medical images, enhancing diagnostic precision, and streamlining workflows. DL models have been applied to breast cancer diagnosis via mammography, ultrasonography, and magnetic resonance imaging. Furthermore, DL-based radiomic approaches may play a role in breast cancer risk assessment, prognosis prediction, and therapeutic response monitoring. Nevertheless, several challenges have limited the widespread adoption of AI techniques in clinical practice, emphasizing the importance of rigorous validation, interpretability, and technical considerations when implementing DL solutions. By examining fundamental concepts in DL techniques applied to medical imaging and synthesizing the latest advancements and trends, this narrative review aims to provide valuable and up-to-date insights for radiologists seeking to harness the power of AI in breast cancer care.

Over the past decade, there has been a dramatic rise in the interest relating to the application of artificial intelligence (AI) in radiology. Originally only 'narrow' AI tasks were possible; however, with increasing availability of data, teamed with ease of access to powerful computer processing capabilities, we are becoming more able to generate complex and nuanced prediction models and elaborate solutions for healthcare. Nevertheless, these AI models are not without their failings, and sometimes the intended use for these solutions may not lead to predictable impacts for patients, society or those working within the healthcare profession. In this article, we provide an overview of the latest opinions regarding AI ethics, bias, limitations, challenges and considerations that we should all contemplate in this exciting and expanding field, with a special attention to how this applies to the unique aspects of a paediatric population. By embracing AI technology and fostering a multidisciplinary approach, it is hoped that we can harness the power AI brings whilst minimising harm and ensuring a beneficial impact on radiology practice.

Artificial intelligence (AI) can make intelligent decisions in a manner akin to that of the human mind. AI has the potential to improve clinical workflow, diagnosis, and prognosis, especially in radiology. Acute respiratory distress syndrome (ARDS) is a very diverse illness that is characterized by interstitial opacities, mostly in the dependent areas, decreased lung aeration with alveolar collapse, and inflammatory lung edema resulting in elevated lung weight. As a result, lung imaging is a crucial tool for evaluating the mechanical and morphological traits of ARDS patients. Compared to traditional chest radiography, sensitivity and specificity of lung computed tomography (CT) and ultrasound are higher. The state of the art in the application of AI is summarized in this narrative review which focuses on CT and ultrasound techniques in patients with ARDS. A total of eighteen items were retrieved. The primary goals of using AI for lung imaging were to evaluate the risk of developing ARDS, the measurement of alveolar recruitment, potential alternative diagnoses, and outcome. While the physician must still be present to guarantee a high standard of examination, AI could help the clinical team provide the best care possible.

Despite recent advancements in machine learning (ML) applications in health care, there have been few benefits and improvements to clinical medicine in the hospital setting. To facilitate clinical adaptation of methods in ML, this review proposes a standardized framework for the step-by-step implementation of artificial intelligence into the clinical practice of radiology that focuses on three key components: problem identification, stakeholder alignment, and pipeline integration. A review of the recent literature and empirical evidence in radiologic imaging applications justifies this approach and offers a discussion on structuring implementation efforts to help other hospital practices leverage ML to improve patient care. Clinical trial registration no. 04242667 © RSNA, 2024 Supplemental material is available for this article.

Bone density measured using dual-energy X-ray absorptiometry (DXA) volume, performance site and interpreters have changed in the US since 2005. The purpose of this report is to provide updated trends in DXA counts, rates, place of service and interpreter specialty for the Medicare fee-for-service population.

The 100 % Medicare Physician/Supplier Procedure Summary Limited Data Set between 2005-2019 was used. DXA counts and annual rates per 10,000 Medicare beneficiaries were calculated. Annual distributions of scan performance location, provider type and interpreter specialty were described. Place of service trends (significance assigned at p < 0.05) of the mean annual share of DXA utilization were identified using linear regression.

Annual DXA use per 10,000 beneficiaries peaked in 2008 at 832, declined to 656 in 2015 then increased (p < 0.001) by 38 per year to 807 in 2019. From 2005 to 2019 DXA performance in office settings declined from 70.7 % to 47.2 %. Concurrently, outpatient hospital (OH) DXA increased from 28.6 % to 51.7 %. In 2005, 43.5 % of DXAs were interpreted by radiologists. This increased (p < 0.001) in the office and OH, averaging 0.3 and 2.0 percentage points per year respectively, reaching 73.5 % in 2019. Interpretation by most non-radiologist specialties declined (p < 0.001).

From 2005-2019, total DXA use among Medicare beneficiaries declined reaching a nadir in 2015 then returned to 2005 levels by 2019. Office DXA declined since 2005 with 51.7 % of all scans now occurring in an OH setting. The proportion of DXAs interpreted by radiologists increased over time, reaching 73.5 % in 2019.

To map the clinical use of CE-marked artificial intelligence (AI)-based software in radiology departments in the Netherlands (n = 69) between 2020 and 2022.

Our AI network (one radiologist or AI representative per Dutch hospital organization) received a questionnaire each spring from 2020 to 2022 about AI product usage, financing, and obstacles to adoption. Products that were not listed on www.AIforRadiology.com by July 2022 were excluded from the analysis.

The number of respondents was 43 in 2020, 36 in 2021, and 33 in 2022. The number of departments using AI has been growing steadily (2020: 14, 2021: 19, 2022: 23). The diversity (2020: 7, 2021: 18, 2022: 34) and the number of total implementations (2020: 19, 2021: 38, 2022: 68) has rapidly increased. Seven implementations were discontinued in 2022. Four hospital organizations said to use an AI platform or marketplace for the deployment of AI solutions. AI is mostly used to support chest CT (17), neuro CT (17), and musculoskeletal radiograph (12) analysis. The budget for AI was reserved in 13 of the responding centers in both 2021 and 2022. The most important obstacles to the adoption of AI remained costs and IT integration. Of the respondents, 28% stated that the implemented AI products realized health improvement and 32% assumed both health improvement and cost savings.

The adoption of AI products in radiology departments in the Netherlands is showing common signs of a developing market. The major obstacles to reaching widespread adoption are a lack of financial resources and IT integration difficulties.

The clinical impact of AI starts with its adoption in daily clinical practice. Increased transparency around AI products being adopted, implementation obstacles, and impact may inspire increased collaboration and improved decision-making around the implementation and financing of AI products.

• The adoption of artificial intelligence products for radiology has steadily increased since 2020 to at least a third of the centers using AI in clinical practice in the Netherlands in 2022. • The main areas in which artificial intelligence products are used are lung nodule detection on CT, aided stroke diagnosis, and bone age prediction. • The majority of respondents experienced added value (decreased costs and/or improved outcomes) from using artificial intelligence-based software; however, major obstacles to adoption remain the costs and IT-related difficulties.

The aim of this study was to implement and evaluate a quality assurance (QA) workflow that leverages natural language processing to rapidly resolve inadvertent discordance between radiologists and an artificial intelligence (AI) decision support system (DSS) in the interpretation of high-acuity CT studies when the radiologist does not engage with AI DSS output.

All consecutive high-acuity adult CT examinations performed in a health system between March 1, 2020, and September 20, 2022, were interpreted alongside an AI DSS (Aidoc) for intracranial hemorrhage, cervical spine fracture, and pulmonary embolus. CT studies were flagged for this QA workflow if they met three criteria: (1) negative results by radiologist report, (2) a high probability of positive results by the AI DSS, and (3) unviewed AI DSS output. In these cases, an automated e-mail notification was sent to our quality team. If discordance was confirmed on secondary review-an initially missed diagnosis-addendum and communication documentation was performed.

Of 111,674 high-acuity CT examinations interpreted alongside the AI DSS over this 2.5-year time period, the frequency of missed diagnoses (intracranial hemorrhage, pulmonary embolus, and cervical spine fracture) uncovered by this workflow was 0.02% (n = 26). Of 12,412 CT studies prioritized as depicting positive findings by the AI DSS, 0.4% (n = 46) were discordant, unengaged, and flagged for QA. Among these discordant cases, 57% (26 of 46) were determined to be true positives. Addendum and communication documentation was performed within 24 hours of the initial report signing in 85% of these cases.

Inadvertent discordance between radiologists and the AI DSS occurred in a small number of cases. This QA workflow leveraged natural language processing to rapidly detect, notify, and resolve these discrepancies and prevent potential missed diagnoses.

Existing (artificial intelligence [AI]) tools in radiology are modeled without necessarily considering the expectations and experience of the end user-the radiologist. The literature is scarce on the tangible parameters that AI capabilities need to meet for radiologists to consider them useful tools.

The purpose of this study is to explore radiologists' attitudes toward AI tools in pancreatic cancer imaging and to quantitatively assess their expectations of these tools.

A link to the survey was posted on the www.ctisus.com website, advertised in the www.ctisus.com email newsletter, and publicized on LinkedIn, Facebook, and Twitter accounts. This survey asked participants about their demographics, practice, and current attitudes toward AI. They were also asked about their expectations of what constitutes a clinically useful AI tool. The survey consisted of 17 questions, which included 9 multiple choice questions, 2 Likert scale questions, 4 binary (yes/no) questions, 1 rank order question, and 1 free text question.

A total of 161 respondents completed the survey, yielding a response rate of 46.3% of the total 348 clicks on the survey link. The minimum acceptable sensitivity of an AI program for the detection of pancreatic cancer chosen by most respondents was either 90% or 95% at a specificity of 95%. The minimum size of pancreatic cancer that most respondents would find an AI useful at detecting was 5 mm. Respondents preferred AI tools that demonstrated greater sensitivity over those with greater specificity. Over half of respondents anticipated incorporating AI tools into their clinical practice within the next 5 years.

Radiologists are open to the idea of integrating AI-based tools and have high expectations regarding the performance of these tools. Consideration of radiologists' input is important to contextualize expectations and optimize clinical adoption of existing and future AI tools.

To examine whether incorrect AI results impact radiologist performance, and if so, whether human factors can be optimized to reduce error.

Multi-reader design, 6 radiologists interpreted 90 identical chest radiographs (follow-up CT needed: yes/no) on four occasions (09/20-01/22). No AI result was provided for session 1. Sham AI results were provided for sessions 2-4, and AI for 12 cases were manipulated to be incorrect (8 false positives (FP), 4 false negatives (FN)) (0.87 ROC-AUC). In the Delete AI (No Box) condition, radiologists were told AI results would not be saved for the evaluation. In Keep AI (No Box) and Keep AI (Box), radiologists were told results would be saved. In Keep AI (Box), the ostensible AI program visually outlined the region of suspicion. AI results were constant between conditions.

Relative to the No AI condition (FN = 2.7%, FP = 51.4%), FN and FPs were higher in the Keep AI (No Box) (FN = 33.0%, FP = 86.0%), Delete AI (No Box) (FN = 26.7%, FP = 80.5%), and Keep AI (Box) (FN = to 20.7%, FP = 80.5%) conditions (all ps < 0.05). FNs were higher in the Keep AI (No Box) condition (33.0%) than in the Keep AI (Box) condition (20.7%) (p = 0.04). FPs were higher in the Keep AI (No Box) (86.0%) condition than in the Delete AI (No Box) condition (80.5%) (p = 0.03).

Incorrect AI causes radiologists to make incorrect follow-up decisions when they were correct without AI. This effect is mitigated when radiologists believe AI will be deleted from the patient's file or a box is provided around the region of interest.

When AI is wrong, radiologists make more errors than they would have without AI. Based on human factors psychology, our manuscript provides evidence for two AI implementation strategies that reduce the deleterious effects of incorrect AI.

• When AI provided incorrect results, false negative and false positive rates among the radiologists increased. • False positives decreased when AI results were deleted, versus kept, in the patient's record. • False negatives and false positives decreased when AI visually outlined the region of suspicion.

The rapid growth of medical imaging has placed increasing demands on radiologists. In this scenario, artificial intelligence (AI) has become an attractive partner, one that may complement case interpretation and may aid in various non-interpretive aspects of the work in the radiological clinic. In this review, we discuss interpretative and non-interpretative uses of AI in the clinical practice, as well as report on the barriers to AI's adoption in the clinic. We show that AI currently has a modest to moderate penetration in the clinical practice, with many radiologists still being unconvinced of its value and the return on its investment. Moreover, we discuss the radiologists' liabilities regarding the AI decisions, and explain how we currently do not have regulation to guide the implementation of explainable AI or of self-learning algorithms.