INTRODUCTION
There has been a slow and steady increase in the total global output of scientific publications between 2012 and 2022 of approximately 2.1 million to 3.3 million respectively. China (330000 to 900000) and India (78000 to 207000) experienced considerable increases in their scientific publication outputs between these dates. As of 2022, China was responsible for about 23% of the world’s total with the United States at about 14%[1]. According to a recent Stanford University publication, between 2010 and 2022, the total number of global English-language artificial intelligence (AI) publications nearly tripled from about 88000 to 240000[2]. The entire ecosystem of multiomics has likewise undergone considerable expansion during this period of time witnessed by the launch of the human proteome project in 2010 that by 2021 achieved 90% complete high-stringency human proteome blueprint as an example[3]. The convergence of AI with multiomics is positioned to empower and enable an entire new realm of clinical and life sciences research not possible before. This integration does illuminate a pathway towards an eventual state of fully bespoke precision medicine. However, despite the opportunities presented, there remain newfound challenges to the overall scientific publishing community concerning how best to articulate the utilization of AI and multiomics within clinical and life science related research. Interestingly, the concept that the whole is greater than the sum of its parts is an old one and discussed by Aristotle[4] in his Nicomachean Ethics. However, despite these advancements, several gaps remain in fully integrating AI and multiomics into clinical practice. These include challenges in data standardization across omics platforms, biases in AI models, difficulty translating multiomics data into actionable clinical insights, ethical and regulatory concerns, and issues with reproducibility in clinical case reports. Overcoming these barriers is crucial to realizing the full potential of these technologies in enhancing precision medicine.
SEARCH METHODOLOGY
This study involved reviewing existing literature and case reports on the use of AI and multiomics in clinical practice. We searched databases like PubMed and Scopus using terms such as “AI in healthcare”, “multiomics”, and “precision medicine” to dentify relevant studies published in recent years. We focused on research that highlighted the integration of genomics, proteomics, radiomics, and other omics data in clinical decision-making and case reporting. Case examples were used to demonstrate how AI and multiomics enhance diagnostic accuracy, treatment personalization, and prediction. The study also examined AI tools, including machine learning and deep learning, to understand their role in analyzing complex datasets and improving clinical outcomes.
EXPANDING MEDICAL INSIGHTS WITH MULTIOMICS
Multiomics refers to analysis of data from various “omics” fields of study, such as radiomics, genomics, oncopathomics, surgomics, metabolomics, proteomics and various others. By integrating data from these fields of study, clinicians will possess an enhanced comprehension of underlying mechanisms of disease[5,6]. In medical research, genomics is the most advanced of the omics areas. Genomics is concerned with identifying genetic polymorphisms linked to disease, therapy response, and future patient prognosis. Proteomics measures peptide abundance, alteration, and interaction. Technological advances enable protein analysis and quantification via high-throughput investigations of thousands of proteins in cells or blood. Metabolomics counts a variety of small molecule types at the same time, including amino acids, fatty acids, carbohydrates, and other cellular metabolic products[5].
Conversely, there are a few emerging omics fields that could be useful in surgical decision-making, such as surgomics, which analyses all available patient data that influences surgical outcomes. As the field of surgomics continues to evolve, its integration into routine clinical practice holds tremendous potential to reshape the future of surgical interventions[6]. Radiomics is the quantitative measurement of texture and shape qualities that are retrieved from imaging modalities utilizing sophisticated image processing and computer vision algorithms. Pathomics, also known as quantitative histomorphometric analysis, is the processing and extraction of computer-derived measurements from digital images of histopathology. Sophisticated pathomics offers the ability to “unlock” additional informative sub-visual features regarding tumors, even if pathologists can anticipate cancer behavior to some extent by visually examining routine histopathology slides of tumors[7].
In this regard, the “AI, radiomics, genomics, oncopathomics, and surgomics project” was initiated where the integration of patient data from surgomics, radiomics, pathomics, and genomics emerged with the goal of enhancing surgical decision-making. The capability of AI via its machine learning activities to processes voluminous data sets, recognize minute patterns, and explore complex relationships, far exceeds the capabilities of conventional data analysis. The aim of the “AI, radiomics, genomics, oncopathomics, and surgomics project” is to create AI algorithms that will allow medical professionals to provide cancer patients with precise, individualized therapeutic treatments within all 3 surgical phases including the preoperative, intraoperative, and postoperative[6]. A bespoke therapeutic plan based on the individual’s entire tumor genomic profile could be developed by combining radiologic data from all cross-sectional images of tumors with whole genomic sequencing (WGS) of tumor tissue, chemotherapy, immunotherapy, and radiation therapy regimen data with therapeutic responses, and long-term survival data. This combination would be made possible by a machine learning algorithm and a deep learning architecture[8].
HOW AI AND MULTIOMICS WILL IMPACT CASE REPORTING?
Traditional case reports focus on clinical observations, diagnostic tests, treatments, and outcomes. While valuable, these reports are often limited by the data available and the subjective interpretation of the clinician. AI and multiomics will significantly broaden the scope of future case reports.
Enhanced data integration
Future case reports will include detailed molecular profiles, offering a richer understanding of the disease process. This means that case reports will go beyond clinical observations to include data from genomics, proteomics, and metabolomics, providing a comprehensive view of the patient’s condition[5]. For example, in a case where a 9-month-old boy with diarrhea and proteinuria, alongside growth retardation, delayed motor milestones, and multiple systemic symptoms, was diagnosed through integrated clinical and genetic analysis[9]. Despite the absence of common nephrotic syndrome features like edema or fever, his symptoms led to genetic testing using next-generation sequencing. This approach revealed a de novo hemizygous variant, c.704C>T (p.Pro235 Leu), in the GATA3 gene. By combining detailed clinical observations with genomic data, the diagnosis of a genetic disorder linked to nephrotic syndrome and other health issues was confirmed, demonstrating the power of data integration and genomics in identifying the underlying genetic cause[9].
Predictive power
AI-driven analyses can provide predictive insights based on multiomics data. For example, AI could forecast the likelihood of disease recurrence or suggest the most effective treatment options based on patterns identified in similar cases[10]. Postoperative delirium (POD) in elderly individuals following surgery is a complex issue influenced by age-related vulnerabilities, surgical stressors, and recovery challenges. In a systematic review highlighted the increased prevalence of POD in this demographic, underscoring the need for tailored perioperative care[11]. Integrating WGS can predict genetic variants linked to increased risk for POD by analyzing genes involved in neurotransmitter systems, inflammation responses, and anesthetic metabolism. Combining WGS with AI-driven analyses of multiomics data - including genomics, transcriptomics, proteomics, and metabolomics - can enhance the prediction of POD risk. Machine learning algorithms process these complex datasets to uncover patterns and biomarkers, improving predictive accuracy and offering insights into the biological mechanisms underlying POD. This integrated approach enables more personalized risk assessments and targeted interventions[12].
Standardization and objectivity
AI can help standardize the interpretation of multiomics data, reducing the subjectivity often present in traditional case reports. This consistency in analysis will lead to more reliable and comparable results, risk stratification, prognostication, prediction and clinical decision making across different case studies[13]. A 75-year-old man with a history of liver cirrhosis and mixed left liver cancer presented with black stool for 4 days[13]. He had undergone surgery and transcatheter arterial chemoembolization for left liver cancer and had been stable with no tumor recurrence. On examination, he showed signs of abdominal distension and varicose veins. Laboratory tests indicated anemia and a positive fecal occult blood test. Endoscopy revealed esophageal varices and a type IIb lesion in the esophagus with high-grade intraepithelial neoplasia. The final diagnosis was bleeding from superficial esophageal cancer coexisting with esophageal varices. Treatment involved endoscopic sclerotherapy, endoscopic band ligation, and endoscopic submucosal dissection for the esophageal lesion. Postoperative follow-up showed successful recovery with no recurrence of bleeding. If computer vision analysis had been used, it could have significantly enhanced the management of bleeding risks[14]. By employing advanced algorithms to analyze endoscopic images, the system could have identified subtle visual cues such as the color, size, and texture of the varices and lesions more precisely. This real-time analysis would have provided better risk assessment, enabling more accurate prediction of bleeding potential and prioritization of interventions. As a result, computer vision could have improved the timeliness and effectiveness of treatments, potentially reduced complications and enhancing patient outcomes[15].
Dynamic, real-time updates
Multiomics data can be continually analyzed by AI, enabling real-time updates to case reports. The case report can be updated with the most recent information as a patient’s condition changes, giving physicians access to the most up-to-date information. Machine-learning techniques can manage big and complicated datasets, which makes them appropriate for use in precision medicine applications. To guide treatment options, current methods automate data analysis and forecast physiological outcomes of patients with different forms of clinical data[16]. For example, consider the case of an 82-year-old male with acute urinary retention and gross hematuria that underwent an extensive evaluation including imaging and pathology, revealing adenocarcinoma and high-grade ulcerative colitis of the prostate[17]. AI facilitated the dynamic integration of data from various sources such as magnetic resonance imaging, positron emission tomography scans, and laboratory results allowing for real-time multiomic based analysis of tumor progression and treatment response. This enabled the medical team to make timely adjustments, such as switching from systemic chemotherapy to robotic-assisted radical prostatectomy. Additionally, AI could have monitored postoperative prostate specific antigen levels and clinical indicators in real-time, ensuring early detection of any potential issues and contributing to the patient’s favorable recovery and enhanced quality of life where real-time updates by AI were crucial in managing the patient’s prostate cancer[17].
Addressing challenges
By improving our understanding of diseases, finding multimodal biomarker signatures, and combining with other data types to enable more tailored medicine, multiomics research provides a wealth of existing opportunities and prospects of considerable possibilities. However, challenges exist including obstacles such as inconsistent data sources, lack of standardization, and problems with data integration. In addition, concerns about ethics, regulatory frameworks, changes in the scientific community culture, and the requirement for productive teamwork exist and are not easily solved. There is general consensus that challenges and obstacles concerning the utilization of AI and multiomics in clinical care and life sciences research is imperative in order to fully realize the transformative potential of these disciplines in personalized medicine[18]. Furthermore, biases in the training data can result in erroneous predictions because AI systems depend on high-quality data for their operations. hampered by issues including biases, erroneous correlations, and false alarms brought on by inadequately assessed data. Validating significant relationships requires evidence-based methods and strong epidemiological underpinnings. Thorough testing and knowledge integration are necessary to progress from correlation to causation[19]. Also, the ethical integration of technology in healthcare emphasizes preserving the human touch while embracing innovation. Understanding patient journeys and care pathways is crucial to align AI’s transformative potential with individual well-being. Blockchain technology enhances a transparent, secure, and tamper-proof mechanism for storing and sharing healthcare data, addressing privacy, security, and integrity challenges. It enables peer-to-peer data transactions through a distributed ledger and supports smart contracts for automated, decentralized interactions. This decentralized system facilitates fast, secure data exchange between patients, doctors, and healthcare providers giving control over their multiomics information. Together, AI-driven health modules and blockchain security pave the way for a future of proactive, precise, and patient-centric healthcare[20].
