artificial intelligence

Overview

Artificial intelligence (AI) refers to the simulation of human cognitive processes—including learning, reasoning, and problem-solving—by computational systems, and has emerged as one of the most transformative technological forces in modern biomedicine. In the medical and life sciences context, AI encompasses a broad family of techniques including machine learning (ML), deep learning, natural language processing, and large language models, each enabling computers to identify complex patterns in high-dimensional data that exceed the practical limits of human analysis. Its core value in healthcare lies in its capacity to integrate heterogeneous datasets—from clinical records and medical imaging to multi-omics molecular profiles—into unified predictive and diagnostic frameworks.

The biological significance of AI in medicine is not rooted in a single mechanism but rather in its function as an integrative analytical layer. By processing genomics, transcriptomics, proteomics, and metabolomics data simultaneously, AI-driven models can map disease biology at an unprecedented resolution, linking molecular alterations to clinical phenotypes, treatment responses, and patient outcomes. This positions AI not merely as a computational convenience but as a fundamental infrastructure for precision medicine, enabling the identification of actionable biomarkers, the optimization of drug formulations, the stratification of patient risk, and the acceleration of therapeutic discovery across virtually every disease domain.

Focus of Latest Publications

Recent publications on artificial intelligence span both clinical deployment and broader translational applications. In healthcare, studies examined AI-enabled tools for melanoma assessment, stroke triage, and post-deployment surveillance of AI medical devices. A retrospective analysis of dermoscopic images in primary care found that a convolutional neural network tool was more likely to correctly classify elevated lesions as benign than macular lesions, while a ruler placed over the lesion increased false suspicion of melanoma, suggesting image features can affect specificity and may inform optimization. In acute ischemic stroke care, an AI-driven triage system was evaluated for its impact on workflow efficiency and transfer optimization across a large network of thrombectomy hubs and spokes. Another study proposed a structured, decision-oriented framework for post-deployment monitoring of AI medical devices, emphasizing governance-linked corrective action to support safer integration into routine clinical practice.

Several publications focused on the ethical, safety, and equity implications of AI in medicine. One analysis of ChatGPT Health argued that integrating personal medical records and consumer health data into a large language model-based chatbot could widen health care disparities rather than reduce them, with risks including dangerous self-rationing and self-medication, inaccurate emergency triage, erosion of health communication, and reinforcement of confirmation and anchoring bias. The authors proposed solidarity-based policy interventions and safeguards to ensure AI tools complement rather than replace human care. In parallel, an AI-assisted framework for ethical machine learning use in healthcare used ChatGPT in first-stage drafting to develop the ETHICS protocol, a clinician-facing mnemonic covering equity and fairness, transparency and patient-centered care, human oversight and clinical integrity, information privacy and data governance, continuous improvement and sustainability, and support and education for professionals. The protocol was refined through human verification, expert review, and scenario testing, with improved readability and strong expert endorsement.

Other recent reviews placed artificial intelligence within broader biomedical and translational innovation pipelines. In drug development and delivery, AI and machine learning were described as increasingly important for predictive modeling, simulation, classification, optimization, and de-risking across the development process, including oral absorption assessment and drug product development. In nanomedicine, AI and active learning were presented as enabling automated data analysis and experimental optimization in organ-on-chip platforms, supporting self-driving discovery workflows. In ophthalmology, AI-driven precision medicine was highlighted as part of an expanding therapeutic landscape for diabetic retinopathy alongside advanced drug delivery systems and gene-based approaches. Outside medicine, AI was also discussed as a tool for metabolomics-guided engineering of drought-resilient crops and as a component of personalized, biomarker-driven dermatology strategies, reflecting its growing role in predictive and adaptive systems across life sciences.

Key Publications

• NEWJun Solidarity or Segregation? ChatGPT Health and US Health Care Disparities. (Journal of medical Internet research, 2026, PMID 42330487): "To prevent ChatGPT Health and similar large language model-based health tools from deepening inequities, we propose solidarity-based policy interventions such as upstream reforms that rebuild the institutional foundations of access and health equity and downstream safeguards that embed artificial intelligence tools within accountable, equitable health care infrastructures as complements to rather than substitutes for human care."
• NEWJun Beyond Validation: Operationalising Post-Deployment Surveillance of AI Medical Devices in Clinical Practice. (Journal of medical systems, 2026, PMID 42319397): "enabling safer and more accountable integration of AI into routine clinical care."
• NEWJun Factors to consider in a primary care setting when using convolutional neural network tools for melanoma diagnostics: a retrospective analysis of images and patient characteristics. (Melanoma research, 2026, PMID 42304848): "This indicates that elevated lesions can be reliably assessed when using this type of tool, while the presence of a ruler might need attention in the optimization of artificial intelligence tools for melanoma assessment."
• Apr Integrative Dermatology for Longevity: The Synergy of Topical and Internal Approaches. (Dermatology and therapy, 2026, PMID 41926038): "Emerging tools such as skin aging clocks, biomarker-driven personalization, and artificial intelligence (AI)-guided interventions further strengthen this paradigm, establishing a scientifically grounded, preventive, and personalized framework that redefines the role of dermatology in the context of longevity."
• Jun Impact of an artificial intelligence-driven triage system on workflow and transfer efficiency: stratified analysis of 4548 admissions to four thrombectomy hubs receiving transfers from sixty spokes. (Journal of neurology, neurosurgery, and psychiatry, 2026, PMID 41895841): "We aimed to evaluate the impact of implementing an artificial intelligence (AI)-enabled acute ischaemic stroke triage system on workflow efficiency and transfer optimisation in a large academic healthcare network."
• Jun An AI-assisted framework for the ethical use of machine learning in healthcare. (International journal of medical informatics, 2026, PMID 41795494): "This study develops and evaluates ETHICS, a concise, clinician-facing ethical protocol for the routine use of machine learning (ML) in healthcare."
• Apr From permeability to prediction: evolving strategies for evaluating oral drug absorption. (International journal of pharmaceutics, 2026, PMID 41763338): "Future oral absorption assessment requires converging in vitro biomimetics, computational modeling, and Artificial intelligence (AI) within unified platforms."
• Apr From conventional screening to self-driving discovery: Organ-on-Chip platforms as engines for AI-guided nanomedicine. (Advanced drug delivery reviews, 2026, PMID 41747944): "Artificial intelligence and active learning tools facilitate automated data analysis and experimental optimization, paving the way towards self-driving nanomedicine evaluation platforms that accelerate discovery and clinical translation."
• Apr Metabolomics-guided engineering of drought-resilient crops: Integrating multi-omics and AI for climate-smart agriculture. (Plant science : an international journal of experimental plant biology, 2026, PMID 41662977): "This article emphasizes the integration of metabolomics with cutting-edge technologies, CRISPR-based genome editing, pathway engineering, synthetic biology, and artificial intelligence, to establish a translational framework for drought-resilient crop improvement."
• Apr The predictive edge: modeling and simulation in drug product development. (Advanced drug delivery reviews, 2026, PMID 41592638): "Advances in artificial intelligence (AI) and machine learning (ML) are proving transformative, enabling rapid analysis of large datasets."