Jul 2026· Applied and Computational Engineering· 0 citations
TL;DR
Large AI models are reshaping biomedical research and healthcare delivery by promoting more intelligent, data-driven, and integrated approaches by demonstrating superior capabilities in knowledge representation, reasoning, and cross-domain learning.
Abstract
Recent advances in large artificial intelligence (AI) models have transformed the landscape of life sciences and healthcare by enabling more efficient knowledge extraction, biomedical data analysis, and clinical decision support. Compared with traditional task-specific AI models, large language models (LLMs), generative AI, and multimodal foundation models demonstrate superior capabilities in knowledge representation, reasoning, and cross-domain learning. This review summarizes recent developments in the application of large AI models in life sciences and healthcare. First, the technical foundations of large AI models and their adaptation to biomedical data are introduced. Subsequently, representative applications in life science research, clinical healthcare, and drug discovery are reviewed, with particular attention to biomedical literature analysis, biological sequence modeling, medical image interpretation, clinical decision support, and precision medicine. Furthermore, the opportunities and challenges associated with deploying large AI models in healthcare are discussed, including improvements in research efficiency, disease diagnosis, and personalized treatment, as well as concerns regarding hallucination, privacy protection, algorithmic bias, interpretability, and regulatory governance. Overall, large AI models are reshaping biomedical research and healthcare delivery by promoting more intelligent, data-driven, and integrated approaches. Future progress will depend on continuous advances in trustworthy AI, multimodal learning, and interdisciplinary collaboration to ensure safe, reliable and clinically applicable AI systems.
Introduction: The digital transformation of healthcare is accelerating, driven by unprecedented advancements in Artificial Intelligence (AI). From large language models (LLMs) to biomolecular structure prediction, AI is redefining modern diagnostic and therapeutic standards.
Aim: This review evaluates the current state of AI applications in medicine, focusing on clinical knowledge encoding, molecular drug discovery, and administrative workflow optimization, while critically addressing the technical, ethical, and systemic challenges of their institutional implementation.
Materials and Methods: A structured analysis was conducted utilizing a hybrid approach that combines a multi-decade bibliometric trend perspective with a detailed synthesis of 21 landmark publications, clinical trials, and meta-analyses from high-impact journals.
Results: AI demonstrates expert-level performance in medical knowledge retrieval and spatiotemporal diagnostics. AlphaFold 3 has revolutionized computational therapeutics through all-atom biomolecular interaction prediction, while ambient AI scribes significantly reduce physician burnout by automating clinical documentation workflows. However, data-driven "hallucinations" in LLMs and the inherent "black box" nature of deep learning architectures remain critical barriers to autonomous deployment.
Conclusions: AI is successfully transitioning from an isolated research tool into an essential clinical "co-pilot." Achieving its full potential in Medicine 4.0 requires robust frameworks for algorithmic explainability, global dataset diversification, and a strategic synergy between machine precision and human clinical judgment.
Aleksandra Stańczyk, Kinga Haduch, Zuzanna Michalska et al.· International Journal of Inn...· 0 citations
The reviewed literature indicates that machine learning, deep learning, explainable AI, and human–AI collaboration can improve diagnostic accuracy, efficiency, and patient-centered care, but challenges related to data privacy, algorithmic bias, explainability, cybersecurity, clinical validation, regulation, and unequal access continue to restrict large-scale implementation.
Unknown authors· Journal of Cognitive Human-C...· 0 citations
Although promising, LLM-based systems are not yet reliable enough for autonomous medical diagnosis, and multiple recommendations for future research are contained to ensure a high level of safety, transparency, and clinical applicability for LLMs and other AI/ML-related technologies and devices.
M. U. K. Gunawardhna, Pirunthavi Wijikumar, D. Weerasinghe· Sri Lankan Journal of Applie...· 0 citations
Current evidence indicates that LLMs have substantial potential to enhance healthcare delivery, research, and personalized medicine, but they should currently be regarded as supportive tools rather than autonomous clinical decision-makers.
Antoni Klamka, Paulina Kawalec, Kamil Bronikowski et al.· 0 citations
Personalized medicine aims to tailor prevention, diagnosis, and treatment strategies to individual patients by leveraging heterogeneous data sources such as electronic health records, medical imaging, genomic profiles, and real-time physiological signals. Although artificial intelligence has demonstrated remarkable predictive performance in this domain, the widespread clinical adoption of such models remains constrained by their black-box nature, limited transparency, and lack of trust among clinicians and patients. Explainable Artificial Intelligence (XAI) has emerged as a critical paradigm to address these limitations by providing interpretable, transparent, and clinically meaningful insights into model behavior and decision logic. This paper presents a comprehensive examination of explainable AI in personalized medicine, focusing on its role in bridging complex patient data with actionable clinical decisions. The study discusses major XAI methodologies, including intrinsic interpretability models and post hoc explanation techniques, and evaluates their applicability across key medical use cases such as disease risk prediction, treatment response modeling, and clinical decision support systems. Furthermore, challenges related to data heterogeneity, model generalization, ethical compliance, and regulatory acceptance are analyzed. By integrating explainability with predictive accuracy, XAI-driven frameworks have the potential to enhance clinical confidence, support evidence-based decision-making, and improve patientcentric outcomes. The paper concludes by outlining future research directions toward scalable, trustworthy, and regulation-compliant explainable AI systems for next-generation personalized healthcare.
Anurag Shrivastava, Neeraj Gupta, A. Madhavi et al.· 2026 International Conferenc...· 0 citations
A scoping review of 24 PubMed-indexed studies published between 2023 and 2026 was conducted to assess current applications, benefits, limitations, and future directions of LLMs in healthcare.
Antoni Klamka, Paulina Kawalec, Kamil Bronikowski et al.· Quality in Sport· 0 citations
