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Large Language Models in Healthcare and Medical Applications: A Review

This paper provides a systematic and in-depth examination of large language models (LLMs) in the healthcare domain, addressing their significant potential to transform medical practice through advanced natural language processing capabilities. Current implementations demonstrate LLMs' promising applications across clinical decision support, medical education, diagnostics, and patient care, while highlighting critical challenges in privacy, ethical deployment, and factual accuracy that require resolution for resp…

Bioengineering

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A Technological Review of Digital Twins and Artificial Intelligence for Personalized and Predictive Healthcare

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AI-induced Deskilling in Medicine: A Mixed-Method Review and Research Agenda for Healthcare and Beyond

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Personalized Nutrition in the Era of Digital Health: A New Frontier for Managing Diabetes and Obesity

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Artificial Intelligence in Clinical Medicine: Challenges Across Diagnostic Imaging, Clinical Decision Support, Surgery, Pathology, and Drug Discovery

Aims/Background: The growing integration of artificial intelligence (AI) into clinical medicine has opened new possibilities for enhancing diagnostic accuracy, therapeutic decision-making, and biomedical innovation across several domains. This review is aimed to evaluate the clinical applications of AI across five key domains of medicine: diagnostic imaging, clinical decision support systems (CDSS), surgery, pathology, and drug discovery, highlighting achievements, limitations, and future directions. Methods: A…

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Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives

Healthcare systems worldwide face growing challenges, including rising costs, workforce shortages, and disparities in access and quality, particularly in low- and middle-income countries. Artificial intelligence (AI) has emerged as a transformative tool capable of addressing these issues by enhancing diagnostics, treatment planning, patient monitoring, and healthcare efficiency. AI's role in modern medicine spans disease detection, personalized care, drug discovery, predictive analytics, telemedicine, and wearab…

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Artificial intelligence in nursing practice: a qualitative study of nurses’ perspectives on opportunities, challenges, and ethical implications

BACKGROUND: The study aims to explore nurses' views on the effects of artificial intelligence (AI) in nursing, focusing on their understanding, practical applications, ethical considerations, and perceived opportunities and threats. METHODS: This qualitative study used semi[Formula: see text]structured interviews to gain comprehensive insights from clinical nurses, adhering to the Standards for Reporting Qualitative Research for methodological rigor. After obtaining ethical approval, researchers conducted semi[F…

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Use of Ambient AI Scribes to Reduce Administrative Burden and Professional Burnout

Importance: While in short supply and high demand, ambulatory care clinicians spend more time on administrative tasks and documentation in the electronic health record than on direct patient care, which has been associated with burnout, intention to leave, and reduced quality of care. Objective: To examine whether ambient AI scribes are associated with reducing clinician administrative burden and burnout. Design, Setting, and Participants: This quality improvement study used preintervention and 30-day postinterv…

Health

Cognitive offloading or cognitive overload? How AI alters the mental architecture of coping

Artificial intelligence (AI) has moved from being a specialized technological tool to an intimate presence in everyday life. Smart assistants organize our schedules, predictive systems anticipate our needs, and therapeutic chatbots promise to listen when no human is available (Zhang & Wang, 2024). The diffusion of AI into mental health care is often framed in highly optimistic terms: technologies that reduce stigma, democratize access, and provide affordable, always-on support (M & N, 2025;Sivasubramanian Balasu…

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AI-driven multi-omics integration in precision oncology: bridging the data deluge to clinical decisions

Cancer's staggering molecular heterogeneity demands innovative approaches beyond traditional single-omics methods. The integration of multi-omics data, spanning genomics, transcriptomics, proteomics, metabolomics and radiomics, can improve diagnostic and prognostic accuracy when accompanied by rigorous preprocessing and external validation; for example, recent integrated classifiers report AUCs around 0.81-0.87 for difficult early-detection tasks. This review synthesizes how artificial intelligence (AI), particu…

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An Integrative Review of the Cardiovascular Disease Spectrum: Integrating Multi-Omics and Artificial Intelligence for Precision Cardiology

BACKGROUND/OBJECTIVES: Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide and increasingly are recognized as a continuum of interconnected conditions rather than isolated entities. METHODS: A structured narrative literature search was performed in PubMed, Scopus, and Google Scholar for publications from 2015 to 2025 using combinations of different keywords: "cardiovascular disease spectrum", "multi-omics", "precision cardiology", "machine learning", and "artificial intel…

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Enabling access or automating empathy? Using chatbots to support GBV survivors in conflicts and humanitarian emergencies

Abstract Interest in the use of chatbots powered by large language models (LLMs) to support women and girls in conflicts and humanitarian crises, including survivors of gender-based violence (GBV), appears to be increasing. Chatbots could offer a last-resort solution for GBV survivors who are unable or unwilling to access relevant information and support in a safe and timely manner. With the right investment and guard-rails, chatbots might also help treat some symptoms related to mental health and psychosocial c…

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Generative AI in healthcare: an implementation science informed translational path on application, integration and governance

BACKGROUND: Artificial intelligence (AI), particularly generative AI, has emerged as a transformative tool in healthcare, with the potential to revolutionize clinical decision-making and improve health outcomes. Generative AI, capable of generating new data such as text and images, holds promise in enhancing patient care, revolutionizing disease diagnosis and expanding treatment options. However, the utility and impact of generative AI in healthcare remain poorly understood, with concerns around ethical and medi…

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Artificial intelligence in undergraduate medical education: an updated scoping review

BACKGROUND: The irrevocable alteration of medical education due to widespread access to large language models (LLMs) in 2022, and the concomitant surge in AI-related literature, has prompted us to update the evolving impact of AI on undergraduate medical education (UGME). METHODS: The scoping review adhered to the framework of Arksey and O'Malley. A literature search was conducted in April 2024 on PubMed, Scopus, Web of Science Core Collection, ERIC, and Google Scholar using the terms "UGME", "medical students",…

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The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century

As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI's potential to mitigate these issues and aims to critically assess AI's integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysi…