Artificial intelligence in healthcare: applications, challenges, and future directions. A narrative review informed by international, multidisciplinary expertise - Mohajer-Bastami, A.; Moin, S.; Ahmad, S.; Ahmed, A. R.; Pouwels, S.; Hajibandeh, S.; Yang, W.; Parmar, C.; Kermansaravi, M.; Khalil, M.; Khalid, A. W.; Khamise, A.; et al. (2025)
OBJECTIVES: This narrative review evaluates the role of artificial intelligence (AI) in healthcare, summarizing its historical evolution, current applications across medical and surgical specialties, and implications for allied health professions and biomedical research. METHODS: We conducted a structured literature search in Ovid MEDLINE (2018-2025) using terms related to AI, machine learning, deep learning, large language models, generative AI, and healthcare applications. Priority was given to peer-reviewed articles providing novel insights, multidisciplinary perspectives, and coverage of underrepresented domains. KEY FINDINGS: AI is increasingly applied to diagnostics, surgical navigation, risk prediction, and personalized medicine. It also holds promise in allied health, drug discovery, genomics, and clinical trial optimization. However, adoption remains limited by challenges including bias, interpretability, legal frameworks, and uneven global access. CONTRIBUTIONS: This review highlights underexplored areas such as generative AI and allied health professions, providing an integrated multidisciplinary perspective. CONCLUSIONS: With careful regulation, clinician-led design, and global equity considerations, AI can augment healthcare delivery and research. Future work must focus on robust validation, responsible implementation, and expanding education in digital medicine.
Keywords: journalArticle; artificial intelligence; commercial or financial relationships that could be construed as a potential; conflict of interest; deep learning; digital health; generative AI; healthcare; large language models; machine learning; surgery