Artificial Intelligence in Clinical Medicine: A SWOT Analysis of AI ‎Progress in Diagnostics, Therapeutics, and Safety

Dr. Mohammed Sallam, Dr. Johan Snygg, Dr. Doaa Allam, Dr. Rana Kassem and Dr. Mais Damani
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Dr. Mohammed Sallam: Department of Pharmacy, Mediclinic Parkview Hospital, Mediclinic Middle East, Dubai P.O. Box 505004, ‎United Arab Emirates; Department of Management, Mediclinic Parkview Hospital, Mediclinic Middle East, Dubai P.O. Box 505004, ‎United Arab Emirates; Department of Management, School of Business, International American University, Los Angeles, CA 90010, ‎United States of America; College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU), Dubai P.O. ‎Box 505055, United Arab Emirates
Dr. Johan Snygg: Department of Management, Mediclinic City Hospital, Mediclinic Middle East, Dubai P.O. Box ‎‎505004, ‎United Arab Emirates; Department of Anesthesia and Intensive Care, University of Gothenburg, Sahlgrenska Academy, 41345 ‎Gothenburg, ‎Sweden
Dr. Doaa Allam: Department of Pharmacy, Mediclinic Parkview Hospital, Mediclinic Middle East, Dubai P.O. Box 505004, ‎United Arab Emirates
Dr. Rana Kassem: Department of Pharmacy, Mediclinic Parkview Hospital, Mediclinic Middle East, Dubai P.O. Box 505004, ‎United Arab Emirates
Dr. Mais Damani: Department of Pharmacy, Mediclinic Parkview Hospital, Mediclinic Middle East, Dubai P.O. Box 505004, ‎United Arab Emirates

Journal of Innovations in Medical Research, 2025, vol. 4, issue 3, 1-20

Abstract: Artificial intelligence (AI) is increasingly recognized as a developing driver of innovation in clinical medicine, with reported advancements in diagnosis, treatment, and patient safety. Its capabilities may support new applications in care delivery and quality improvement, though the full extent of its impact remains under investigation. ‎This analysis aimed to explore recent advances in AI across clinical laboratory medicine, infectious disease management, and pharmacovigilance, drawing insights from a ‎peer-reviewed English literature published between 2019 and 2024. The study presented a descriptive literature review using the strengths, weaknesses, opportunities, and threats (SWOT) framework to examine recent AI developments in selected clinical domains, noting its emerging role and potential relevance in clinical settings. In clinical laboratories, AI has been associated with improved diagnostic accuracy and operational efficiency, while in infectious diseases, it has enabled ‎rapid pathogen identification and precision-guided treatments. In pharmacovigilance, AI has been explored for its potential to enhance predictive analytics and real-time monitoring, which may have contributed to reducing medication-related errors and adverse drug events. Despite these reported benefits, AI adoption raised critical concerns, including data privacy, algorithmic bias, and the ongoing need for human oversight. Addressing these challenges is essential to promote ethical, transparent, and appropriate AI integration into clinical medicine. By addressing these complexities, AI may unlock new opportunities in personalized medicine, safety, and care delivery, positioning it as a supportive tool in the evolving landscape of clinical practice.‎

Keywords: artificial intelligence; machine learning; clinical medicine; laboratory medicine; infectious diseases; pharmacovigilance (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:bdz:joimer:v:4:y:2025:i:3:p:1-20

DOI: 10.63593/JIMR.2788-7022.2025.06.001

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