Risks Associated with AI in Healthcare and Life Sciences. Artificial Intelligence in Healthcare and Life Sciences: An Update on the Multifaceted Applications, Risks, and Tremendous Opportunities

Abstract

Introduction:: Artificial intelligence (AI) has emerged as a transformative force in healthcare and the life sciences. From diagnostics to therapy, AI applications are reshaping medical imaging, drug discovery, clinical workflows, and public health administration. While offering significant opportunities, AI adoption also raises concerns regarding ethics, data security, and real-world implementation. Methods:: A structured literature search was conducted across PubMed, Scopus, Web of Science, and Google Scholar up to August 2024 using combinations of the terms “artificial intelligence,” “machine learning,” “deep learning,” “healthcare,” “oncology,” “antimicrobial resistance,” and “clinical trials.” Of the 1,126 retrieved records, 814 remained after duplicates were removed. Following title and abstract screening, 277 full texts were reviewed, and 185 studies were included in the narrative synthesis. Citation tracking of key studies supplemented the review. Results:: The review demonstrates that artificial intelligence significantly improves diagnostic accuracy, predictive risk assessment, drug discovery efficiency, and personalized treatment planning across multiple medical domains. However, the analysis also reveals critical challenges, including algorithmic bias, data privacy vulnerabilities, lack of transparency in deep learning models, and regulatory uncertainty. The findings highlight that while AI offers transformative clinical and research benefits, safe implementation requires standardized validation frameworks, ethical governance mechanisms, and human-in-the-loop decision supervision to ensure reliability and patient safety. Discussion:: AI-powered imaging analysis improves accuracy, speed, and early detection. Predictive algorithms accelerate compound identification and reduce trial costs. AI supports personalized therapy, antimicrobial resistance detection, and prognostic modeling. Decision support systems and monitoring tools enhance patient care and administrative efficiency. Despite these advances, limitations persist, including poor generalizability, algorithmic bias, high infrastructure costs, and regulatory gaps. Several AI models, such as dermatology tools, underperform on darker skin, highlighting the risk of inequitable outcomes. Conclusion:: AI offers significant opportunities to transform healthcare, but its adoption requires addressing ethical, regulatory, and implementation challenges. Multicenter validation, explainable models, and robust governance frameworks are essential to ensure safe, equitable, and effective integration into clinical practice.

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