JMIR Publications

ABSTRACT

Clinical decision-making is a crucial aspect of healthcare, involving the balanced integration of scientific evidence, clinical judgment, ethical considerations, and patient involvement. This process is dynamic and multifaceted, relying on clinicians' knowledge, experience, and intuitive understanding to achieve optimal patient outcomes through informed, evidence-based choices. The advent of generative Artificial Intelligence (AI) presents a revolutionary opportunity in clinical decision-making. AI's advanced data analysis and pattern recognition capabilities can significantly enhance the diagnosis and treatment of diseases, processing vast medical data to identify patterns, tailor treatments, predict disease progression, and aid in proactive patient management. However, the incorporation of AI into clinical decision-making raises concerns regarding the reliability and accuracy of AI-generated insights. To address these concerns, eleven “verification paradigms” are here proposed, with each paradigm offering unique methods to verify the evidence-based nature of AI in clinical decision-making. The paper also frames the concept of “clinically explainable, fair, and responsible, clinician-, expert-, and patient-in-the-loop AI”. This model focuses on ensuring AI's comprehensibility, collaborative nature, and ethical grounding, advocating for AI to serve as an augmentative tool, with its decision-making processes being transparent and understandable to clinicians and patients. The integration of AI should enhance, not replace, the clinician’s judgment and should involve continuous learning and adaptation based on real-world outcomes and ethical and legal compliance. In conclusion, while generative AI holds immense promise in enhancing clinical decision-making, it is essential to ensure that it produces evidence-based, reliable, and impactful knowledge. Employing the outlined paradigms and approaches can help the medical and patient communities harness AI's potential while maintaining high patient care standards.