Bariatric Evaluation Through AI: a Survey of Expert Opinions Versus ChatGPT-4 (BETA-SEOV)

Jazi, Amir Hossein Davarpanah; Mahjoubi, Mohammad; Shahabi, Shahab; Alqahtani, Aayed R.; Haddad, Ashraf; Pazouki, Abdolreza; Prasad, Arun; Safadi, Bassem Y.; Chiappetta, Sonja; Taskin, Halit Eren; Billy, Helmuth Thorlakur; Kasama, Kazunori; Mahawar, Kamal; Gawdat, Khaled; Rheinwalt, Karl Peter; Miller, Karl A.; Kow, Lilian; Neto, Manoel Galvao; Yang, Wah; Palermo, Mariano; Ghanem, Omar M.; Lainas, Panagiotis; Peterli, Ralph; Kassir, Radwan; Puy, Ramon Vilallonga; Da Silva Ribeiro, Rui José; Verboonen, Sergio; Pintar, Tadeja; Shabbir, Asim; Musella, Mario; Kermansaravi, Mohammad

doi:10.1007/s11695-023-06903-w

Cited by 8 publications

(1 citation statement)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paradigm, AI-generated diagnoses or treatment plans are evaluated by a panel of medical experts, with the consensus among these experts on the validity of the AI’s recommendations serving as a measure of their reliability. This paradigm is particularly useful in assessing the AI’s performance in complex cases in which human expertise is invaluable, ranging from the psychiatric field in dealing with issues such as suicide risk assessment [ 34 ] to occupational medicine [ 35 ]; oncology, with the management of malignancies [ 36 ]; and complex surgical procedures such as bariatric surgery [ 37 ].…”

Section: Implementing “Verification Paradigms”: a Comprehensive Evalu...mentioning

confidence: 99%

Toward Clinical Generative AI: Conceptual Framework

Bragazzi,

Garbarino

2024

JMIR AI

View full text Add to dashboard Cite

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.

show abstract