Exploration of the impact mechanisms of generative artificial intelligence on doctor-patient shared decision-making
10.12026/j.issn.1001-8565.2024.09.11
- VernacularTitle:生成式人工智能对医患共享决策的影响机制探究
- Author:
Antian CHEN
1
;
Jun LU
2
;
Xinqing ZHANG
3
Author Information
1. Department of Cardiology， Fuwai Hospital， Chinese Academy of Medical Sciences & Peking Union Medical College/National Center for Cardiovascular Diseases， Beijing 100037， China
2. Department of General Surgery， Peking University Third Hospital， Beijing 100191， China
3. School of Marxism/School of Humanities and Social Sciences， Peking Union Medical College， Beijing 100730， China
- Publication Type:Journal Article
- Keywords:
generative artificial intelligence;
clinical application;
doctor-patient relationship;
shared decision-making;
ethical challenge
- From:
Chinese Medical Ethics
2024;37(9):1087-1092
- CountryChina
- Language:Chinese
-
Abstract:
As large-scale language models become increasingly mature， generative artificial intelligence （GenAI）， represented by ChatGPT/GPT-4， is anticipated to be deeply embedded in clinical decision-making. However， the clinical application of GenAI also has potential issues， such as the Moravec paradox. The Ethics and Governance of Artificial Intelligence for Health： Guidance on Large Multi-Modal Models released by the World Health Organization proposed six principles that should be followed when large models are applied in the medical field. The participation of GenAI in clinical decision-making requires the joint engagement of both doctors and patients. Clinical doctors are involved in the research and development， promotion， and application of GenAI， as well as in controlling the direction of technological development. GenAI empowers patients to participate in decision-making， aligning with actual medical scenario and meeting the value selection preferences of patients. Deepen GenAI’s explainability and responsibility allocation system， empower doctor-patient shared decision-making， properly handle the challenges brought by GenAI to traditional information and understanding， and achieve maximum clinical benefits.
