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Infusing knowledge- and fact-based information through prompt engineering and calibration may alleviate the issue. Although deidentified medical records were used in this work, generative language model may overfit the medical domain knowledge and diagnosis from the training corpus, and could produce biased outputs. For instance, diagnostic summary and medication recommendation can be conditioned on population geography and time, therefore the medical model can draw incorrect conclusions for the patients. Moreover, misuse of generative models in the medical field will lead to a large number of misdiagnoses and the dissemination of incorrect medical knowledge. Controlled and accountable AI for medical field may require substantial external modules and engineering (e.g. fact\/coherence\/knowledge real-time checking models), cooperating with human healthcare professionals. Furthermore, potential shared liability between AI systems, service providers, and AI-assisted doctors remains a debatable issue.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"54"}}