人工智慧 (Artificial intelligence, AI) 被認為在醫療照護與樂活健康領域之應用深具潛力，導因於AI系統對大量資訊數據的蒐集處理及運算的高效能，若能適切地應用在疾病預測分析、影像診斷、臨床決策輔助、個人精準醫學等等用途時，不但能夠有助於降低醫療人員的工作負荷，亦能夠提升預防醫學的實用價值。然而當AI被使用在這些醫療或健康範疇時，醫護等應用人員所該具備的相關知識、能力與態度，包括在應用過程中輸入數據的準確性與可靠性、輸出結果的判讀能力與解釋力、資訊的隱私維護等等，成為AI是否能適切地應用在醫療照護與健康促進的重要人為因子。本研究欲以問卷調查的方式，瞭解臨床醫師們對於人工智慧應用在醫療照護以及健康促進的準備度，並從認知、能力、態度、擔憂、期望不同面向進行分析。問卷填答對象為2021年9月到2022年5月在北部某醫學中心服務的實習醫學生、住院醫師、主治醫師，共回收232份有效問卷。問卷內容經過編碼之後，通過信度與效度分析驗證其為有效的研究工具。分析結果顯示，整體受訪醫師們各因素的平均分數為「能力」3.07分、「期望」4.13分、「擔憂」3.25分、「認知」3.46分、「態度」3.75分，以及「整體準備度」3.49分。針對受訪醫師的年齡、性別、身份別、專業科別及畢業學校加以分層分析，可以發現「整體準備度」分數主要會受到專業科別、身份別以及畢業學校影響而有顯著的不同；科別差異上以PGY一般科醫師以及影像醫學科醫師準備度較高，婦產部與麻醉部醫師較低；身份別上以PGY一般科醫師較高。年齡與性別的差異雖未影響整體準備度，但年齡的差異會影響「擔憂」分數，以20-30歲的醫師最高分；性別的差異則是在「擔憂」與「認知」兩個因素上有顯著的不同，其中男性「認知」分數顯著較高但是「擔憂」分數顯著較低。由本研究結果得知，臨床醫師自評對於人工智慧應用在醫療照護以及樂活健康的準備度普遍落在普通到同意之間。透過分層分析發現最年輕世代的PGY醫師對AI應用有顯著較高的解釋能力及擔憂程度於是具備較高的整體準備度，然而在「認知」的部份尚未有顯著提高。未來如何從醫學教育方面補強人工智慧在醫療與健康應用的相關知識與技能，將會是重要的課題。

Artificial intelligence (AI) is considered to have great potential in the field of medical care and health promotion, due to the high efficiency of AI systems in the collection, processing and calculation of large amounts of information data. If it can be properly applied in disease prediction analysis It can not only help reduce the workload of medical personnel, but also enhance the practical value of preventive medicine. However, when AI is used in these medical or health fields, the relevant knowledge, abilities and attitudes that medical and nursing personnel should have, including the accuracy and reliability of input data during the application process, and the ability to interpret and explain the output results. , information privacy maintenance, etc., have become an important human factor for whether AI can be properly applied in medical care and health promotion. The results of the analysis showed that the average scores of each factor among the interviewed physicians were 3.07 points for "ability", 4.13 points for "expectation", 3.25 points for "worry", 3.46 points for "cognition", 3.75 points for "attitude", and 3.49 points for "overall readiness". By stratified analyses among age, gender, identity, specialty, and graduation school, it can be found that the "overall readiness" score is significantly different by specialty, identity, and graduation school. In terms of specialty, PGY general physicians and the physicians in imaging medicine had higher readiness, while physicians in obstetrics and anesthesia departments were lower; PGY general physicians have higher readiness score than other identities. Although the difference in age and gender did not affect the overall readiness, the difference in age did affected the score of "worry", with the highest score for physicians aged 20-30. The gender difference is significantly different in the two factors of "worry" and "cognition", with males having significantly higher "cognition" scores but significantly lower "worry" scores. According to the results of this study, clinicians' self-assessed readiness for the application of artificial intelligence in medical care and health promotion generally falls between average and agree. It is found that the youngest generation, PGY physicians, have significantly higher explanatory ability and worry about AI application, thus having a higher overall readiness. However, there is no corresponding improvement in the "cognition" in these PGY physicians. From the perspective of medical education, it will be an important tsak to help strengthen the knowledge and skills of AI-related medical and health applications in the future.

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