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題名 論使用醫療人工智慧系統之侵權責任—以臨床決策輔助系統為中心
A Study on Tortious Liability for Using Medical Artificial Intelligence Systems—Focusing on the Clinical Decision Support Systems作者 羅濟軒
Lo, Chi-Hsuan貢獻者 劉宏恩
Liu, Hung-En
羅濟軒
Lo, Chi-Hsuan關鍵詞 人工智慧
醫療臨床決策輔助系統
侵權責任
醫療過失責任
商品責任
高自主醫療AI
Artificial Intelligence
Medical Clinical Decision Support System
Tortious Liability
Medical Negligence Liability
Product Liability
Highly Autonomous Medical AI日期 2022 上傳時間 1-Mar-2022 17:35:23 (UTC+8) 摘要 隨著應用於醫學影像判讀分析與提供治療方案之醫療臨床決策輔助系統興起，改變醫療機構、醫師與病患間的互動關係，體現於告知說明義務內容、醫療機構、醫師執行醫療業務之注意義務內容與標準之調整，及使用系統為病患診療之醫療過失與責任成立之認定。又，若系統出錯，系統製造商是否需負責，究竟醫療機構、醫師與系統製造商應如何分配責任？當未來出現高自主醫療AI，醫療機構、製造商又應如何分配責任？本研究旨在探討能否按我國民法、醫療法、消保法與醫療器材管理法規定向醫療機構、醫師與系統製造商分別主張醫療過失責任與商品責任？主要將整理與分析美國學者對於醫療過失要件之調整見解。另，將以歐盟與美國之商品責任法於適用AI之要件疑義，探討我國商品責任法制於適用醫療AI上可能衍生之相同爭議；又，輔以歐盟相關機構對於AI等新興技術出版之研究報告，勾勒出AI產品之管理與監管措施。同時，本文將以歐盟研究報告與美國文獻、自駕車相關立法例中提出之新興歸責理論進行論述。鑑於現階段臨床決策輔助系統居於輔助角色，醫師負有把關系統決策正確性與最終決策之責任。然而AI之資料依賴性、自主性、不透明性與不可預測性，需考量系統製造商相較醫療機構、醫師，較有能力與機會控制系統風險，尤其針對未來應用之高自主醫療AI，製造商自須負起主要之賠償責任，醫療機構仍須負起使用人責任。然而，未來醫療AI無可避免越趨複雜、人類越難掌握風險，需考量建立與加強包含醫療強制責任險、產品責任險、甚至是醫療AI救濟補償基金，並延伸討論是否需賦予醫療AI法人格之責任體系。無論如何，皆以消費者，甚至是第三人都能順利且快速地獲得損害填補為最終目的。
With the emergence of medical clinical decision support systems that are applied to medical image interpretation and analysis, and proffering treatment plans, the interactive relationship between medical institutions, physicians and patients has changed. It reflects in the adjustment to the content of informed consent obligation, the standard of care with medical institutions and physicians providing medical services, and determination of the establishment of medical negligence and liability when using systems to provide patient for diagnosis and treatment. In addition, if systems made mistakes, would the system manufacturer should take the responsibility for it? How could medical institutions, physicians and system manufacturers allocate responsibilities? When highly autonomous medical AI emerges in the future, how should medical institutions and manufacturers allocate responsibilities?The purpose of this study is to explore whether we can claim for medical negligence liability and product liability against medical institutions, physicians, and system manufacturers in accordance with Civil Code, Medical Care Act, Consumer Protection Act, and Medical Devices Act. We will sort out and analyze the opinions of American scholars on the adjustment of the legal elements of medical negligence. In addition, we will discuss our country product liability on the application of medical AI, which is based on the same disputes with the European Union and the United States product liability laws applying to the legal elements of AI. Furthermore, we will outline the management and supervision measures of AI products by reading and analyzing research reports published by the relevant European Union institutions on AI and other emerging technologies. Meanwhile, we will discuss the emerging theory of liability proposed in the European Union research reports, American literature, and the related legislation about self-driving vehicles.Since the clinical decision support systems are currently in an auxiliary role, the physicians are responsible for the accuracy of the system’s decision-making, and the final decision. However, the data dependence, autonomy, opacity, and unpredictability of the AI, it is considered that the system manufacturers have the better ability and opportunity to control the risks than the medical institutions and physicians. Especially for the highly autonomous medical AI used in the future, the system manufacturer must bear the main compensation responsibility, and medical institutions still have to assume the responsibility of the user. In the future, medical AI will inevitably become more complex and difficult for humans to control the risks. It is necessary to establish and strengthen medical compulsory liability insurance, product liability insurance, and even medical AI relief and compensation funds, and extend the discussion on whether the responsibility system should be given to the medical AI legal personality. 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精準對抗8大癌症，遠見雜誌，2017年8月30日，載於：https://www.gvm.com.tw/article/39828（最後瀏覽日：2021年4月20日）（七）我國法院判決最高法院109年度台上字第1425號民事判決最高法院109年度台上字第1425號民事判決最高法院107年度台上字第4587號刑事判決最高法院105年度台上字第89號民事判決最高法院98年度台上字第673號民事判決最高法院97年度台上字第741號民事判決最高法院94年度台上字第2676號刑事判決最高法院93年度台上字第2021號民事判決最高法院93年度台上字第989號民事判決臺灣高等法院106年度消上易字第8號判決臺灣高等法院105年度醫上字第22號判決台灣高等法院96年度醫上字第5號民事判決臺灣高等法院96年度消上字第3號判決臺灣高等法院96年度醫上字第11號民事判決台灣高等法院95年度醫上易字第1號民事判決臺灣高雄地方法院108年度醫字第2號民事判決二、日文參考書目松尾剛行「健康医療分野におけるAIの民刑事責任に関する検討-AI 画像診断(支援)システムを中心に」Law & Practice 13号（2019年）。厚生労働省，保健医療分野におけるＡＩ活用推進懇談会報告書，2017年6月27日，載於：https://www.mhlw.go.jp/file/05-Shingikai-10601000-Daijinkanboukouseikagakuka-Kouseikagakuka/0000169230.pdf (最後瀏覽日：2021年10月2日)。三、英文參考書目（一）專書Goodfellow, Ian, Bengio, Yoshua & Courville, Aaron (2016), DEEP LEARNING, Cambridge, Massachusetts: The MIT Press.Pasquale, Frank (2016), THE BLACK BOX SOCIETY: THE SECRET ALGORITHMS THAT CONTROL MONEY AND INFORMATION. Cambridge, Massachusetts; London, England: Harvard University Press.Wani, M. Arif et al. (2020), ADVANCES IN DEEP LEARNING. Singapore: Springer Singapore Pte. Limited.（二）專書論文Benhamou, Yaniv & Ferland, Justine (2020), Artificial Intelligence & Damages: Assessing Liability and Calculating the Damages, in Pina D’Agostino, Carole Piovesan & Aviv Gaon eds., LEADING LEGAL DISRUPTION: ARTIFICIAL INTELLIGENCE AND A TOOLKIT FOR LAWYERS AND THE LAW. (Thomson Reuters).Fonseca, Alcides & Cabral, Bruno (2019), Designing a Neural Network from Scratch for Big Data Powered by Multi-node GPUs, in Balas V. E., Roy S. S., Sharma D. & Samui P. eds., HANDBOOK OF DEEP LEARNING APPLICATIONS. (Springer).Price II, William N. (2018), Medical Malpractice and Black-Box Medicine, in I. Glenn Cohen et al., eds., BIG DATA, HEALTH LAW, AND BIOETHICS. (Cambridge University Press).Raff, Edward, Lantzy, Shannon & Maier, Ezekiel J. 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國立政治大學
法律科際整合研究所
107652020資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107652020 資料類型 thesis dc.contributor.advisor 劉宏恩 zh_TW dc.contributor.advisor Liu, Hung-En en_US dc.contributor.author (Authors) 羅濟軒 zh_TW dc.contributor.author (Authors) Lo, Chi-Hsuan en_US dc.creator (作者) 羅濟軒 zh_TW dc.creator (作者) Lo, Chi-Hsuan en_US dc.date (日期) 2022 en_US dc.date.accessioned 1-Mar-2022 17:35:23 (UTC+8) - dc.date.available 1-Mar-2022 17:35:23 (UTC+8) - dc.date.issued (上傳時間) 1-Mar-2022 17:35:23 (UTC+8) - dc.identifier (Other Identifiers) G0107652020 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/139233 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 法律科際整合研究所 zh_TW dc.description (描述) 107652020 zh_TW dc.description.abstract (摘要) 隨著應用於醫學影像判讀分析與提供治療方案之醫療臨床決策輔助系統興起，改變醫療機構、醫師與病患間的互動關係，體現於告知說明義務內容、醫療機構、醫師執行醫療業務之注意義務內容與標準之調整，及使用系統為病患診療之醫療過失與責任成立之認定。又，若系統出錯，系統製造商是否需負責，究竟醫療機構、醫師與系統製造商應如何分配責任？當未來出現高自主醫療AI，醫療機構、製造商又應如何分配責任？本研究旨在探討能否按我國民法、醫療法、消保法與醫療器材管理法規定向醫療機構、醫師與系統製造商分別主張醫療過失責任與商品責任？主要將整理與分析美國學者對於醫療過失要件之調整見解。另，將以歐盟與美國之商品責任法於適用AI之要件疑義，探討我國商品責任法制於適用醫療AI上可能衍生之相同爭議；又，輔以歐盟相關機構對於AI等新興技術出版之研究報告，勾勒出AI產品之管理與監管措施。同時，本文將以歐盟研究報告與美國文獻、自駕車相關立法例中提出之新興歸責理論進行論述。鑑於現階段臨床決策輔助系統居於輔助角色，醫師負有把關系統決策正確性與最終決策之責任。然而AI之資料依賴性、自主性、不透明性與不可預測性，需考量系統製造商相較醫療機構、醫師，較有能力與機會控制系統風險，尤其針對未來應用之高自主醫療AI，製造商自須負起主要之賠償責任，醫療機構仍須負起使用人責任。然而，未來醫療AI無可避免越趨複雜、人類越難掌握風險，需考量建立與加強包含醫療強制責任險、產品責任險、甚至是醫療AI救濟補償基金，並延伸討論是否需賦予醫療AI法人格之責任體系。無論如何，皆以消費者，甚至是第三人都能順利且快速地獲得損害填補為最終目的。 zh_TW dc.description.abstract (摘要) With the emergence of medical clinical decision support systems that are applied to medical image interpretation and analysis, and proffering treatment plans, the interactive relationship between medical institutions, physicians and patients has changed. It reflects in the adjustment to the content of informed consent obligation, the standard of care with medical institutions and physicians providing medical services, and determination of the establishment of medical negligence and liability when using systems to provide patient for diagnosis and treatment. In addition, if systems made mistakes, would the system manufacturer should take the responsibility for it? How could medical institutions, physicians and system manufacturers allocate responsibilities? When highly autonomous medical AI emerges in the future, how should medical institutions and manufacturers allocate responsibilities?The purpose of this study is to explore whether we can claim for medical negligence liability and product liability against medical institutions, physicians, and system manufacturers in accordance with Civil Code, Medical Care Act, Consumer Protection Act, and Medical Devices Act. We will sort out and analyze the opinions of American scholars on the adjustment of the legal elements of medical negligence. In addition, we will discuss our country product liability on the application of medical AI, which is based on the same disputes with the European Union and the United States product liability laws applying to the legal elements of AI. Furthermore, we will outline the management and supervision measures of AI products by reading and analyzing research reports published by the relevant European Union institutions on AI and other emerging technologies. Meanwhile, we will discuss the emerging theory of liability proposed in the European Union research reports, American literature, and the related legislation about self-driving vehicles.Since the clinical decision support systems are currently in an auxiliary role, the physicians are responsible for the accuracy of the system’s decision-making, and the final decision. However, the data dependence, autonomy, opacity, and unpredictability of the AI, it is considered that the system manufacturers have the better ability and opportunity to control the risks than the medical institutions and physicians. Especially for the highly autonomous medical AI used in the future, the system manufacturer must bear the main compensation responsibility, and medical institutions still have to assume the responsibility of the user. In the future, medical AI will inevitably become more complex and difficult for humans to control the risks. It is necessary to establish and strengthen medical compulsory liability insurance, product liability insurance, and even medical AI relief and compensation funds, and extend the discussion on whether the responsibility system should be given to the medical AI legal personality. In any case, the ultimate goal is that consumers, even third parties, can successfully and immediately obtain the damage compensation. en_US dc.description.tableofcontents 第一章 緒論 ..............................................1第一節 研究動機與問題意識..................................1第二節 研究範圍..........................................13第三節 研究方法..........................................18第一項 文獻分析法........................................18第二項 比較法學法........................................20第四節 研究架構..........................................21第二章 人工智慧與醫療AI概論 ..............................23第一節 人工智慧的發展歷程.................................23第二節 人工智慧之基本概念：機器學習與深度學習...............25第一項 人工智慧技術應用之現況.............................25第二項 機器學習與深度學習.................................26第三節 華生與VeriSee DR之運作原理與實務操作過程............27第一項 華生之運作原理及臨床操作...........................28第二項 VeriSee DR之運作原理及臨床操作.....................31第四節 機器學習、深度學習演算法涉及之爭議..................32第一項 人工智慧系統發生錯誤、有瑕疵之可能原因..............33第二項 人工智慧系統需持續更新健康數據資料..................37第三項 黑盒子演算法之基本概念及其衍生爭議..................38第一款 黑盒子演算法之基本概念.............................38第二款 黑盒子演算法之特性.................................39第五節 華生與VeriSee DR涉及黑盒子演算法爭議................41第六節 小結..............................................44第三章 醫療機構與醫師使用醫療AI之醫療責任 ................ 46前言.....................................................46第一節 我國一般侵權行為法之立法例..........................50第二節 探討VeriSee DR與華生對醫師違反告知說明義務之影響.....52第一項 我國規範告知說明義務之相關條文......................52第二項 使用VeriSee DR與華生違反告知說明義務之爭議..........54第三項 使用VeriSee DR與華生牽涉告知說明內容之爭議..........55第一款 使用VeriSee DR與華生之可能情境.....................55第二款 我國與美國法院就告知說明內容之見解..................57第三款 評析使用VeriSee DR與華生應告知說明內容.............58第四項 違反使用VeriSee DR與華生之告知說明義務責任..........66第三節 探討使用VeriSee DR與華生為診斷與治療之醫療責任......68第一項 探討VeriSee DR與華生對醫療注意義務之調整............68第一款 醫療機構與醫師新增之行為義務........................68第二款 我國與美國於醫療注意義務標準之演變..................72第三款 外國學者對醫療注意義務標準之適用見解................73第二項 探討我國與美國對使用VeriSee DR與華生之醫療責任......80第一款 美國醫療過失責任之成立要件及舉證責任................80第二款 我國醫療過失責任之成立要件及舉證責任................82第三款 醫師使用VeriSee DR與華生之臨床情境.................84第四款 我國與美國醫療過失責任適用於醫療機構與醫師使用VeriSee DR與華生之爭議..............................90第一目 構成醫療過失行為之爭議.............................95第二目 構成因果關係之認定爭議.............................96第三目 醫療訴訟舉證責任之適用爭議.........................96第四目 小結............................................101第四節 總結醫療機構與醫師使用VeriSee DR與華生之歸責情形...102第一項 醫療機構管理使用VeriSee DR與華生之醫療責任.........103第二項 醫師使用VeriSee DR與華生之醫療責任................104第三項 調整醫療機構與醫師使用醫療AI之責任體系.............106第五節 小結............................................110第四章 醫療AI製造商設計與製造醫療AI之責任體系.............115前言....................................................115第一節 歐盟針對人工智慧所涉及民事責任歸屬之官方研究文件.....116第一項 關於機器人民事法律規則的立法建議...................117第二項 可信賴之人工智慧倫理準則...........................118第三項 人工智慧與其他新興數位技術之責任研究報告............118第四項 人工智慧白皮書....................................120第五項 人工智慧及其民事責任研究報告.......................121第六項 人工智慧民事責任制度的建議.........................122第七項 人工智慧統一管理規則的立法草案.....................124第八項 小結.............................................127第二節 我國、歐盟與美國商品責任之立法例....................128第一項 歐盟產品責任指令之立法例...........................128第二項 美國商品責任法之立法例.............................131第三項 我國民法商品製造人與消保法商品責任之立法例...........136第一款 商品之範疇........................................137第二款 責任主體與責任性質.................................137第三款 請求權人之範疇....................................138第四款 商品欠缺與可合理期待安全性之涵義....................140第五款 舉證責任與競合適用.................................142第四項 我國、歐盟與美國商品責任法之比較....................144第三節 商品責任適用VeriSee DR與華生製造商之要件爭議........145第一項 VeriSee DR與華生製造商適用無過失責任之爭議..........146第二項 VeriSee DR與華生於商品之認定.......................147第三項 VeriSee DR與華生欠缺安全性之認定...................150第四項 VeriSee DR與華生於因果關係之認定...................154第五項 VeriSee DR與華生於舉證責任之認定...................156第六項 VeriSee DR與華生於科技發展抗辯之限制...............157第四節 VeriSee DR與華生製造商適用醫療器材管理法製造商責任...158第五節 小結.............................................160第五章 未來應用高自主醫療AI之侵權責任體系 .................163前言.....................................................163第一節 自駕車之分級制度與責任體系.........................164第二節 醫療機構於管理與使用醫療AI之推定過失責任............165第一項 醫療AI得否類比於「未成年人」.......................167第二項 醫療AI得否類比於「受僱人」.........................168第三項 醫療AI得否類比於「動物」...........................169第四項 小結.............................................170第三節 醫療AI製造商之無過失責任...........................172第一項 共同企業責任理論..................................172第二項 市場占有率責任理論................................174第三項 建構醫療AI安全性標準與製造商責任之減免..............177第四節 將醫療AI比擬為醫學生與醫師，賦予法人格地位..........181第一項 賦予法人格之目的..................................182第二項 醫療AI之醫療侵權責任..............................184第一款 醫療AI故意與過失要件之認定.........................184第二款 醫療AI之責任財產..................................185第五節 建立強制責任保險與補償基金制度......................186第一項 建立無過失補償制度.................................186第二項 無過失補償制度介紹.................................186第三項 醫療AI救濟金補償制度...............................187第四項 醫療責任保險......................................191第五項 醫療AI之商品保險..................................193第六節 小結.............................................194第六章 結論與建議.......................................197參考文獻 ...............................................206 zh_TW dc.format.extent 5285993 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107652020 en_US dc.subject (關鍵詞) 人工智慧 zh_TW dc.subject (關鍵詞) 醫療臨床決策輔助系統 zh_TW dc.subject (關鍵詞) 侵權責任 zh_TW dc.subject (關鍵詞) 醫療過失責任 zh_TW dc.subject (關鍵詞) 商品責任 zh_TW dc.subject (關鍵詞) 高自主醫療AI zh_TW dc.subject (關鍵詞) Artificial Intelligence en_US dc.subject (關鍵詞) Medical Clinical Decision Support System en_US dc.subject (關鍵詞) Tortious Liability en_US dc.subject (關鍵詞) Medical Negligence Liability en_US dc.subject (關鍵詞) Product Liability en_US dc.subject (關鍵詞) Highly Autonomous Medical AI en_US dc.title (題名) 論使用醫療人工智慧系統之侵權責任—以臨床決策輔助系統為中心 zh_TW dc.title (題名) A Study on Tortious Liability for Using Medical Artificial Intelligence Systems—Focusing on the Clinical Decision Support Systems en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 一、中文參考書目（一）政府資料行政院消費者保護委員會消保法字第0980005526號函，2009年6月29日。立法院公報處，立法院公報，107卷9期院會紀錄，2017年12月。衛生福利部國民健康署，107年健康促進統計年報，2021年3月17日。衛生福利部統計處，109年死因統計結果分析，2021年6月18日。衛生福利部食品藥物管理署FDA器字第1101608354號公告，2021年8月16日。行政院新聞傳播處，重要政策：台灣的「AI小國大戰略」，2018年10月16日，載於：https://www.ey.gov.tw/Page/5A8A0CB5B41DA11E/50a08776-e33a-4be2-a07c-a6e523f5031b（最後瀏覽日：2021年3月1日）行政院科技部，智慧醫療人機協作新時代，臨床輔助診斷精準且快速，2020年8月20日，載於：https://www.most.gov.tw/folksonomy/detail/6929ce33-9a89-44a2-8ccb-edfb7f2ef5d2?l=CH（最後瀏覽日：2021年7月11日）衛生福利部中央健康保險署全民健康保險醫療品質資訊公開網，認識糖尿病，載於：http://health.ntu.edu.tw/html.php?categoryid=8&id=23（最後瀏覽日：2021年7月12日）衛生福利部國民健康署，健康監測與統計/統計結果報告/健康促進統計年報/歷年報告，載於：https://dep.mohw.gov.tw/DOS/lp-5069-113.html（最後瀏覽日：2021年7月12日）（二）專書Eric Topol著，黃鈺閔、王心薇、涂瑋瑛、李偉誠譯，AI醫療DEEP MEDICINE，旗標，2020年11月。Klaus 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日，載於：https://www.commonhealth.com.tw/article/66916?rec=ch-i2i-2-visitor&from_area=area06&from_id=expert-940&from_index=1（最後瀏覽日：2021年7月12日）林怡廷，每年消化5萬篇新研究，「華生」幫人類打贏抗癌戰，天下雜誌637期，2017年12月4日，載於：https://www.cw.com.tw/article/5086589（最後瀏覽日：2021年7月14日）翁書婷，癌症病患新選擇：台北醫學大學導入IBM華生人工智慧治療輔助系統，2017年7月14日，載於：https://www.bnext.com.tw/article/45382/watson-for-oncology-ibm-cancer（最後瀏覽日：2021年4月19日)衛生福利部食品藥物管理署針對西藥、醫療器材、化妝品許可證查詢網頁搜尋，關於VeriSee DR系統之查詢結果，衛部醫器製字第006966號，載於： https://info.fda.gov.tw/MLMS/H0001D.aspx?Type=Lic&LicId=55006966（最後瀏覽日：2021年6月21日)衛生福利部食品藥物管理署關於西藥、醫療器材、化妝品許可證查詢網頁搜尋VeriSee DR後，再點選「仿單」連結，即可下載檔案。載於：https://info.fda.gov.tw/MLMS/H0001D3.aspx?LicId=55006966（最後瀏覽日：2021年7月16日)蕭玉品，北醫聯手IBM華生 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