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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. In any case, the ultimate goal is that consumers, even third parties, can successfully and immediately obtain the damage compensation.
參考文獻 一、中文參考書目

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(五)期刊論文
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(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. (2019), Dr. AI, Where Did You Get Your Degree, in Koch F. et al., eds., ARTIFICIAL INTELLIGENCE IN HEALTH. (Springer).

(三)期刊論文
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Zhou, Na et al., Concordance Study between IBM Watson for Oncology and Clinical Practice for Patients with Cancer in China, 24 ONCOLOGIST 812 (2019).

(四)歐盟相關機構研究報告
European Commission, (2020), White Paper on Artificial Intelligence: a European Approach to Excellence and Trust, https://ec.europa.eu/info/publications/white-paper-artificial-intelligence-european-approach-excellence-and-trust_en (last visited: 2021/11/10)
European Commission, (2021), Proposal for a Regulation of The European Parliament and of The Council Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts, https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52021PC0206 (last visited: 2021/11/25)
European Parliament, (2017), European Parliament Resolution of 16 February 2017 with Recommendations to The Commission on Civil Law Rules on Robotics (2015/2103(INL)), https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52017IP0051&from=EN (last visited: 2021/11/1)
European Parliament, Policy Department for Citizens` Rights and Constitutional Affairs, (2020), Artificial Intelligence and Civil Liability. https://www.europarl.europa.eu/RegData/etudes/STUD/2020/621926/IPOL_STU(2020)621926_EN.pdf (last visited: 2021/11/15)
European Parliament, (2020), European Parliament Resolution of 20 October 2020 with Recommendations to the Commission on a Civil Liability Regime for Artificial Intelligence (2020/2014(INL)), https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52020IP0276&qid=1639129746358 (last visited: 2021/12/11)
Expert Group on Liability and New Technologies - New Technology Formation, (2019), Report on Liability for Artificial Intelligence and Other Emerging Digital Technologies, https://op.europa.eu/en/publication-detail/-/publication/1c5e30be-1197-11ea-8c1f-01aa75ed71a1/language-en (last visited: 2021/11/3)
FRA, (2018), #BigData: Discrimination in Data-Supported Decision Making, https://fra.europa.eu/sites/default/files/fra_uploads/fra-2018-focus-big-data_en.pdf (last visited: 2021/07/10)
FRA, (2019), Data Quality and Artificial Intelligence-Mitigating Bias and Error to Protect Fundamental Rights, https://fra.europa.eu/sites/default/files/fra_uploads/fra-2019-data-quality-and-ai_en.pdf (last visited: 2021/07/10)
High-Level Expert Group on Artificial Intelligence, (2019), Ethics Guidelines for Trustworthy AI, https://www.aepd.es/sites/default/files/2019-12/ai-ethics-guidelines.pdf (last visited: 2021/11/3)

(五)美國法院判決
Canterbury v. Spence, 464 F.2d 772, 787 (D.C. Cir. 1972).
Sindell v. Abbott Laboratories, 26 Cal. 3d 588 (1980).

(六)網路資料
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American Medical Association, Augmented Intelligence in Health Care H-480.939, (2019), https://policysearch.ama-assn.org/policyfinder/detail/AI?uri=%2FAMADoc%2FHOD.xml-H-480.939.xml (last visited: 2021/12/10)
DiSanzo, Deborah, Watson Health is Committed to Using AI to Tackle Major Healthcare Challenges, IBM (Aug. 2, 2018), https://www.ibm.com/blogs/watson-health/ai-healthcare-challenges/ (last visited: 2021/3/12)
FDA, Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML) - Based Software as a Medical Device(SaMD)Discussion Paper and Request for Feedback, (Apr. 2, 2019), https://www.fda.gov/files/medical%20devices/published/US-FDA-Artificial-Intelligence-and-Machine-Learning-Discussion-Paper.pdf (last visited: 2021/12/13)
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描述 碩士
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-Enen_US
dc.contributor.author (Authors) 羅濟軒zh_TW
dc.contributor.author (Authors) Lo, Chi-Hsuanen_US
dc.creator (作者) 羅濟軒zh_TW
dc.creator (作者) Lo, Chi-Hsuanen_US
dc.date (日期) 2022en_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) G0107652020en_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) 107652020zh_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/#G0107652020en_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AIzh_TW
dc.subject (關鍵詞) Artificial Intelligenceen_US
dc.subject (關鍵詞) Medical Clinical Decision Support Systemen_US
dc.subject (關鍵詞) Tortious Liabilityen_US
dc.subject (關鍵詞) Medical Negligence Liabilityen_US
dc.subject (關鍵詞) Product Liabilityen_US
dc.subject (關鍵詞) Highly Autonomous Medical AIen_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 Systemsen_US
dc.type (資料類型) thesisen_US
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劉宏恩、吳采玟,美容醫學醫療行為是否具消費行為性質的法社會實證研究-兼論醫療法第 82 條新法與消費者保護法適用之關係,月旦醫事法報告,32期,頁7-30,2019年6月。
蔡宜臻,人工智慧醫療器材軟體上市監管之法律與歐美政策概述,科技法律透析,31卷,頁16,2019年10月。
劉明生,人工智慧醫療產品瑕疵事件舉證責任分配與舉證減輕之研究—以醫院、醫師與產品製造人為中心,月旦民商法雜誌,74期,頁32,2021年12月。
賴昭翰、吳佩蓉、張惠茹,專題報導:人工智慧對醫療產業的衝擊,科學發展期刊,563期,頁6-11,2019年11月。
謝哲勝,現代商品責任規範的檢討,台北大學法學論叢,87期,頁59-118,2013年9月。
簡美夷、黃鈺媖、洪國登、黃琴喨、吳明美、陳文雯,臺灣藥害救濟制度與瑞典、德國、日本及韓國之比較,載:月旦醫事法報告第46期,頁135-156,2020年8月。

(六)網路資料
司法院,最高法院108年度台上字第2035號請求損害賠償事件新聞稿,2020年8月19日,載於:https://www.judicial.gov.tw/tw/cp-1888-269402-dfe87-1.html (最後瀏覽日:2021年10月1日)
宏碁智醫(Acer Healthcare)對於VeriSee DR的產品介紹頁面提及:「No ophthalmologist required.」,載於:https://www.acer-healthcare.com/veriseedr(最後瀏覽日:2021年7月16日)
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(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. (2019), Dr. AI, Where Did You Get Your Degree, in Koch F. et al., eds., ARTIFICIAL INTELLIGENCE IN HEALTH. (Springer).

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(四)歐盟相關機構研究報告
European Commission, (2020), White Paper on Artificial Intelligence: a European Approach to Excellence and Trust, https://ec.europa.eu/info/publications/white-paper-artificial-intelligence-european-approach-excellence-and-trust_en (last visited: 2021/11/10)
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(五)美國法院判決
Canterbury v. Spence, 464 F.2d 772, 787 (D.C. Cir. 1972).
Sindell v. Abbott Laboratories, 26 Cal. 3d 588 (1980).

(六)網路資料
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dc.identifier.doi (DOI) 10.6814/NCCU202200350en_US