Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/139233
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
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?\n\n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。\n\n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?\n\nThe 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.\n\nSince 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.
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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)\n\n(七)我國法院判決\n最高法院109年度台上字第1425號民事判決\n最高法院109年度台上字第1425號民事判決\n最高法院107年度台上字第4587號刑事判決\n最高法院105年度台上字第89號民事判決\n最高法院98年度台上字第673號民事判決\n最高法院97年度台上字第741號民事判決\n最高法院94年度台上字第2676號刑事判決\n最高法院93年度台上字第2021號民事判決\n最高法院93年度台上字第989號民事判決\n臺灣高等法院106年度消上易字第8號判決\n臺灣高等法院105年度醫上字第22號判決\n台灣高等法院96年度醫上字第5號民事判決\n臺灣高等法院96年度消上字第3號判決\n臺灣高等法院96年度醫上字第11號民事判決\n台灣高等法院95年度醫上易字第1號民事判決\n臺灣高雄地方法院108年度醫字第2號民事判決\n\n二、日文參考書目\n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)。\n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)。\n\n三、英文參考書目\n\n(一)專書\nGoodfellow, Ian, Bengio, Yoshua & Courville, Aaron (2016), DEEP LEARNING, Cambridge, Massachusetts: The MIT Press.\nPasquale, Frank (2016), THE BLACK BOX SOCIETY: THE SECRET ALGORITHMS THAT CONTROL MONEY AND INFORMATION. Cambridge, Massachusetts; London, England: Harvard University Press.\nWani, M. Arif et al. (2020), ADVANCES IN DEEP LEARNING. Singapore: Springer Singapore Pte. Limited.\n\n(二)專書論文\nBenhamou, 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).\nFonseca, 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).\nPrice 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).\nRaff, 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).\n\n(三)期刊論文\nAbraham, Kenneth S. & Rabin, Robert L., Automated Vehicles and Manufacturer Responsibility for Accidents: A New Legal Regime For a New Era, 105 VIRGINIA LAW REVIEW 127 (2019).\nAbràmoff, M.D. et al., Pivotal Trial of an Autonomous AI-Based Diagnostic System for Detection of Diabetic Retinopathy in Primary Care Offices, 39 NPJ DIGITAL MED 1 (2018).\nAllain, Jessica S., From Jeopardy! To Jaundice: The Medical Liability Implications of Dr. Watson and Other Artificial Intelligence Systems, 73 LOUISIANA LAW REVIEW 1049 (2013).\nAstromskė, Kristina, Peičius, Eimantas & Astromskis, Paulius, Ethical and Legal Challenges of Informed Consent Applying Artificial Intelligence in Medical Diagnostic Consultations, 36 AI & SOCIETY 509 (2021).\nBal, B. Sonny, An Introduction to Medical Malpractice in the United States, 467 CLINICAL ORTHOPAEDICS AND RELATED RESEARCH 339 (2009).\nBambauer, Jane R., Dr. Robot, 51 UC DAVIS LAW REVIEW 383 (2017).\nBarfield, Woodrow, Liability for Autonomous and Artificially Intelligent Robots, 9 PALADYN, JOURNAL OF BEHAVIORAL ROBOTICS 193 (2018).\nBathaee, Yavar, The Artificial Intelligence Black Box and the Failure of Intent and Causation, 31 HARVARD JOURNAL OF LAW & TECHNOLOGY 889 (2018).\nCarr, Nanci K., As the Role of the Driver Changes with Autonomous Vehicle Technology, so, Too, Must the Law Change, 51 ST. MARY`S LAW JOURNAL 817 (2020).\nCheong, M. C. S., Artificial Intelligence in Healthcare, 30 JOURNAL OF BIOMEDICAL & LABORATORY SCIENCES 33 (2018).\nChoi, Youn I. et al., Concordance Rate between Clinicians and Watson for Oncology among Patients with Advanced Gastric Cancer: Early, Real-World Experience in Korea, CANADIAN JOURNAL OF GASTROENTEROLOGY AND HEPATOLOGY 1 (2019).\nChung, Jason & Zink, Amanda, Hey Watson – Can I Sue You for Malpractice? Examining the Liability of Artificial Intelligence in Medicine, 11 ASIA PACIFIC JOURNAL OF HEALTH LAW & ETHICS 51 (2017).\nChua, Isaac S. et al., Artificial Intelligence in Oncology: Path to Implementation, 10 CANCER MEDICINE. 4138 (2021).\nCohen, I. Glenn, Informed Consent and Medical Artificial Intelligence: What to Tell the Patient?, 108 GEORGETOWN LAW JOURNAL 1425 (2020), Harvard Public Law Working Paper No. 20-03.\nDoyle-Lindrud, Susan, Watson will See You Now: A Supercomputer to Help Clinicians Make Informed Treatment Decisions, 19 CLINICAL JOURNAL OF ONCOLOGY NURSING 31 (2015).\nDuffy, Sophia H. & Hopkins, Jamie P., Sit, Stay, Drive: The Future of Autonomous Car Liability, 16 SMU SCIENCE & TECHNOLOGY LAW REVIEW 454 (2013).\nEsteva, Andre et al., Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks, 542 NATURE 115 (2017).\nFeldman, Robin, Aldana, Ehrik & Stein, Kara, Artificial Intelligence in the Health care Space: How We Can Trust What We Cannot Know, 30 STANFORD LAW & POLICY REVIEW 399 (2019).\nFinlayson, Samuel G. et al., The Clinician and Dataset Shift in Artificial Intelligence, 385 THE NEW ENGLAND JOURNAL OF MEDICINE 283 (2021).\nFroomkin, A. Michael, Ian, Kerr & Joelle, Pineau, When AIs Outperform Doctors: Confronting the Challenges of a Tort-Induced Over-Reliance on Machine Learning, 61 ARIZONA LAW REVIEW 33 (2019).\nGiuffrida, Iria & Treece, Taylor, Keeping AI Under Observation: Anticipated Impacts on Physicians` Standard of Care, 22 TULANE JOURNAL OF TECHNOLOGY & INTELLECTUAL PROPERTY 111 (2020).\nGulshan, Varun et al., Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs, 316 JAMA 2402 (2016).\nHacker, Philipp et al., Explainable AI under Contract and Tort Law: Legal Incentives and Technical Challenges, 28 ARTIFICIAL INTELLIGENCE AND LAW 415 (2020).\nHaenlein, Michael & Kaplan, Andreas, A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence, 61 CALIFORNIA MANAGEMENT REVIEW 5 (2019).\nHarned, Zach, Lungren, Matthew P. & Rajpurkar, Pranav, Machine Vision, Medical AI, and Malpractice, HARVARD JOURNAL OF LAW & TECHNOLOGY DIGEST 1 (2019).\nHaupt, Claudia E., Artificial Professional Advice, 18 YALE JOURNAL OF HEALTH POLICY, LAW & ETHICS 55 (2019).\nHoffman, Kelly M. et al., Racial Bias in Pain Assessment and Treatment Recommendations, and False Beliefs about Biological Differences between Blacks and Whites, 113 PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 4296 (2016).\nHsieh, Yi-Ting et al., Application of Deep Learning Image Assessment Software VeriSee™ for Diabetic Retinopathy Screening, 120 JOURNAL OF THE FORMOSAN MEDICAL ASSOCIATION 165 (2021).\nJackson, Brandon W., Artificial Intelligence and the Fog of Innovation: A Deep-Dive on Governance and The Liability of Autonomous Systems, 35 SANTA CLARA HIGH TECHNOLOGY LAW JOURNAL 35 (2019).\nJetten, Lieke & Sharon, Stephen, Selected Issues Concerning the Ethical Use of Big Data Health Analytics, 72 WASHINGTON AND LEE LAW REVIEW ONLINE 489 (2016).\nJiang Fei et al., Artificial Intelligence in Healthcare: Past, Present and Future, 2 STROKE AND VASCULAR NEUROLOGY 230 (2017).\nKaplan, Andreas & Haenlein, Michael, Siri, Siri, in My Hand: Who’s the Fairest in the Land? On the Interpretations, Illustrations, and Implications of Artificial Intelligence, 62 BUSINESS HORIZONS 15 (2019).\nKatyal, Sonia K., Private Accountability in the Age of Artificial Intelligence, 66 UCLA LAW REVIEW 54 (2019).\nKerr, Ian & Gruben, Vanessa, AIs as Substitute Decision-Makers, 21 YALE JOURNAL OF LAW AND TECHNOLOGY 78 (2019).\nKiener, Maximilian, Artificial Intelligence in Medicine and the Disclosure of Risks, 36 AI & SOCIETY 705 (2021).\nKrause, Jonathan et al., Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy, 125 AMERICAN ACADEMY OF OPHTHALMOLOGY 1264 (2018).\nLai, Alicia, Artificial Intelligence, LLC: Corporate Personhood as Tort Reform, 2021 MICHIGAN STATE LAW REVIEW 1 (2021).\nLupton, Michael, Some Ethical and Legal Consequences of the Application of Artificial Intelligence in the Field of Medicine, 18 TRENDS IN MEDICINE 1 (2018).\nLysaght, Tamra et al., AI-Assisted Decision-Making in Healthcare, 11 ASIAN BIOETHICS REVIEW 299 (2019).\nMarchant, Gary E. & Tournas, Lucille M., AI Health Care Liability: From Research Trials to Court Trials, 12 JOURNAL OF HEALTH & LIFE SCIENCES LAW 23 (2019).\nMatsuzaki, Tokio, Ethical Issues of Artificial Intelligence in Medicine, 55 CALIFORNIA WESTERN LAW REVIEW 255 (2018).\nMcNamara, Donna et al., Differential Impact of Cognitive Computing Augmented by Real World Evidence on Novice and Expert Oncologists, 8 CANCER MEDICINE 6578 (2019).\nNersessian, David & Ruben, Mancha, From Automation to Autonomy: Legal and Ethical Responsibility Gaps in Artificial Intelligence Innovation, 27 MICHIGAN TECHNOLOGY LAW REVIEW 55 (2020).\nObermeyer, Ziad & Emanuel, Ezekiel J., Predicting the Future–Big Data, Machine Learning, and Clinical Medicine, 375 THE NEW ENGLAND JOURNAL OF MEDICINE 1216 (2016).\nOwen, David G., Manufacturing Defects, 53 SOUTH CAROLINA LAW REVIEW 851 (2002).\nOwen, David G., Design Defects, 73 MISSOURI LAW REVIEW 291 (2008).\nParikh, Ravi B., Teeple, Stephanie & Navathe, Amol S., Addressing Bias in Artificial Intelligence in Health Care, 322 JAMA 2377 (2019).\nPeters, Philip G. Jr., The Role of Jury in Modern Malpractice Law, 87 IOWA LAW REVIEW 909 (2002).\nPowell, Dalton, Autonomous Systems as Legal Agents: Directly by the Recognition of Personhood or Indirectly by the Alchemy of Algorithmic Entities, 18 DUKE LAW AND TECHNOLOGY REVIEW 307 (2020).\nPrice II, William N., Black-Box Medicine, 28 HARVARD JOURNAL OF LAW & TECHNOLOGY 419 (2015).\nPrice II, William N., Artificial Intelligence in Health Care: Applications and Legal Implications, 14 SCITECH LAWYER 10 (2017).\nPrice II, William N., Artificial Intelligence in the Medical System: Four Roles for Potential Transformation, 21 YALE JOURNAL OF LAW AND TECHNOLOGY 122 (2019).\nPrice II, William N., Gerke, Sara & Cohen, I. G., Potential Liability for Physicians Using Artificial Intelligence, 322 JAMA 1765 (2019).\nScherer, Matthew U., Regulating Artificial Intelligence Systems: Risks, Challenges, Competencies, and Strategies, 29 HARVARD JOURNAL OF LAW & TECHNOLOGY 353 (2016).\nSchiff, Daniel & Borenstein, Jason, How Should Clinicians Communicate With Patients About the Roles of Artificially Intelligent Team Members?, 21 AMA JOURNAL OF ETHICS E138 (2019).\nSchönberger, Daniel, Artificial Intelligence in Healthcare: A Critical Analysis of the Legal and Ethical Implications, 27 INTERNATIONAL JOURNAL OF LAW AND INFORMATION TECHNOLOGY 171 (2019).\nSelbst, Andrew D., Negligence and AI`s Human Users, 100 BOSTON UNIVERSITY LAW REVIEW 1315 (2020).\nSmith, Helen & Fotheringham, Kit, Artificial Intelligence in Clinical Decision-Making: Rethinking Liability, 20 MEDICAL LAW INTERNATIONAL 131 (2020).\nSomashekhar, Sampige P. et al., Watson for Oncology and Breast Cancer Treatment Recommendations: Agreement with an Expert Multidisciplinary Tumor Board, 29 ANNALS OF ONCOLOGY 418 (2018).\nStrickland, E., IBM Watson, Heal Thyself: How IBM Overpromised and Underdelivered on AI Health Care, 56 IEEE SPECTRUM 24 (2019).\nSullivan, Hannah R. & Schweikart, Scott J., Are Current Tort Liability Doctrines Adequate for Addressing Injury Caused by AI?, 21 AMA JOURNAL OF ETHICS 160 (2019).\nSutton, Reed T. et al., An Overview of Clinical Decision Support Systems: Benefits, Risks, and Strategies for Success, 3 NPJ DIGITAL MEDICINE 1 (2020).\nSwanson, Amanda & Khan, Fazal, The Legal Challenge of Incorporating Artificial Intelligence into Medical Practice, 6 JOURNAL OF HEALTH & LIFE SCIENCES LAW 90 (2012).\nSword, Megan, To Err is Both Human and Non-Human, 88 UMKC LAW REVIEW 211 (2019).\nThomas, Justin, Putting Programmers in the Driver’s Seat: State Tort Systems Applied to Autonomous Automobiles, 93 UNIVERSITY OF DETROIT MERCY LAW REVIEW 553 (2016).\nTobia, Kevin, Nielsen, Aileen & Stremitzer, Alexander, When Does Physician Use of AI Increase Liability?, 62 JOURNAL OF NUCLEAR MEDICINE 17 (2021).\nTupasela, Aaro & Di Nucci, Ezio, Concordance as Evidence in the Watson for Oncology Decision Support System, 35 AI & SOCIETY 811 (2020).\nTuring, Alan M., Computing Machinery and Intelligence, 59 MIND 433 (1950).\nvan Hartskamp, Michael et al., Artificial Intelligence in Clinical Health Care Applications: Viewpoint, 8 INTERACTIVE JOURNAL OF MEDICAL RESEARCH e12100 (2019).\nVladeck, David C., Machines Without Principals: Liability Rules and Artificial Intelligence, 89 WASHINGTON LAW REVIEW 117 (2014).\nWalz, Axel & Firth-Butterfield, Kay, Implementing Ethics Into Artificial Intelligence: A Contribution, From A Legal Perspective, To The Development of An AI Governance Regime, 18 DUKE LAW & TECHNOLOGY REVIEW 176 (2019).\nYu, Ronald & Alì, Gabriele S., What`s Inside the Black Box? AI Challenges for Lawyers and Researchers, 19 LEGAL INFORMATION MANAGEMENT 2 (2019).\nYu, Peter K., Beyond Transparency and Accountability: Three Additional Features Algorithm Designers Should Build into Intelligent Platforms, 13 NORTHEASTERN UNIVERSITY LAW REVIEW 263 (2020).\nZhou, Na et al., Concordance Study between IBM Watson for Oncology and Clinical Practice for Patients with Cancer in China, 24 ONCOLOGIST 812 (2019).\n\n(四)歐盟相關機構研究報告\nEuropean 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)\nEuropean 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)\nEuropean 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)\nEuropean 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)\nEuropean 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)\nExpert 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)\nFRA, (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)\nFRA, (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)\nHigh-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)\n\n(五)美國法院判決\nCanterbury v. Spence, 464 F.2d 772, 787 (D.C. Cir. 1972).\nSindell v. Abbott Laboratories, 26 Cal. 3d 588 (1980).\n\n(六)網路資料\nAmerican Medical Association, Augmented Intelligence in Health Care H-480.940, (2018), https://policysearch.ama-assn.org/policyfinder/detail/AI?uri=%2FAMADoc%2FHOD.xml-H-480.940.xml (last visited: 2021/12/10)\nAmerican 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)\nDiSanzo, 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)\nFDA, 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)\nHernandez, Daniela & Greenwald, Ted, IBM Has a Watson Dilemma, THE WALL STREET JOURNAL (Aug. 11, 2018), https://www.wsj.com/articles/ibm-bet-billions-that-watson-could-improve-cancer-treatment-it-hasnt-worked-1533961147 (last visited: 2021/2/23)\nHosanagar, Kartik & Jair, Vivian, We Need Transparency in Algorithms, But Too Much Can Backfire, HARVARD BUSINESS REVIEW (Jul. 25, 2018), https://hbr.org/2018/07/we-need-transparency-in-algorithms-but-too-much-can-backfire (last visited: 2021/7/18)\nMichigan Compiled Laws Annotated § 257.35a (b), https://1-next-westlaw-com.autorpa.lib.nccu.edu.tw/Document/N11FE401178F611E38559C9F6C8FB618C/View/FullText.html?originationContext=documenttoc&transitionType=CategoryPageItem&contextData=(sc.Default) (last visited: 2021/12/13)\nNational CONFERENCE OF STATE LEGISLATURES (NCSL), Autonomous Vehicles | Self-Driving Vehicles Enacted Legislation, (Feb. 18, 2020), https://www.ncsl.org/research/transportation/autonomous-vehicles-self-driving-vehicles-enacted-legislation.aspx (last visited: 2021/12/13)\nRoss, Casey & Swetlitz, Ike, IBM Pitched Its Watson Supercomputer as a Revolution in Cancer Care. It’s Nowhere Close, STAT (Sep. 5, 2017), https://www.statnews.com/2017/09/05/watson-ibm-cancer/ (last visited: 2021/2/22)\nRoss, Casey & Swetlitz, Ike, IBM’s Watson Supercomputer Recommended “Unsafe and Incorrect” Cancer Treatments, Internal Documents Show, STAT (Jul. 25, 2018), www.statnews.com/wp-content/uploads/2018/09/IBMs-Watson-recommended-unsafe-and-incorrect-cancer-treatments-STAT.pdf (last visited: 2021/2/22)\nSAE INTERNATIONAL, SAE International Releases Updated Visual Chart for Its “Levels of Driving Automation” Standard for Self-Driving Vehicles, (Dec. 11, 2018), https://www.sae.org/news/press-room/2018/12/sae-international-releases-updated-visual-chart-for-its-%E2%80%9Clevels-of-driving-automation%E2%80%9D-standard-for-self-driving-vehicles (last visited: 2021/12/13)\nTownson, Sian, AI Can Make Bank Loans More Fair, HARVARD BUSINESS REVIEW (Nov. 6, 2020), https://hbr.org/2020/11/ai-can-make-bank-loans-more-fair (last visited: 2021/8/12)
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
Appears in Collections:學位論文

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