The ethics of AI in health care: A mapping review.
暂无分享,去创建一个
L. Floridi | M. Taddeo | J. Morley | Josh Cowls | C. Machado | C. Burr | Indra Joshi
[1] T. Beauchamp,et al. Principles of biomedical ethics , 1991 .
[2] Johannes Gehrke,et al. Data Mining with Decision Trees , 2000, ICDE.
[3] D Haussler,et al. Knowledge-based analysis of microarray gene expression data by using support vector machines. , 2000, Proceedings of the National Academy of Sciences of the United States of America.
[4] Martinez L. Global infectious disease surveillance. , 2000, International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases.
[5] R. Chang,et al. Data mining with decision trees for diagnosis of breast tumor in medical ultrasonic images , 2001, Breast Cancer Research and Treatment.
[6] R. Andorno,et al. The right not to know: an autonomy based approach , 2004, Journal of Medical Ethics.
[7] C. Sugnet,et al. Knowledge-based Analysis of Mi roarray Gene Expression Data , 2007 .
[8] Luciano Floridi,et al. The Method of Levels of Abstraction , 2008, Minds and Machines.
[9] Matteo Turilli,et al. The ethics of information transparency , 2009, Ethics and Information Technology.
[10] Maria J Grant,et al. A typology of reviews: an analysis of 14 review types and associated methodologies. , 2009, Health information and libraries journal.
[11] Andrew P. Bradley,et al. Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus , 2010, IEEE Transactions on Information Technology in Biomedicine.
[12] C. Goss,et al. Monitoring Influenza Activity in the United States: A Comparison of Traditional Surveillance Systems with Google Flu Trends , 2011, PloS one.
[13] T. Burns. Our Necessary Shadow: The Nature and Meaning of Psychiatry , 2013 .
[14] Nayha Sethi,et al. Delivering proportionate governance in the era of eHealth , 2013, Medical law international.
[15] Dylan B. George,et al. Big Data Opportunities for Global Infectious Disease Surveillance , 2013, PLoS medicine.
[16] M. Danis,et al. Providers, payers, the community, and patients are all obliged to get patient activation and engagement ethically right. , 2013, Health affairs.
[17] Luciano Floridi,et al. Distributed Morality in an Information Society , 2012, Science and Engineering Ethics.
[18] Steven N Goodman,et al. An ethics framework for a learning health care system: a departure from traditional research ethics and clinical ethics. , 2013, The Hastings Center report.
[19] Natalia Romero Herrera,et al. Reflective Healthcare Systems: micro-Cycle of Self-Reflection to empower users , 2014, IxD&A.
[20] K. Foot,et al. Media Technologies: Essays on Communication, Materiality, and Society , 2014 .
[21] Barry R. Masters,et al. Principles of Biomedical Ethics, Seventh Edition Eds: Tom L. Beauchamp and James F. Childress Oxford University Press, 2013. XVI, 459 Pages, US$66.95, ISBN-13: 978-0-19-992458-5 , 2014, Graefe's Archive for Clinical and Experimental Ophthalmology.
[22] A. Miah,et al. Understanding Digital Health as Public Pedagogy: A Critical Framework , 2014 .
[23] Luciano Floridi,et al. The Fourth Revolution: How the infosphere is reshaping human reality , 2014 .
[24] Ruben Amarasingham,et al. The legal and ethical concerns that arise from using complex predictive analytics in health care. , 2014, Health affairs.
[25] Marcel Salathé,et al. Ethical Challenges of Big Data in Public Health , 2015, PLoS Comput. Biol..
[26] Victor A. Richardson,et al. Is Sharing De-Identified Data Legal? The State of Public Health Confidentiality Laws and Their Interplay with Statistical Disclosure Limitation Techniques , 2015, Journal of Law, Medicine & Ethics.
[27] Mark Coeckelbergh,et al. Good Healthcare Is in the “How”: The Quality of Care, the Role of Machines, and the Need for New Skills , 2015 .
[28] Dimitrios I. Fotiadis,et al. Machine learning applications in cancer prognosis and prediction , 2014, Computational and structural biotechnology journal.
[29] K. Mclaughlin. Empowerment: A Critique , 2015 .
[30] C. Petrini. On the "pendulum" of bioethics. , 2015, La Clinica terapeutica.
[31] Zhaozheng Yin,et al. Human Activity Recognition Using Wearable Sensors by Deep Convolutional Neural Networks , 2015, ACM Multimedia.
[32] J. Listgarten,et al. Personalized medicine: from genotypes, molecular phenotypes and the quantified self, towards improved medicine. , 2014, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.
[33] Alistair Wardrope. Relational Autonomy and the Ethics of Health Promotion , 2015 .
[34] Andreas Matthias,et al. Robot Lies in Health Care: When Is Deception Morally Permissible? , 2015, Kennedy Institute of Ethics journal.
[35] Niccolò Tempini,et al. Emerging ethical issues regarding digital health data. On the World Medical Association Draft Declaration on Ethical Considerations Regarding Health Databases and Biobanks , 2016, Croatian medical journal.
[36] L. Floridi,et al. Data ethics , 2021, Effective Directors.
[37] Sarah A. Delgado,et al. The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine’s Computer Age , 2016 .
[38] L. Floridi. Faultless responsibility: on the nature and allocation of moral responsibility for distributed moral actions , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[39] Mariarosaria Taddeo,et al. The ethics of algorithms: Mapping the debate , 2016, Big Data Soc..
[40] E. Juengst,et al. From "Personalized" to "Precision" Medicine: The Ethical and Social Implications of Rhetorical Reform in Genomic Medicine. , 2016, The Hastings Center report.
[41] Thomas Ploug,et al. Meta Consent – A Flexible Solution to the Problem of Secondary Use of Health Data , 2016, Bioethics.
[42] “You hoped we would sleep walk into accepting the collection of our data”: controversies surrounding the UK care.data scheme and their wider relevance for biomedical research , 2015, Medicine, health care, and philosophy.
[43] É. Kleinpeter. Four Ethical Issues of “E-Health” , 2017 .
[44] Jing Zhang,et al. Prediction of Adverse Events in Patients Undergoing Major Cardiovascular Procedures , 2017, IEEE Journal of Biomedical and Health Informatics.
[45] Luciano Floridi,et al. The Logic of Design as a Conceptual Logic of Information , 2017, Minds and Machines.
[46] Ramesh C. Jain,et al. Cybernetic Health , 2017, ArXiv.
[47] Natasha D. Schüll,et al. The Datafication of Health , 2017 .
[48] Alan Borning,et al. A Survey of Value Sensitive Design Methods , 2018, Found. Trends Hum. Comput. Interact..
[49] Iyad Rahwan,et al. Society-in-the-loop: programming the algorithmic social contract , 2017, Ethics and Information Technology.
[50] David Beer,et al. The social power of algorithms , 2017, The Social Power of Algorithms.
[51] Hal Hodson,et al. Google DeepMind and healthcare in an age of algorithms , 2017, Health and Technology.
[52] Luciano Floridi,et al. Digital’s Cleaving Power and Its Consequences , 2017 .
[53] Paul H Mason,et al. The Ethics of Biomedical Big Data , 2017, Journal of Bioethical Inquiry.
[54] John Cheney-Lippold,et al. We Are Data: Algorithms and the Making of Our Digital Selves , 2017 .
[55] F. Cabitza,et al. Unintended Consequences of Machine Learning in Medicine , 2017, JAMA.
[56] Sophia Melanson,et al. We are data: algorithms and the making of our digital selves , 2017 .
[57] Umesh R. Hodeghatta,et al. Unsupervised Machine Learning , 2017 .
[58] W. Nicholson Price,et al. Medical Malpractice and Black-Box Medicine , 2017 .
[59] Bo Xie,et al. Health literacy in the eHealth era: A systematic review of the literature. , 2017, Patient education and counseling.
[60] G. Rees,et al. Letter in response to Google DeepMind and healthcare in an age of algorithms , 2018 .
[61] Nic Fleming,et al. How artificial intelligence is changing drug discovery , 2018, Nature.
[62] Nabile M. Safdar,et al. Protecting Your Patients' Interests in the Era of Big Data, Artificial Intelligence, and Predictive Analytics. , 2018, Journal of the American College of Radiology : JACR.
[63] Jong Wook Kim,et al. Health Big Data Analytics: A Technology Survey , 2018, IEEE Access.
[64] Ali Dag,et al. Predicting graft survival among kidney transplant recipients: A Bayesian decision support model , 2018, Decis. Support Syst..
[65] Wei Dai,et al. Improving Data Quality through Deep Learning and Statistical Models , 2018, ArXiv.
[66] David Townend,et al. Conclusion: harmonisation in genomic and health data sharing for research: an impossible dream? , 2018, Human Genetics.
[67] Eleonore Bayen,et al. Unsupervised Machine Learning to Identify High Likelihood of Dementia in Population-Based Surveys: Development and Validation Study , 2018, Journal of medical Internet research.
[68] Julia E. Powles,et al. Response to DeepMind , 2018 .
[69] J. Cowie,et al. Evaluation of a Digital Consultation and Self-Care Advice Tool in Primary Care: A Multi-Methods Study , 2018, International journal of environmental research and public health.
[70] R. McDougall. Computer knows best? The need for value-flexibility in medical AI , 2018, Journal of Medical Ethics.
[71] E. Kluge,et al. Ethics Certification of Health Information Professionals , 2018, Yearbook of Medical Informatics.
[72] Mariarosaria Taddeo,et al. How AI can be a force for good , 2018, Science.
[73] Gautam Kunapuli,et al. A Decision-Support Tool for Renal Mass Classification , 2018, Journal of Digital Imaging.
[74] Luciano Floridi,et al. Soft ethics, the governance of the digital and the General Data Protection Regulation , 2018, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[75] J. Schouten,et al. Self-quantification and the datapreneurial consumer identity , 2018, Consumption Markets & Culture.
[76] M. Howell,et al. Ensuring Fairness in Machine Learning to Advance Health Equity , 2018, Annals of Internal Medicine.
[77] Luxia Zhang,et al. Big data and medical research in China , 2018, British Medical Journal.
[78] Fei Wang,et al. Deep learning for healthcare: review, opportunities and challenges , 2018, Briefings Bioinform..
[79] E. Vayena,et al. Machine learning in medicine: Addressing ethical challenges , 2018, PLoS medicine.
[80] Parisa Rashidi,et al. Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis , 2017, IEEE Journal of Biomedical and Health Informatics.
[81] N. Shah,et al. Implementing Machine Learning in Health Care - Addressing Ethical Challenges. , 2018, The New England journal of medicine.
[82] E. Vayena,et al. Digital health: meeting the ethical and policy challenges. , 2018, Swiss medical weekly.
[83] Geraint Rees,et al. Clinically applicable deep learning for diagnosis and referral in retinal disease , 2018, Nature Medicine.
[84] Xinli Shi,et al. Review and approval of medical devices in China: Changes and reform. , 2018, Journal of biomedical materials research. Part B, Applied biomaterials.
[85] Mariarosaria Taddeo,et al. Artificial Intelligence and the ‘Good Society’: the US, EU, and UK approach , 2016, Sci. Eng. Ethics.
[86] Rayid Ghani,et al. Machine learning and AI research for Patient Benefit: 20 Critical Questions on Transparency, Replicability, Ethics and Effectiveness , 2018, ArXiv.
[87] Ahmed Hosny,et al. The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards , 2018, Data Protection and Privacy.
[88] Cong Wang,et al. Analysis of Risk Factors for Cervical Cancer Based on Machine Learning Methods , 2018, 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS).
[89] Marc Kohli,et al. Ethics, Artificial Intelligence, and Radiology. , 2018, Journal of the American College of Radiology : JACR.
[90] S. Mahomed,et al. Healthcare, artificial intelligence and the Fourth Industrial Revolution: Ethical, social and legal considerations , 2018, South African Journal of Bioethics and Law.
[91] J. Torous,et al. Augmenting Mental Health in Primary Care: A 1-Year Study of Deploying Smartphone Apps in a Multi-site Primary Care/Behavioral Health Integration Program , 2019, Front. Psychiatry.
[92] W. Price,et al. Potential Liability for Physicians Using Artificial Intelligence. , 2019, JAMA.
[93] B. Mittelstadt. The Ethics of Biomedical ‘Big Data’ Analytics , 2019, Philosophy & Technology.
[94] Ariane Chan,et al. A Review of the Role of Augmented Intelligence in Breast Imaging: From Automated Breast Density Assessment to Risk Stratification. , 2019, AJR. American journal of roentgenology.
[95] K. Voigt,et al. Social Justice, Equality and Primary Care: (How) Can ‘Big Data’ Help? , 2019 .
[96] Parashkev Nachev,et al. Predicting scheduled hospital attendance with artificial intelligence , 2019, npj Digital Medicine.
[97] Jia Fan,et al. Predicting overall survival of patients with hepatocellular carcinoma using a three‐category method based on DNA methylation and machine learning , 2019, Journal of cellular and molecular medicine.
[98] Hannah R Sullivan,et al. Are Current Tort Liability Doctrines Adequate for Addressing Injury Caused by AI? , 2019, AMA journal of ethics.
[99] Jukka-Pekka Onnela,et al. Passive data collection and use in healthcare: A systematic review of ethical issues , 2019, Int. J. Medical Informatics.
[100] Chiara Garattini,et al. Big Data Analytics, Infectious Diseases and Associated Ethical Impacts , 2017, Philosophy & Technology.
[101] Luciano Floridi,et al. What the Near Future of Artificial Intelligence Could Be , 2019, Philosophy & Technology.
[102] John Powell,et al. What is an appropriate level of evidence for a digital health intervention? , 2018, The Lancet.
[103] Luciano Floridi,et al. Clinical applications of machine learning algorithms: beyond the black box , 2019, BMJ.
[104] A. Tekkeşin. Artificial Intelligence in Healthcare: Past, Present and Future. , 2019, Anatolian journal of cardiology.
[105] Jijun Tang,et al. Identification of drug-side effect association via multiple information integration with centered kernel alignment , 2019, Neurocomputing.
[106] John Owens,et al. ‘My Fitbit Thinks I Can Do Better!’ Do Health Promoting Wearable Technologies Support Personal Autonomy? , 2019 .
[107] Benjamin Haibe-Kains,et al. Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data , 2019, European Journal of Nuclear Medicine and Molecular Imaging.
[108] Mauricio Santillana,et al. Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches , 2019, Nature Communications.
[109] Jennifer Dixon,et al. The NHS long term plan , 2019, British Medical Journal.
[110] J. Powell,et al. Characterizing the Digital Health Citizen: Mixed-Methods Study Deriving a New Typology , 2018, Journal of medical Internet research.
[111] J. Denny,et al. Artificial intelligence, bias and clinical safety , 2019, BMJ Quality & Safety.
[112] The Lancet Digital Health. Walking the tightrope of artificial intelligence guidelines in clinical practice. , 2019, The Lancet. Digital health.
[113] J. V. van Delden,et al. Consensus Statement on Public Involvement and Engagement with Data Intensive Health Research , 2018, International journal of population data science.
[114] Russell Greiner,et al. Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning , 2019, npj Schizophrenia.
[115] Yifan Zhou,et al. Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs , 2019, Comput. Biol. Medicine.
[116] L. Floridi,et al. How to Design a Governable Digital Health Ecosystem , 2019, SSRN Electronic Journal.
[117] K. Ngiam,et al. Big data and machine learning algorithms for health-care delivery. , 2019, The Lancet. Oncology.
[118] Daniel Schönberger,et al. Artificial intelligence in healthcare: a critical analysis of the legal and ethical implications , 2019, Int. J. Law Inf. Technol..
[119] Óscar Álvarez-Machancoses,et al. Using artificial intelligence methods to speed up drug discovery , 2019, Expert opinion on drug discovery.
[120] Paul Wicks,et al. Beyond the hype of big data and artificial intelligence: building foundations for knowledge and wisdom , 2019, BMC Medicine.
[121] Hao Lu,et al. RL4health: Crowdsourcing Reinforcement Learning for Knee Replacement Pathway Optimization , 2019, ArXiv.
[122] Jianbin Li,et al. Experience and reflection from China's Xiangya medical big data project , 2019, J. Biomed. Informatics.
[123] Rebecca J Bartlett Ellis,et al. Building the case for actionable ethics in digital health research supported by artificial intelligence , 2019, BMC Medicine.
[124] Zhong Wang. Data integration of electronic medical record under administrative decentralization of medical insurance and healthcare in China: a case study , 2019, Israel Journal of Health Policy Research.
[125] Irina Czogiel,et al. Supervised learning improves disease outbreak detection , 2019, ArXiv.
[126] Thomas Ploug,et al. The right to refuse diagnostics and treatment planning by artificial intelligence , 2020, Medicine, health care, and philosophy.
[127] Leo Anthony Celi,et al. The “inconvenient truth” about AI in healthcare , 2019, npj Digital Medicine.
[128] Yordan Zaykov,et al. Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care , 2019, ArXiv.
[129] L. Floridi,et al. Enabling digital health companionship is better than empowerment. , 2019, The Lancet. Digital health.
[130] Robert M Wachter,et al. Artificial Intelligence in Health Care: Will the Value Match the Hype? , 2019, JAMA.
[131] Á. Ruibal,et al. Prediction of Alzheimer's disease dementia with MRI beyond the short-term: Implications for the design of predictive models , 2019, NeuroImage: Clinical.
[132] Ivana Bartoletti,et al. AI in Healthcare: Ethical and Privacy Challenges , 2019, AIME.
[133] Jie Xu,et al. The practical implementation of artificial intelligence technologies in medicine , 2019, Nature Medicine.
[134] Eric Racine,et al. Healthcare uses of artificial intelligence: Challenges and opportunities for growth , 2019, Healthcare management forum.
[135] Luciano Floridi,et al. From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices , 2019, ArXiv.
[136] Ariel Rosenfeld,et al. Big Data Analytics and AI in Mental Healthcare , 2019, ArXiv.
[137] Ezio Di Nucci,et al. Should we be afraid of medical AI? , 2019, Journal of Medical Ethics.
[138] Andrea Martani,et al. Stay fit or get bit - ethical issues in sharing health data with insurers' apps. , 2019, Swiss medical weekly.
[139] Vicente García-Díaz,et al. A neural network approach to predict early neonatal sepsis , 2019, Comput. Electr. Eng..
[140] Eric J Topol,et al. High-performance medicine: the convergence of human and artificial intelligence , 2019, Nature Medicine.
[141] Ross E. G. Upshur,et al. Three Problems with Big Data and Artificial Intelligence in Medicine , 2019, Perspectives in biology and medicine.
[142] Mariarosaria Taddeo,et al. The Ethics of Digital Well-Being: A Thematic Review , 2020, Sci. Eng. Ethics.
[143] L. Floridi,et al. An ethically mindful approach to AI for health care , 2020, The Lancet.
[144] Luciano Floridi,et al. The Limits of Empowerment: How to Reframe the Role of mHealth Tools in the Healthcare Ecosystem , 2019, Sci. Eng. Ethics.
[145] Timnit Gebru,et al. Datasheets for datasets , 2018, Commun. ACM.