A5000641874- Artificial Intelligence in Healthcare and Education
- Ethics in Clinical Research
- Biomedical Ethics and Regulation
- Ethics and Social Impacts of AI
- Medical Malpractice and Liability Issues
- COVID-19 Digital Contact Tracing
- Pluripotent Stem Cells Research
- Medical and Health Sciences Research
- Law, AI, and Intellectual Property
- Privacy-Preserving Technologies in Data
- Healthcare cost, quality, practices
- Machine Learning in Healthcare
- Health Systems, Economic Evaluations, Quality of Life
- SARS-CoV-2 and COVID-19 Research
- Ethics and Legal Issues in Pediatric Healthcare
- Autopsy Techniques and Outcomes
- Digital Transformation in Law
- Digital Imaging in Medicine
- Neuroethics, Human Enhancement, Biomedical Innovations
- COVID-19 Pandemic Impacts
- Explainable Artificial Intelligence (XAI)
- Economic and Financial Impacts of Cancer
- Colorectal Cancer Screening and Detection
- CRISPR and Genetic Engineering
- Patient Dignity and Privacy
Dickinson College
2021-2025
Pennsylvania State University
2019-2025
University of Illinois Urbana-Champaign
2024-2025
Chicago Kent College of Law
2024
Dickinson State University
2019-2022
Harvard University
2019-2021
Hebrew University of Jerusalem
2021
University of Augsburg
2021
King's College London
2021
Western University
2021
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The benefits of explainable artificial intelligence are not what they appear
Explainability for artificial intelligence (AI) in medicine is a hotly debated topic. Our paper presents review of the key arguments favor and against explainability AI-powered Clinical Decision Support System (CDSS) applied to concrete use case, namely an CDSS currently used emergency call setting identify patients with life-threatening cardiac arrest. More specifically, we performed normative analysis using socio-technical scenarios provide nuanced account role CDSSs allowing abstractions...
This Viewpoint discusses the potential use of generative artificial intelligence (AI) in medical care and liability risks for physicians using technology, as well offers suggestions safeguards to protect patients.
This chapter will map the ethical and legal challenges posed by artificial intelligence (AI) in health care suggest directions for resolving them. Section 1 briefly clarify what AI is 2 give an idea of trends strategies United States (U.S.) Europe, thereby tailoring discussion to debate AI-driven care. be followed 3 a four primary challenges, namely:(1) informed consent use, (2) safety transparency, (3) algorithmic fairness biases, (4) data privacy. 4 then analyze five U.S. Europe: (1)...
Policy Points With increasing integration of artificial intelligence and machine learning in medicine, there are concerns that algorithm inaccuracy could lead to patient injury medical liability. While prior work has focused on malpractice, the ecosystem consists multiple stakeholders beyond clinicians. Current liability frameworks inadequate encourage both safe clinical implementation disruptive innovation intelligence. Several policy options ensure a more balanced system, including...
Prioritize risk monitoring to address the “update problem”
Abstract Companies and healthcare providers are developing implementing new applications of medical artificial intelligence, including the intelligence sub-type machine learning (MML). MML is based on application (ML) algorithms to automatically identify patterns act data guide clinical decisions. poses challenges raises important questions, (1) How will regulators evaluate MML-based devices ensure their safety effectiveness? (2) What additional considerations should be taken into account in...
Abstract When applied in the health sector, AI-based applications raise not only ethical but legal and safety concerns, where algorithms trained on data from majority populations can generate less accurate or reliable results for minorities other disadvantaged groups.
This Viewpoint discusses a proposed DHHS rule to address discrimination in clinical algorithms and the need for additional considerations ensure burden of liability biased is not disproportionately placed on health care professionals.
This Viewpoint summarizes a recent lawsuit alleging that hospital violated patients’ privacy by sharing electronic health record (EHR) data with Google for development of medical artificial intelligence (AI) and discusses how the federal court’s decision in case provides key insights hospitals planning to share EHR for-profit companies developing AI.
Questions about the future of 23andMe underscore challenges inherent to a legal system that relies on privacy policies protect consumer data, while also treating those data as valuable asset.
This Viewpoint reviews the ethical and legal implications of using ambient intelligence, use artificial intelligence–based technologies to monitor health care quality measures like handwashing patient falls in setting.
Policy Points Millions of life‐sustaining implantable devices collect and relay massive amounts digital health data, increasingly by using user‐downloaded smartphone applications to facilitate data clinicians via manufacturer servers. Our analysis privacy laws indicates that most US patients may have little access their own in the United States under Health Insurance Portability Accountability Act Privacy Rule, whereas EU General Data Protection Regulation California Consumer grant greater...