Article
Psychiatry
Michael R. MacIntyre, Richard G. Cockerill, Omar F. Mirza, Jacob M. Appel
Summary: The rapid advancement of AI and machine learning provide new tools for clinicians. AI has the potential to assist in medical decision-making capacity assessments, but concerns such as biased decisions, autonomy, and accountability make it unlikely to replace human evaluators anytime soon.
PSYCHIATRY RESEARCH
(2023)
Article
Computer Science, Information Systems
Max Kasun, Katie Ryan, Jodi Paik, Kyle Lane-McKinley, Laura Bodin Dunn, Laura Weiss Roberts, Jane Paik Kim
Summary: This study investigates the ethical considerations of machine learning researchers in medicine when developing machine learning tools for clinical care. The researchers found that while the participants had positive attitudes towards continued machine learning innovation, they also expressed concerns regarding data sampling and labeling, as well as algorithm training and testing. They suggested the need for increased interdisciplinary training and more coordinated approaches to address ethics issues.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
(2023)
Article
Education & Educational Research
Andy Nguyen, Ha Ngan Ngo, Yvonne Hong, Belle Dang, Bich-Phuong Thi Nguyen
Summary: This paper explores the existence of a global consensus on ethical artificial intelligence in education by analyzing policies and guidelines from international organizations. It proposes a set of ethical principles to guide educational stakeholders in the development and deployment of ethical and trustworthy artificial intelligence in education.
EDUCATION AND INFORMATION TECHNOLOGIES
(2023)
Article
Business
Jean-Marie John-Mathews
Summary: The lack of ethics and the interpretability of AI decisions are two fundamental issues facing the development of Artificial Intelligence (AI). Research shows that interpretable AI explanations may lack denunciatory power and this power is highly dependent on the context in which the explanation takes place, such as the gender or education of the recipient. AI ethics tools are sometimes too flexible and self-regulation is not enough to address ethical issues.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2022)
Article
Social Issues
Leonie Bossert, Thilo Hagendorff
Summary: The impact of AI on animals, including animal testing and applications in agricultural monitoring and marketing, is often overlooked in debates. While some AI applications may have negative effects on animals, there are also beneficial applications, particularly in nature and wildlife conservation. This paper aims to fill a research gap by providing an overview of how AI technologies are applied to animals and how this affects them.
TECHNOLOGY IN SOCIETY
(2021)
Article
Education, Scientific Disciplines
Al Dowie
Summary: This paper proposes an approach to professional medical ethics education that combines the Socratic dialogue method with the educational theories developed by American pragmatic philosophers. By engaging students in maieutic teaching and learning through actual clinical narratives, educators can help students develop their ethical sense and determine professional ethical obligations.
Editorial Material
Health Care Sciences & Services
Gemma Sharp, John Torous, Madeline L. West
Summary: The use of AI in addressing eating disorders and body image concerns is promising, but it comes with risks. It is crucial to establish responsible standards to mitigate these risks and ensure the success and safety of technology and users.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Eda Yirmibesoglu Erkal, Aslihan Akpinar, Haldun Sukru Erkal
Summary: The use of Artificial Intelligence in radiotherapy workflow is expected to have a significant impact, but it has also raised concerns about ethical agreements between patients and physicians. Addressing ethical concerns by recording and implementing personal and social moral values can help ensure patient safety and resolve ethical issues in medical decision-making.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2021)
Review
Health Care Sciences & Services
Sreenidhi Prakash, Jyotsna Needamangalam Balaji, Ashish Joshi, Krishna Mohan Surapaneni
Summary: Artificial intelligence has the potential to revolutionize healthcare, but it also brings about ethical and legal challenges. This review examines the ethical concerns of AI applications in healthcare and provides recommendations for addressing these issues. A multifaceted approach involving various stakeholders is essential for finding feasible solutions.
JOURNAL OF PERSONALIZED MEDICINE
(2022)
Review
Computer Science, Information Systems
Anto Cartolovni, Ana Tomicic, Elvira Lazic Mosler
Summary: This comprehensive review highlights the ethical, legal, and social implications (ELSI) of AI in healthcare. It identifies key issues surrounding AI algorithms, physicians, patients, and healthcare in general, including patient safety, algorithmic transparency, lack of regulation, liability and accountability. While AI shows potential in improving patient care, it is important to address the complex ELSI concerns before implementation.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2022)
Review
Communication
Erik Hermann
Summary: This article systematically examines the ethical challenges of using AI for mass personalization of communication content from a multi-stakeholder perspective, advocating the importance of basic AI literacy in promoting individual and social good.
NEW MEDIA & SOCIETY
(2022)
Review
Public, Environmental & Occupational Health
Blanka Klimova, Marcel Pikhart, Jaroslav Kacetl
Summary: This article describes the ethical issues related to the use of artificial intelligence in education and emphasizes the need for cooperation and research among all stakeholders to ensure ethical use of AI in education.
FRONTIERS IN PUBLIC HEALTH
(2023)
Article
Medicine, General & Internal
T. Y. Alvin Liu, Jo-Hsuan Wu
Summary: Medical specialties, such as ophthalmology, with access to large amounts of imaging data have been pioneers in the medical artificial intelligence (AI) revolution driven by deep learning (DL) and big data. The rise of AI and big data has brought about concerns regarding bias and privacy. These concerns have been addressed to some extent in ophthalmology through low-shot learning, generative DL, federated learning, and a model-to-data approach, as demonstrated by various groups of investigators. However, a more comprehensive approach is necessary to effectively tackle the ethical and societal challenges associated with the rise of AI in ophthalmology, considering AI as a sociotechnical technology that both shapes and is shaped by social phenomena.
FRONTIERS IN MEDICINE
(2022)
Article
Medical Informatics
Jie Zhang, Zong-ming Zhang
Summary: The trustworthiness of medical AI is influenced by various factors such as data quality, algorithmic bias, opacity, safety and security, and responsibility attribution, which need to be managed and addressed.
BMC MEDICAL INFORMATICS AND DECISION MAKING
(2023)
Letter
Anatomy & Morphology
James K. Ruffle, Chris Foulon, Parashkev Nachev
Summary: Foundational models like ChatGPT heavily rely on large-scale data made available by the internet, which introduces exposure to diverse material in terms of logic, facts, morals, and legality. While data scaling can be resolved with more computational resources, complex semantic filtering requires human intervention since it presupposes the very abilities the models aim to acquire. This necessitates large-scale human supervision not only in training, but also in model output, resulting in subjective perspectives imparted by the model's creator. The conflicting pressures to minimize cost and maximize quality present challenges in this process, especially when it comes to complex semantics such as moral values.
BRAIN STRUCTURE & FUNCTION
(2023)
Editorial Material
Ethics
Torbjorn Gundersen, Kristine Baeroe
AMERICAN JOURNAL OF BIOETHICS
(2022)
Article
History & Philosophy Of Science
Torbjorn Gundersen, Cathrine Holst
Summary: This paper examines the conditions for trustworthy science advice mechanisms, emphasizing on possession of relevant expertise, justified moral and political considerations, as well as proper institutional design. The case of temporary advisory committees in Norway is explored to assess these conditions. Lessons drawn include the importance of distinguishing between well-placed and de facto trust, the significance of some conditions over others, and the influence of institutional design, social, and political context on trust and trustworthiness.
SOCIAL EPISTEMOLOGY
(2022)
Article
Health Care Sciences & Services
Kristine Baeroe, Torbjorn Gundersen, Edmund Henden, Kjetil Rommetveit
Summary: This paper discusses the challenge of reconciling the idea of fairness in medical algorithms and machine learning with broader discussions on fairness and health equality in health research. The study utilizes a theoretical and ethical analysis to explore the topic, revealing that ensuring comprehensive fairness in machine learning is connected to three quandaries and one dilemma. The paper concludes that further analytical work is necessary to accurately conceptualize fairness in machine learning and reflect the complexity of justice and fairness concerns in the field of health research.
BMJ HEALTH & CARE INFORMATICS
(2022)
