Mitigating AI Risk in Healthcare through Ethical Data Science
A special issue of Healthcare (ISSN 2227-9032).
Deadline for manuscript submissions: 31 January 2025 | Viewed by 150
Special Issue Editors
Interests: ethical data science; artificial intelligence (AI); AI risk mitigation
Interests: data science; mental health; explainable AI; LLMs(large language models)
Interests: natural language processing on clinical/ healthcare data; hallucinations in LLMs (large language models); LLMs in healthcare; AI in healthcare, AI for clinical decision support; AI for clinician burnout prevention; AI ethics; principles of AI in medicine/healthcare
Special Issue Information
Dear Colleagues,
Artificial Intelligence (AI) has the potential to improve healthcare outcomes, yet AI technologies can also pose risks to individuals, communities, and society. For example, AI systems that are trained on biased data may perpetuate the discrimination against disadvantaged patients. Bias in AI, defined as “unfair systematic error”, or more specifically, “the inclination or prejudice of a decision made by an AI system which is for or against one person or group, especially in a way considered to be unfair” is a topic of vital importance in the informatics and healthcare communities. As AI systems proliferate in clinical decision support and other applications crucial to people’s wellbeing, bias and the risks it creates in AI demand careful consideration and in-depth discussion. The burgeoning field of ethical data science provides a venue for exploring the causes of bias in AI, and how to correct them, rendering AI output as true, realistic, and fair to all demographic and socio-economic groups.
This Special Issue aims to publish original studies and reviews addressing major facets of AI risks and their mitigation in healthcare, including legal, machine learning, statistical, mathematical, and clinical perspectives.
Submitted manuscripts should describe original work and not have been previously published, or be under consideration elsewhere (except in conference proceedings or as a preprint).
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:
- Original research
- Systematic reviews and meta-analyses
- Policy and practice reviews
- Area perspective
Dr. T. Elizabeth Workman
Dr. Phillip Ma
Dr. Adnan Lakdawala
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- AI and public trust
- transparency in AI
- AI legal and policy issues
- AI bias considerations addressing data
- fairness in healthcare AI and open data
- policies that support ethical AI
- bias assessment in large language model applications
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