Guest Editorial Explainable AI: Towards Fairness, Accountability, Transparency and Trust in Healthcare
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The papers in this special section focus on explainable artificial intelligence (AI) in healthcare services. Recent advances in AI, precision health, and medicine have paved the way for the accelerated adaptation and use of intelligent tools and systems in decision-making processes across the healthcare spectrum. Insights and knowledge derived from complex analytics are used to implement diagnostic and therapeutic solutions and targeted interventions in individuals and communities across the globe. Given the complexity of the current multi-dimensional clinical and public health data landscape, providing explainability in the context of socio-environmental and technical systems is a key to revealing pathways from socio-economic disadvantages to health disparities and implementing equitable interventions. As the complexity of the underlying data sets and AI-based algorithms increases, the explainability and justifiability of the insights generated decrease. Humans need to understand the underlying mechanism behind these insights to know whether they are sound, correct, trustable, and justifiable to make informed decisions. Lack of understandability and explainability in the biomedical domain often leads to poor transparency and accountability and ultimately lower quality of care and suboptimal and unfair health policies. Explainability is considered one of the prerequisites for deep medicine, where AI is meant to provide composite, panoramic views of individuals’ medical data.
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The integration of AI into healthcare alters traditional decision-making practices based on epistemic probability and prudence and raises ethical, legal, and medical dilemmas when making critical decisions about human lives and medical conditions.
“Additionally, the integration of AI into healthcare alters traditional decision-making practices based on epistemic probability and prudence (Parviainen, Rantala (2022)) and raises ethical, legal, and medical dilemmas when it comes to making critical decisions about human lives and medical conditions (Shaban-Nejad et al., 2021;Vilaza & McCashin, 2021).”
From Navigating the perils of artificial intelligence: a focused review on ChatGPT and responsible research and innovation (2024) · cites this paper as Shaban-Nejad et al., 2021
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Cites
XAI—Explainable artificial intelligence
Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., & Yang, G.-Z. (2019) · Science Robotics · doi:10.1126/scirobotics.aay7120 · Cited by 2,053 · see citing works
Causability and explainability of artificial intelligence in medicine
Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1312 · Cited by 1,885 · see citing works
(2019) · Cited by 986 · see citing works
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Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution
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Morphological and molecular breast cancer profiling through explainable machine learning
Binder, A., Bockmayr, M., Hägele, M., Wienert, S., Heim, D., Hellweg, K., Ishii, M., Stenzinger, A., Hocke, A., Denkert, C., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00303-4 · Cited by 156 · see citing works
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Self-citationAnguita‐Ruiz, A., Segura‐Delgado, A., Alcalá, R., Aguilera, C. M., & Alcalá‐Fdez, J. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007792 · Cited by 63 · see citing works
Ammar, N., & Shaban‐Nejad, A. (2020) · JMIR Medical Informatics · doi:10.2196/18752 · Cited by 62 · see citing works
Self-citationSeven pillars of precision digital health and medicine
Shaban‐Nejad, A., Michalowski, M., Peek, N. B., Brownstein, J. S., & Buckeridge, D. L. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101793 · Cited by 37 · see citing works
Self-citationExplainability and Interpretability: Keys to Deep Medicine
Shaban-Nejad, A., Michalowski, M., & Buckeridge, D. L. (2021) · Studies in Computational Intelligence · doi:10.1007/978-3-030-53352-6_1 · Cited by 35 · see citing works
Self-citationGeo-clustered chronic affinity: pathways from socio-economic disadvantages to health disparities
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Self-citationHealth Intervention Evaluation Using Semantic Explainability and Causal Reasoning
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Flagged first, then by citations
Research works printed but not linked: 12
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Cited by
Sorted by citations
Ali, O., Abdelbaki, W., Shrestha, A., Elbasi, E., Alryalat, M. A. A., & Dwivedi, Y. K. (2023) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2023.100333 · Cited by 568 · see citing works
Polyportis, A., & Pahos, N. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-023-02464-6 · Cited by 64 · see citing works
Wang, B., Asan, O., & Mansouri, M. (2023) · JMIR Human Factors · doi:10.2196/49788 · Cited by 23 · see citing works
- Authors
- Shaban-Nejad, A., Michalowski, M., Brownstein, J., & Buckeridge, D.
- Affiliations
- University of Minnesota (US)
- Boston Children's Hospital (US)
- McGill University (CA)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2021-07
- Volume
- 25
- Issue
- 7
- Pages
- 2374-2375
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W3185885987
- Cited by
- 71
- References
- 15
- Citations by year
- 2026: 10 · 2025: 13 · 2024: 19 · 2023: 17 · 2022: 9 · 2021: 3
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- explainable AI, artificial intelligence in healthcare, fairness, accountability, transparency, trust, health disparities, clinical decision support, precision health
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