The DOI matches its Crossref record.
Ensuring AI explainability in healthcare: problems and possible policy solutions
Abstract
AI promises to address health services’ quality and cost challenges, however, errors and bias in medical devices decisions pose threats to human health and life. This has also led to the lack of trust in AI medical devices among clinicians and patients. The goal of this article is to assess whether AI explainability principle established in numerous ethical AI frameworks can help address these and other challenges posed by AI medical devices. We first define the AI explainability principle, delineate it from the AI transparency principle, and examine which stakeholders in healthcare sector would need AI to be explainable and for what purpose. Second, we analyze whether explainable AI in healthcare is capable of achieving its intended goals. Finally, we examine robust regulatory approval framework as an alternative – and a more suitable – way in addressing challenges caused by black-box AI.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Patients, with their highly limited technical and clinical knowledge, are less likely to draw relevant insights from such technical explanation modalities such as activation maps and feature attribution plots [119].”
From Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: Systematic Review (2026) · cites this paper as [119]
Citations
Cited by
Sorted by citations
Mersha, M. A., Lâm, K. N., Wood, J., AlShami, A. K., & Kalita, J. K. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128111 · Cited by 346
“Transparency and accountability [22], fairness and bias mitigation [165], ethical frameworks and guidelines [167], privacy and confidentiality [168] are some of the key ethical aspects of XAI in healthcare.”
BackgroundA historical perspective of biomedical explainable AI research
Malinverno, L., Barros, V., Ghisoni, F., Visonà, G., Kern, R., Nickel, P. J., Ventura, B. E., Šimić, I., Stryeck, S., Manni, F., et al. (2023) · Patterns · doi:10.1016/j.patter.2023.100830 · Cited by 46
“42 Briefly, XAI algorithms can be divided into two main categories: transparent models that convey some intrinsic degree of interpretability and post hoc explainability techniques.”
BackgroundBlackman, J., & Veerapen, R. J. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-02891-2 · Cited by 38
“…o v e rr i d e i n c o rr e c t d e c i s i o n s a n d t h u s a v o i d h a r m , a s w e ll a s h e l p i m p r o v e A I t e c hn o l o g y ” [ 30 ] “ A p r o t o t y p i c a l e x a m p l e o f t h i s i s a [ h e a l t h c a r e m a c h i n e l e a r n i n g ] m o d e l w h i c h e rr o n e…”
InfluentialToumaj, S., Heidari, A., & Navimipour, N. J. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103243 · Cited by 36
Settouti, N., & Saidi, M. (2023) · Evolutionary Intelligence · doi:10.1007/s12065-023-00833-3 · Cited by 15
“In recent years, there has been growing interest in using explainable machine learning models, which provide inter-pretable predictions, in the healthcare field [4, 12, 20, 22, 39, 45, 53].”
Metadata
- Authors
- Aranovich, T. D. C., & Matulionyte, R.
- Affiliations
- Universidade Federal do Rio Grande do Sul (BR)
- Law School, Macquarie University, Sydney, Australia
- Corresponding author
- Aranovich, Tatiana de Campos
- Venue
- Information & Communications Technology Law
- Publisher
- Informa UK Limited
- Published
- 2022-11-14
- Volume
- 32
- Issue
- 2
- Pages
- 259-275
- ISSN
- 1360-0834 (ISSN-L)
- 1469-8404
- OpenAlex
- W4308998881
- Cited by
- 18
- References
- 0
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 2 · 2023: 2
- FWCI
- 1.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Information & Communications Technology Law (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- explainable AI, artificial intelligence in healthcare, AI medical devices, AI transparency, algorithmic bias, AI errors, trust in AI, AI ethics framework, regulatory approval, black-box AI, AI policy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Ensuring AI explainability in healthcare: problems and possible policy solutions. https://app.cochat.ai/papers/10.1080/13600834.2022.2146395
- The paper
- Aranovich, T. D. C., & Matulionyte, R. (2023). Ensuring AI explainability in healthcare: problems and possible policy solutions. Information & Communications Technology Law. https://doi.org/10.1080/13600834.2022.2146395
