In Defense of Post Hoc Explanations in Medical AI
Bibliographic Data
| ID | 17536173 |
|---|---|
| Authors | Joshua Hatherley (0000-0002-8581-9669), Lauritz Aastrup Munch (0000-0002-3510-5422), Jens Christian Bjerring (0000-0001-8755-6746) |
| Year | 2026 |
| Volume | 56 |
| Issue | 1 |
| Pages | 40-46 |
| Publication date | 2026-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | The Hastings Center Report (JOURNAL) |
| Journal identifiers | ISSN: 0093-0334 • E-ISSN: 1552-146X |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/hast.4971 |
| PMID | 41639026 |
| OpenAlex | W7128078825 |
| Language | EN |
| References cited | 42 |
Since the early days of the explainable artificial intelligence movement, post hoc explanations have been praised for their potential to improve user understanding, promote trust, and reduce patient-safety risks in black box medical AI systems. Recently, however, critics have argued that the benefits of post hoc explanations are greatly exaggerated since they merely approximate, rather than replicate, the actual reasoning processes that black box systems take to arrive at their outputs. In this paper, we aim to defend the value of post hoc explanations against this recent critique. We argue that even if post hoc explanations do not replicate the exact reasoning processes of black box systems, they can still improve users' functional understanding of black box systems, increase the accuracy of clinician-AI teams, and assist clinicians in justifying their AI-informed decisions. While post hoc explanations are not a silver-bullet solution to the black box problem in medical AI, they remain a useful strategy for addressing it
Black box · Post hoc · Post-hoc analysis · Replicate · Wireless ad hoc network · Artificial Intelligence in Healthcare and Education · Clinical Reasoning and Diagnostic Skills · Explainable Artificial Intelligence (XAI
To Trust or to Think
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
On the ethics of algorithmic decision-making in healthcare
A Survey of Methods for Explaining Black Box Models
High-performance medicine
Putting explainable AI in context
AI and the need for justification (to the patient)
Conceptualizing understanding in explainable artificial intelligence (XAI)
The Virtues of Interpretable Medical AI
Defining the undefinable
On the Opacity of Deep Neural Networks
Against Interpretability
Artificial Intelligence and Patient-Centered Decision-Making
Transparency in Algorithmic and Human Decision-Making
The mindlessness of ostensibly thoughtful action
| Citation velocity | historical |
|---|---|
| Highly cited | No |