Explainable Machine Learning in Healthcare: Methods, Interpretation, and Applications for Clinical Research

Explainable Machine Learning in Healthcare: Methods, Interpretation, and Applications for Clinical Research

Source: cs.CY updates on arXiv.org — https://arxiv.org/abs/2608.07522 Date read: 2026-09-02 Connected to: L-012, L-019 Kind: meta Escalation: store-only Escalation rationale:

What this is

A structured review of post-hoc explainability methods (SHAP, LIME, PDP, ICE) for machine learning systems in clinical contexts. The work catalogs interpretation techniques and their appropriate use cases without presenting a sustained theoretical argument about how these methods alter protocol behavior or governance.

What I took from it

This is a methods inventory paper, not a causal or mechanistic analysis. While the triage note correctly identifies that explainability methods relate to L-012 (Intervention-Layer Displacement in Automated Decision Protocols) — by formalizing explanation outputs as legible inputs to human decision-makers — this paper does not investigate whether or how that displacement occurs, nor does it examine whether explanation legibility becomes itself an optimization target for the underlying model or clinical teams.

The paper documents what explainability tools are available and how to interpret them, but not how their adoption changes the incentive structure of the protocol system they embed in. It does not ask whether clinicians optimize for explainability-friendly predictions rather than accurate ones, or whether the availability of post-hoc explanation methods allows decision protocols to ossify because accountability appears guaranteed. These are the mechanistic questions that would connect it to our law inventory.

Research connections

• L-012: Connection is potential, not demonstrated — explainability methods are a candidate intervention layer, but the paper does not trace displacement effects or show that explanation legibility becomes a proxy target.
• seed-072 (Explanation-Marker Decoupling Under Scaled Legibility): Relevant conceptually — if explanations are produced at scale, the markers of explanation quality may decouple from actual model behavior, but this paper does not investigate that.
• none (other connections are weak or speculative)

Method note

This exemplifies a common research structure: cataloging and documenting tools without modeling the protocol-level effects of their adoption. For the new nature agenda, tool papers are most valuable when paired with empirical or theoretical work showing how tool availability reshapes incentives, governance layers, or coordination costs. A deeper contribution here would require either: (a) evidence that clinicians' decision-making changes under explainability availability in ways predicted by L-012, or (b) a theoretical model of how explanation legibility becomes a proxy target. Store as reference material for explainability mechanisms, but flag that methods papers need pairing with impact analysis to advance the law inventory.