Bridging AI and Clinical Reasoning: Abductive Explanations for Alignment on Critical Symptoms
arXiv:2602.13985v2 Announce Type: replace Abstract: Artificial intelligence (AI) has demonstrated strong potential in clinical diagnostics, often achieving accuracy comparable to or exceeding that of human experts. A key challenge, however, is that AI reasoning frequently diverges from structured clinical frameworks, limiting trust, interpretability, and adoption. Critical symptoms, pivotal for rapid and accurate decision-making, may be overlooked by AI models even when predictions are correct. Existing post hoc explanation methods provide limited transparency and lack formal guarantees. To ad
The increasing deployment of AI in critical sectors like healthcare, coupled with growing demands for transparency and accountability, makes bridging the gap between AI reasoning and human understanding a priority now.
This development addresses a key obstacle to AI adoption in clinical settings: the lack of trust due to divergent reasoning, which is crucial for ethical deployment and patient safety.
The focus shifts from mere predictive accuracy to interpretable and alignable AI, potentially leading to more robust and trustworthy AI diagnostic tools that integrate seamlessly with clinical workflows.
- · AI healthcare developers
- · Healthcare providers (doctors)
- · Patients
- · Clinical AI research
- · AI models lacking explainability
- · Healthcare institutions with low AI adoption
- · Companies relying solely on 'black box' AI
AI diagnostic tools gain increased adoption and trust in clinical environments due to improved interpretability.
New regulatory frameworks emerge that mandate explainability and clinical alignment for AI in medicine, shaping future AI development.
The development of 'clinical reasoning AI agents' could emerge, capable of not just diagnosis but also explaining their rationale to human experts, potentially automating parts of resident training.
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Read at arXiv cs.AI