arXiv:2606.31616v1 Announce Type: new Abstract: Medical Artificial Intelligence (AI) is widely expected to transform clinical practice, yet the decision-making processes of many Machine Learning (ML) models remain opaque. Explainability has been advanced as a partial remedy to clarify why AI generates predictions, particularly in high-stakes contexts. Despite ongoing efforts, debates on what constitutes an adequate medical explanation remain unsettled. Yet, explanation has long been a central topic of inquiry in the philosophy of science and medicine. The insights developed in these fields, ho
The rapid deployment of AI in sensitive fields like healthcare is pushing the urgent need for robust explainability frameworks, making this discussion timely.
Understanding and standardizing scientific explanations for AI in health sciences is crucial for regulatory approval, public trust, and ethical deployment of transformative technologies.
This research contributes to establishing foundational principles for AI interpretability in healthcare, potentially leading to more transparent and trustworthy medical AI systems.
- · Healthcare AI developers prioritizing explainability
- · Patients benefiting from transparent AI-driven diagnoses
- · Regulators developing AI governance frameworks
- · Philosophers of science and medicine
- · Opaque 'black box' AI models in healthcare
- · Companies neglecting explainability in medical AI
- · Healthcare systems unable to validate AI decisions
Increased focus on interpretable AI design in medical technology development.
Development of industry standards and regulatory requirements for AI explainability in healthcare.
Enhanced public and professional trust in AI applications, accelerating adoption and integration into clinical practice.
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Read at arXiv cs.AI