Doctors & Patients Demand Answers: Why Explainable AI is Crucial for Trust in Healthcare

Overview

Paper Summary › Explain Like I'm Five › Conflicts of Interest › Identified Limitations › Rating Explanation › Good to know › Topic Hierarchy › File Information ›

Paper Summary

Paperzilla title

Explainability in clinical decision support systems (CDSS) is crucial for maintaining patient autonomy, trust, and ethical medical practice. Opaque AI algorithms pose challenges to informed consent, shared decision-making, and the equitable distribution of healthcare resources, potentially hindering the responsible integration of AI into medicine.

Explain Like I'm Five

Scientists found that when computers help doctors make choices, it's super important to know *how* the computer decided. This helps patients understand, trust their doctors, and pick what's best for them.

Possible Conflicts of Interest

The authors declare no competing interests, and no obvious conflicts are apparent from their affiliations or the funding source (EU Horizon 2020).

Identified Limitations

Limited Technical Evaluation

The conceptual analysis methodology, while appropriate for exploring ethical and societal implications, might not be sufficient for rigorously evaluating the technical aspects of explainability. A stronger technical analysis, possibly incorporating empirical studies or case studies of specific explainable AI systems, would enhance the paper's contribution.

Narrow Focus on CDSS

Focusing solely on CDSS limits the scope of the analysis, as explainability is relevant to other healthcare AI applications (e.g., medical imaging, drug discovery). Broadening the scope would provide a more comprehensive perspective on the role of explainability in healthcare.

Lack of a Clear Definition of Explainability

The paper acknowledges the lack of a universally accepted definition of explainability but doesn't offer a clear operational definition for its analysis. This ambiguity might lead to inconsistencies in how explainability is understood and applied across different sections of the paper.

Rating Explanation

This paper provides a valuable multidisciplinary perspective on the critical issue of explainability in healthcare AI. The analysis covers technological, legal, medical, and patient viewpoints, offering a comprehensive assessment of the ethical implications. While the technical analysis could be strengthened, the paper's strength lies in its ethical discussion and its focus on the importance of explainability for patient trust and autonomy.

Good to know

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Topic Hierarchy

Domain: Health Sciences

Field: Medicine

Subfield: Health Informatics

File Information

Original Title: Explainability for artificial intelligence in healthcare: a multidisciplinary perspective

Uploaded: July 14, 2025 at 11:25 AM

Privacy: Public