R0043/2026-03-28/Q001/SRC09
Healthcare AI — Acquiescence problem and dialogic reasoning
Source
| Field |
Value |
| Title |
Dialogical AI for Cognitive Bias Mitigation in Medical Diagnosis |
| Publisher |
MDPI Applied Sciences |
| Author(s) |
Various |
| Date |
2025 |
| URL |
https://www.mdpi.com/2076-3417/16/2/710 |
| Type |
Research paper (peer-reviewed) |
Summary
| Dimension |
Rating |
| Reliability |
Medium-High |
| Relevance |
High |
| Bias: Missing data |
Low risk |
| Bias: Measurement |
Low risk |
| Bias: Selective reporting |
Low risk |
| Bias: Randomization |
N/A — not an RCT |
| Bias: Protocol deviation |
N/A — not an RCT |
| Bias: COI/Funding |
Low risk |
Rationale
| Dimension |
Rationale |
| Reliability |
Peer-reviewed; proposes a concrete framework (Dialogic Reasoning Framework) |
| Relevance |
Introduces "acquiescence problem" as healthcare-specific terminology for AI that confirms rather than challenges clinicians |
| Bias flags |
No significant concerns |
| Evidence ID |
Summary |
| SRC09-E01 |
Healthcare "acquiescence problem" — AI passively confirming clinician hypotheses |