R0044/2026-03-29/Q002/SRC03
JAMA Editorial: Automation Bias and Assistive AI — Risk of Harm From AI-Driven CDS (2023)
Source
| Field |
Value |
| Title |
Automation Bias and Assistive AI: Risk of Harm From AI-Driven Clinical Decision Support |
| Publisher |
JAMA (Journal of the American Medical Association) |
| Author(s) |
Khera, Simon, Ross |
| Date |
December 19, 2023 |
| URL |
https://jamanetwork.com/journals/jama/article-abstract/2812931 |
| Type |
Research paper (editorial in peer-reviewed journal) |
Summary
| Dimension |
Rating |
| Reliability |
High |
| Relevance |
High |
| Bias: Missing data |
Some concerns |
| Bias: Measurement |
N/A |
| 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 |
JAMA editorial — one of the most prestigious medical journals. Authors are established clinical informatics researchers. |
| Relevance |
Directly addresses automation bias in AI-driven clinical decision support and its risk to patient safety. |
| Bias flags |
Full text behind paywall; analysis based on abstract and secondary reporting. |
| Evidence ID |
Summary |
| SRC03-E01 |
Clinician deference to AI models leads to patient harm, with 31% higher misdiagnosis rates for minority patients |