R0044/2026-03-29/Q002/SRC04
"Exploring the risks of automation bias in healthcare AI: A Bowtie analysis" (ScienceDirect, 2024)
Source
| Field |
Value |
| Title |
Exploring the risks of automation bias in healthcare artificial intelligence applications: A Bowtie analysis |
| Publisher |
ScienceDirect |
| Author(s) |
Not fully extracted |
| Date |
2024 |
| URL |
https://www.sciencedirect.com/science/article/pii/S2666449624000410 |
| Type |
Research paper (peer-reviewed) |
Summary
| Dimension |
Rating |
| Reliability |
Medium-High |
| Relevance |
Medium-High |
| Bias: Missing data |
Low risk |
| 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 |
Peer-reviewed journal article using established Bowtie analysis methodology for risk assessment. |
| Relevance |
Directly examines automation bias risks in healthcare AI systems, including design-phase interventions. |
| Bias flags |
Low risk. Proposes both design-phase and post-deployment interventions. |
| Evidence ID |
Summary |
| SRC04-E01 |
Bowtie analysis identifies automation bias as systemic healthcare AI risk requiring design-phase intervention |