Abstract
Many regard iatrogenic injuries as consequences of diagnosis or intervention actions. But inaction-not offering indicated major surgery-can also result in iatrogenic injury. This article explores some surgeons' overestimations of operative risk based on patients' race and socioeconomic status as unduly influential in their decisions about whether to perform major cancer or cardiac surgery on some patients with appropriate clinical indications. This article also considers artificial intelligence and machine learning-based clinical decision support systems that might offer more accurate, individualized risk assessment that could make patient selection processes more equitable, thereby mitigating racial and ethnic inequity in cancer and cardiac disease.
| Original language | English |
|---|---|
| Pages (from-to) | 773-780 |
| Number of pages | 8 |
| Journal | AMA journal of ethics |
| Volume | 24 |
| Issue number | 8 |
| DOIs | |
| State | Published - Aug 2022 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 3 Good Health and Well-being
ASJC Scopus subject areas
- Issues, ethics and legal aspects
- Health(social science)
- Health Policy
Fingerprint
Dive into the research topics of 'Should We Rely on AI to Help Avoid Bias in Patient Selection for Major Surgery?'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver