Title |
Should We Rely on AI to Help Avoid Bias in Patient Selection for Major Surgery?
|
---|---|
Published in |
The AMA Journal of Ethic, August 2022
|
DOI | 10.1001/amajethics.2022.773 |
Pubmed ID | |
Authors |
Charles E Binkley, David S Kemp, Brandi Braud Scully |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 6 | 40% |
Switzerland | 2 | 13% |
Brazil | 1 | 7% |
Netherlands | 1 | 7% |
Unknown | 5 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 9 | 60% |
Scientists | 3 | 20% |
Science communicators (journalists, bloggers, editors) | 2 | 13% |
Practitioners (doctors, other healthcare professionals) | 1 | 7% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 11 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Bachelor | 2 | 18% |
Professor > Associate Professor | 2 | 18% |
Student > Master | 1 | 9% |
Student > Ph. D. Student | 1 | 9% |
Unknown | 5 | 45% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 2 | 18% |
Biochemistry, Genetics and Molecular Biology | 1 | 9% |
Social Sciences | 1 | 9% |
Neuroscience | 1 | 9% |
Medicine and Dentistry | 1 | 9% |
Other | 0 | 0% |
Unknown | 5 | 45% |