AI News, Women in AI: mitigating the gender bias
- On Thursday, March 28, 2019
- By Read More
Will robots replace doctors?
2017 study out of the Massachusetts General Hospital and MIT showed that an artificial intelligence (AI) system was equal or better than radiologists at reading mammograms for high risk cancer lesions needing surgery.
Advances in computing power have enabled the creation and cost-effective analysis of large datasets of payer claims, electronic health record data, medical images, genetic data, laboratory data, prescription data, clinical emails, and patient demographic information to power AI models.
According to a 2017 report by the National Academy of Medicine on health care disparities , non-whites continue to experience worse outcomes for infant mortality, obesity, heart disease, cancer, stroke, HIV/AIDS, and overall mortality.
It is even harder to address AI-generated disparities because the models are largely “black boxes” devised by the machines and inexplicable, and far harder to audit than our current human health care delivery processes.
For example, when the University of Pittsburgh Medical Center (UPMC) evaluated the risk of death from pneumonia of patients arriving in their emergency department, the AI model predicted that mortality dropped when patients were over 100 years of age or had a diagnosis of asthma.
Rather, their risk was so high that the emergency department staff gave these patients antibiotics before they were even registered into the electronic medical record, so the time stamps for the lifesaving antibiotics were inaccurate.
Without understanding clinician assumptions and their impact on data—in this case accurate timing of antibiotic administration—this kind of analysis could lead to AI-inspired protocols that harm high-risk patients.
One approach could be to create national test datasets with and without known biases to understand how adeptly models are tuned to avoid unethical care and nonsensical clinical recommendations.
These interventions could go a long way towards improving public trust in AI and perhaps, someday, enabling a patient to receive the kind of unbiased care that human doctors should have been providing all along.
- On Tuesday, May 26, 2020
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