A look at how experts in the medical field are approaching some of the most pressing ethical challenges presented by AI. paid intel
he ethical guidelines laid out in the Hippocratic Oath nearly 2,500 years ago are about to collide with 21AI promises to be a boon to medical practice, improving diagnoses, personalizing treatment, and spotting future public-health threats. By 2024, experts predict that healthcare AI will be a nearly $20 billion market, with tools that transcribe medical records, assist surgery, and investigate insurance claims for fraud.
In testing the system, the team found that the most accurate predictor of a patient’s length of stay was his or her ZIP code. This immediately raised red flags for the team: ZIP codes, they knew, were strongly correlated with a patient’s race and socioeconomic status. Relying on them would disproportionately affect African-Americans from Chicago’s poorest neighborhoods, who tended to stay in the hospital longer.
“How do we, as machine-learning practitioners, handle societal problems while trying to make accurate, actionable models?”This case points to a weak spot in AI-based healthcare tools: The data used by the algorithms can often reflect existing racial or gender health disparities. Left unaddressed, this could contribute to perpetuating bias and lock in existing inequalities in healthcare.
“It’s often very difficult to understand what the ‘thought’ process of the algorithm is,” says Eleonore Pauwels, a research fellow in emerging cybertechnologies at the United Nations University’s Centre for Policy Research.Eleonore Pauwelshighlights this problem. In the study, researchers compared how well different AI models predicted mortality risk for pneumonia patients. Those at greater risk would be admitted to the hospital, while lower-risk patients could be diverted to outpatient care.
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