AI Bias in Healthcare: Lives at Risk
Artificial intelligence (AI) has the potential to revolutionize healthcare, but there’s a dark side to this technological advancement. The data used to train medical AI models often reflects biases and inequities in the U.S. healthcare system, leading to potentially deadly consequences.
Biased Data = Biased AI
AI models are created using algorithms that learn from large data sets. However, the biases present in healthcare practices are embedded in the data itself. This means that certain populations, such as people of color, underrepresented communities, and those with specific health plan coverage, may be ignored, overlooked, or misdiagnosed.
Deadly Consequences
Studies have shown the dire consequences of AI bias in healthcare. For example, a 2019 study found that an AI-based prediction algorithm used in hospitals disproportionately provided care to white patients over Black patients. The algorithm was trained on flawed data that reflected historic disparities in access to healthcare. As a result, Black patients were significantly undercounted for additional care they needed.
In another case, an algorithm used to determine in-home aid for severely disabled residents had multiple biases, leading to disruptions in care and even hospitalizations. The impact of flawed algorithms can be deadly, as shown by an AI tool used to detect sepsis. The tool failed to predict sepsis in 67% of patients and generated false alerts for thousands of others.
The Need for Human Oversight
To prevent these life-threatening consequences, the medical community must step up and demand human oversight at every stage of AI development and deployment. A diverse range of professionals, from data scientists to doctors, should be involved in the process to ensure ethical standards are met and biases are minimized.
Regulation is another crucial aspect. Just as drug trials require FDA oversight, AI tools in healthcare should undergo independent audits and evaluations. However, the FDA currently lacks clear pathways and funding to regulate AI-based tools effectively. This leaves developers to address bias on their own, assuming they even recognize the need to do so.
Building a Better Future
AI has immense potential in healthcare, but it must be developed and deployed with care. The complexity of medicine, coupled with biases in training data, requires a thoughtful approach to AI model design. We need to ensure that AI functions properly in healthcare, putting patient safety and well-being above all else.
As a physician, I took an oath to “first, do no harm,” and now, as an executive and innovator, I strive to uphold that oath. By building an infrastructure that addresses AI bias in healthcare, we can transform the industry for the benefit of all.
What do you think about AI bias in healthcare? Have you had any experiences where biased AI had a negative impact? Share your thoughts and experiences in the comments below.
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