The Blue & Gray Press

The University of Mary Washington Student Newspaper

AI is too biased to be trusted in the medical field

3 min read

Artificial intelligence is not developed enough to be trusted in the medical field.

LEIGH HATTON

Senior Writer

It has been 65 years since the term artificial intelligence (AI) was first coined, but it still has a long way to go before this technology belongs in medical work. As it exists, artificial intelligence does not meet the intersectional or ethical standards that are needed for the sensitive and specific realities of health care.

AI, in general, refers to algorithms that can interpret given data, learn from this data and then execute tasks and continue to adapt based on new data. However, this is an issue due to the data that is available to algorithms. As Dr. Greta Bauer wrote, “because AI applications ‘learn’ from data produced in biased societies, they are shaped by both information biases and societal biases. This reproduction and intensification of societal biases is therefore unsurprising.” 

One well-known example is Amazon’s AI hiring tool, which reviewed candidate resumes and displayed a clear bias against women, especially for technical and engineering jobs. Another example is Microsoft’s Twitter bot named Tay, which posted racist, antisemitic and misogynistic remarks less than 24 hours after it was made public.

If these are the types of AI that are created by some of the most expansive companies in the world, it is not surprising that many health and medical workers have expressed concern about introducing similar technologies in their fields.

Specifically, increased intersectionality is absolutely necessary before AI is used in health fields. Intersectionality refers to heterogeneity of race, age, gender, culture and economic status, as well as the places where these concepts intersect. 

In this case, intersectionality applies to all of the patients in a health system. The data that AIs draw from must be representative of the entire population, not a bare minimum idea of diversity. This will require dedicated work from a range of health and data experts, including many who encompass the scale of social intersections. Without representative data, AIs will perpetuate biases by showing preferences for or against certain groups—similar to Amazon’s tool—or by categorizing them incorrectly in systems, as Dr. Joy Buolamwini’s research concluded. Her work involved the use of different AI to classify faces, and she found that all of them performed the most inaccurately when it came to dark-skinned female faces.

A critical and cautious approach must be taken in terms of ethics. Programmers may aim to create fair AIs, but for medical work, the standard definition of fairness is not just. Bauer explained, “while overall fairness approaches may be utilitarian, generating the least bias on average across a population … maximizing algorithmic fairness does not substitute for addressing historical injustice or protecting the most marginalized.” This type of fairness that may be looked for in AI does not ensure that many groups are being treated justly and equitably.

On the other hand, there are some who advocate for the continued and even increased use of AI in medical work. According to Dr. Jason Morgenstern, “AI applications have matched or outperform physicians in various domains.” Based on this evidence, hospitals and universities have integrated AI into varying daily work tasks, including predictive analytics, identifying risk factors and analyzing records.

Yet, Morgenstern breaks down this argument when he points out that “while considerable attention has been paid to AI in healthcare, there has been less attention on its impact in health.” That is to say, research has almost entirely been focused on how AI can be used in the field, not on how AI affects the patients and workers. 

Therefore, there are potentially endless uses in medical fields where AI technology could be a helpful advancement. However, Dr. Lisa Bowleg concludes that AIs will not be able to provide solutions for any of the problems in the medical field without research on their exact impact in addition to “a radical reimagining of intersectional health equity.”

With the current state of this technology, it would be more harmful than helpful for it to be implemented on any level. Until there is solid evidence that artificial intelligence in healthcare has an unbiased and beneficial impact, it should not be used in this context.

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