Healthcare as an industry is currently undergoing an AI transformation, and many are looking forward to the advantages it will bring. However, in the process of trying to avail the many benefits, it is important to be cautious of any incorporation that can create negative results. Particularly, those stemming from AI biases in healthcare.
The increasing speed at which AI systems are impacting medical decision making reflects the necessity of understanding some potential biases.
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Discover the different AI biases in healthcare that could lead to poor patient care (if not addressed).
They can arise from multiple sources which create inequality in healthcare and perpetuate existing disparities.
Bias Stemming from Data
AI algorithms are trained by using past data. So, appropriate data selection is a necessity before feeding the model. Any inequality present in the data will be displayed in the results.
Additionally, if a certain dataset lacks adequate representation, then the same bias will be adopted by the AI system.
Misdiagnoses and incorrect treatment suggestions are a likely possibility for the underrepresented group.
Algorithm and Its Design Bias
At the end of the stay, a machine is only as good as its creator. Similarly, AI algorithms can introduce biases as well.
It could be due to (incorrect) prioritization of a variable or determining outcome based on their design.
For example, a design flaw could make the algorithm support a treatment option that is effective for one group but not for the other.
Confirmation Bias of Healthcare Professionals
Healthcare professionals can (inadvertently) display confirmation bias when considering the recommendations of the AI model.
Let’s say the physician is experienced in treating a specific condition for a specific demographic, they might lean towards AI inputs that confirm that belief.
Not Accounting for Socioeconomic Factors
Socioeconomic factors play a major role in determining how healthcare is provided. Family income can affect the development of a child. This is something understood by healthcare professionals, but an algorithm might not consider it.
Similarly, other social determinants, such as education or general access to health care affect patient treatment.
Closing Thoughts
Healthcare is a critical industry, and any technological addition must be properly tested before full incorporation. AI biases in healthcare can significantly restrict the benefits of the disruptive technology.
Therefore, it is important to recognize what they are and how they may arise to appropriately address them.