"The algorithm is just a mathematical equation and is not inherently bias, but what you need to understand on the provider or vendor sides is the dataset you are working with," Bogdan said. "What are the biases inherent in that dataset? You need to understand the statistics of your data, such as what percentage of your data is male/female, different ethnicities, and all the different characteristics of your data so you can understand what biases are inherent in that data. Then you can work to address those biases."
As long as you understand the bias, he said programers can solve for them mathematically, discount these issues in the algorithm, or augment the data. Bogdan said you need to fix any biases detected because anything that is overlaid on that algorithm is going to be amplified.
"We are seeing this not only in healthcare but a ton of other industries," Bogdan said. "So addressing the data quality is really important."
For example, in human resources AI, Bogdan said it has been found some algorithm-based screening processes for job candidates were unintentionally bias in favor of whites because of the datasets used to train the algorithm were mostly white job applicants.
In healthcare, he explained it is important to validate if an AI algorithm is appropriate for a hospital or healthcare system's patient population.
"AI is not a one-size fits all solution in terms of the algorithm, and it is not just a one-time implementation. You need to have a life-cycle management plan in place for these algorithms, because as your data changes over time, you need to re-evaluate the outcome of the algorithm and adjust the algorithm as necessary," Bogdan said.
This is part of a 5-part series of interviews with Bogdan on various aspects of AI in healthcare. Here are the other videos in the series:
VIDEO: 9 key areas where AI is being implemented in healthcare
VIDEO: How hospital IT teams should manage implementation of AI algorithms
VIDEO: AI can help prevent clinician burnout
VIDEO: Use of AI to address health equity and health consumerization
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