Artificial intelligence (AI), technology has been promising to vastly improve the healthcare industry for many years. Healthcare pundits eagerly anticipate the widespread adoption of AI, whether it is through its promise to improve access to and understanding of data, better navigation for patients, or better deciphering research and development efforts.
With the goal of increasing the quality and viability of AI within their respective fields, many companies have invested billions of dollar. These efforts have produced a lot of valuable results. Many of these have been the foundation for innovation and building in this area. However, technology has a lot of work ahead.
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The cultivation of good data sets to be used as teaching models is one of the key challenges in the advancement of AI technology in healthcare. The larger scope of AI technology makes use of vast amounts of data to identify patterns and make recommendations. These recommendations and outputs of pattern recognition are only as good the data they are provided. This can be problematic in many situations, especially when it comes to patient care data.
The potential for bias in AI-based care has been extensively discussed among key leaders. Dr. Paul Conway is the Chair of Policy and Global Affairs at the American Association of Kidney Patients. "Devices that use AI and ML technology to transform healthcare delivery will increase efficiency in key processes for the treatment of patients ..."." However, Pat Baird (Regulatory Head of Global Software Standards, Philips) stated that "To support our patients, it's important to get to know them, their medical conditions and their environment.
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This will allow us to better understand the potential confounding factors that can lead to bias An AI algorithm that provides recommendations for pain-alleviating medication would not be able to be applied to the general population if it was based only on cancer patients. The pain medications required for patients with cancer are likely to be more powerful than those needed by the general population. Therefore, the recommendations would be highly skewed. This bias is not the only type. It can lead to dangerously incorrect clinical decisions if this bias is extended across ethnicities, races and socioeconomic statuses.
This is why it is important. This is because AI can be a powerful tool in clinical settings if used correctly. In the past, I have discussed how AI can be useful in many fields, from radiology care to cancer. Although it might not be as powerful as physician-led patient care, AI may still have the potential to enhance clinical workflows.
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This technology must be truly value-add in order to make it work. Systems must provide high fidelity recommendations that take into consideration accurate and representative data. This is how physicians can truly benefit from the technology and make a positive impact on care delivery. Innovators, healthcare leaders, as well as care providers, have a lot to do with this technology in the coming years.