Evidence Based AI Development required for Healthcare Needs

There is a lot of variation in risk-mitigating AI development and deployment techniques right now. The evidence-based medicine movement arose from the documentation of unjustified diversity in clinical procedures. Evidence-based medicine is the deliberate, explicit, and thoughtful application of current best evidence in making practise decisions. According to experts, an Evidence Based AI development and deployment movement requires a similar philosophy and implementation approach.

One of these experts is Joachim Roski, a principal at Booz Allen Hamilton’s health practise. He’ll present case studies next month at HIMSS22 in his educational session “Making a Case for Evidence Based AI,” in which he’ll show how Evidence Based AI development and deployment movement practises could have prevented several high-profile AI failures. He’ll also go over several fundamental design ideas and features for developing Evidence Based AI.

He’ll also explain how healthcare institutions might use them to reduce the risk of AI. To get an early look at Roski’s session, Healthcare IT News chatted with him. Roski has more than 20 years of expertise providing digital/analytic solutions to improve care transformation, clinical quality and safety, operations, and population health improvement. We reviewed data for potentially beneficial AI solutions for use by patients, physicians, administrators, public health officials, and researchers in a 2020 report by the National Academy of Medicine.

However, if the often-hyped expectations for AI solutions are not matched by greater performance, there are warning signals of a “techlash” developing. AI solutions that consistently underestimate illness burden in non-Caucasian populations, poor performance in cancer diagnostic support, and limitations in delivering at scale are only a few examples of these issues.