However, physician skepticism over how AI solutions come to their conclusions may hinder clinical implementation. One of the main barriers to adoption of diagnostic AI is that algorithms are often referred to as "": They're trained on millions of data points and rely on many complex nodes. As such, users may not have insight into how an algorithm came to a conclusion, which could breed skepticism among doctors.
Physicians may be less willing to trust and adopt AI systems in clinical settings if it's unclear what information led the model to reach a decision or diagnosis. With new diagnostic AI tools hitting the market, developers can win over worried doctors by being as transparent as possible about the data algorithms were trained on and the sample size of trials.
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