Improving the diagnostic process is a quality and safety priority.1 With the digitization of health records and rapid expansion of health data, the cognitive demand on the diagnostician has increased. The use of artificial intelligence (AI) to assist human cognition has the potential to reduce this demand and associated diagnostic errors. However, current AI tools have not realized this potential, due in part to the long-standing focus of these tools on predicting final diagnostic labels instead of helping clinicians navigate the dynamic refinement process of diagnosis. This Viewpoint highlights the importance of shifting the role of diagnostic AI from predicting labels to “wayfinding” (interpreting context and providing cues that guide the diagnostician).
There are many examples of AI solutions for well-characterized, stand-alone diagnostic questions. AI-enabled image analysis can predict diabetic retinopathy or whether a chest radiograph shows a pneumothorax.2,3 Differential diagnosis generators4 process data on signs and symptoms to create a prioritized list of diagnoses. These AI tools aim to predict a label that represents the end point of the diagnostic process.
In doing so, these tools overlook the upstream work of navigating the decision nodes along the diagnostic pathway, and therefore are unlikely to garner the trust of clinicians.5 The critical challenges clinicians encounter when making a diagnosis are synthesizing complex patient data and determining the best next steps. A new generation of AI is needed that considers the dynamism of the diagnostic process and answers the questions of where are the clinician and patient on the diagnostic pathway and what should be done next.