Next-Generation Artificial Intelligence for Diagnosis From Predicting Diagnostic Labels to “Wayfinding”
Julia Adler-Milstein et al.
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).
Starting With the End
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.
Diagnosis as a Wayfinding Process
Wayfinding is the process of determining a current position and navigating a route between an origin and a destination. The basic elements of wayfinding are orientation (what is the current location?), path selection (which way to go?), route monitoring (is this the right track?), and destination recognition (is this the end point?).6 Wayfinding also refers to the environmental cues that orient people and help them choose a path within complex spaces such as airports and hospitals (eg, hallway curvature and color that subconsciously guide a person to their final destination). Effective wayfinding reduces cognitive load in navigating a complex journey. It operates in the background, with the individual unaware of why or how.
From the clinician’s perspective, the diagnostic journey begins with assessment of the patient’s signs and symptoms, which in turn triggers information gathering (eg, asking questions or reviewing the medical record). As the clinician integrates and interprets accumulating data, next steps are planned, often with a general direction or potential destinations (diagnostic hypotheses) in mind. As next steps are pursued, new information is generated (eg, test result or physical examination), which prompts another round of the data acquisition-integration cycle. As cycles repeat, uncertainty is reduced, and the destination becomes clearer. The diagnostic journey ends when the clinician and patient have reduced uncertainty sufficiently to shift their focus to management decisions. Conceptualizing diagnosis as a dynamic refinement process (Figure) moves the emphasis from the destination (diagnostic label) to the journey, including information-intensive activities in the electronic health record.
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