Is AI Ready to Become a Heuristic in Clinical Decision-Making? A Comparative Study of Two Regions (Preprint)

Abstract

<sec> <title>BACKGROUND</title> In the age of AI and its capacity for superior computational power and speed, AI cues have the potential of becoming a heuristic in clinical judgment with implications for shared decision-making, overreliance on technology, and generalization to unique clinical settings across the globe. Our first research objective is to explore this proposition by asking clinicians to perform an inferencing task with the help of only traditional clinical cues and with the addition of an AI cue. Our second research goal is to detect effort reduction in the presence of an AI cue. We recruited participants from two regions, differentiated by AI adoption: US as a mature market and India as an emerging market and conducted a between-subjects study via a field experiment. We found evidence of AI presenting as a heuristic, with traditional information used as validation in the mature market; we also found evidence of effort reduction in both markets. Furthermore, engaging two regions uncovered variances in the assessment of the AI cue, enabling us to propose factors contributing to these differences and illuminate them further in interviews with practitioners from both regions. As AI tools get integrated in clinical routines alongside traditional information, understanding their application to clinical judgment is critical for practitioners and others with roles in design, implementation, and training of such tools. </sec> <sec> <title>OBJECTIVE</title> We explore the proposition of AI presenting as a heuristic by asking clinicians to perform an inferencing task with the help of only traditional clinical cues and with the addition of an AI cue. Engaging two regions enables us to uncover potential differences in the evaluation of the AI cue in a mature AI market and an emerging AI market and answer the following research questions: 1. Will the AI cue be interpreted as the Take the Best heuristic11 when it is compared to traditional cues in either market? 2. Will the addition of the AI cue lead to fast and frugal decision-making, i.e., effort reduction in either market? </sec> <sec> <title>METHODS</title> A between-subjects study via a field experiment. </sec> <sec> <title>RESULTS</title> Our findings indicate that AI can be used as a heuristic but with the back-up of a traditional cue and limited to a mature market. We also find significant differences between the regions in the assessment of the AI cue and enrich these findings by perspectives of clinicians from both markets. Clinicians in the mature, resource-rich environment call for increased transparency of AI and evidence of well-researched methodology, while practitioners in the emerging market raise concerns about generalization of AI built on non-native data to their unique settings. We propose further exploration of these market differences as, with ongoing incorporation of AI-based tools into clinical practices across the globe, there is a need of clear understanding of unique success factors to adoption and use of AI alongside traditional information. </sec> <sec> <title>CONCLUSIONS</title> What is already known about the subject • Heuristics is a common phenomenon in clinical decision-making, usually presenting as effort reduction in lieu of using all the available information. • AI’s computational power, speed, and replicability position it well to become a heuristic in clinical decision-making. • AI-based tools, despite the potential to augment human judgment, still fall short of full-scale integration into clinical routines. • At the same time, there are rising concerns about overreliance on technology in medical settings. What this research study adds • The study tests if an AI cue will be treated as the Take the Best heuristic in clinical inferencing in two regions, US as an AI mature market and India as an AI emerging market. • The study demonstrates the results of testing for effort reduction with the addition of the AI cue to traditional information. • The study proposes factors contributing to the differences in interpretation of the AI cue between the two regions. </sec> <sec> <title>CLINICALTRIAL</title> N/A </sec>

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