The Paradox of Dissonant Predictions: A Central Dilemma of Physician-Algorithm Interaction

Clinical algorithms have become highly sophisticated and can outperform physicians in many scenarios. Despite the promise of these tools, uptake and appropriate use is variable. One reason may be because superhuman algorithm performance requires it to come in conflict with a physician's judgment. The paradox is that physicians do not know how to effectively incorporate information that conflicts with their existing beliefs or expectations, even if it may steer them toward the right answer. This confusion around how to confront conflicting algorithmic output is a central obstacle to effective physician-algorithm collaboration. Simply providing accurate recommendations is insufficient; algorithms must effectively change physicians' minds when they are incorrect. This requires rethinking algorithmic design, physician training, and physician-algorithm collaborative models. Rethinking the human-algorithm interface through structured interaction protocols may offer a promising approach. Ultimately, optimizing physician-algorithm synergy likely requires addressing the dissonance generated by a strong model to promote effective integration of algorithmic insights into clinical decision-making.