All in the Name of Artificial Intelligence: A Commentary on Linardon (2025)

Artificial Intelligence (AI) is being rapidly integrated into healthcare, but Linardon et al. reveal a troubling gap between what AI actually is, its capabilities, and the patients' and clinicians' perceptions of it-equating AI solely with large language models. In this commentary, we discuss concerns over AI's black-box nature, its potential to perpetuate existing biases, and the blind trust some people place in its decisions, despite evidence that quantitative models outperform large language models in clinical decision-making tasks. While AI holds promise in eating disorder care, its integration requires a nuanced understanding of its capabilities, limitations, and the critical distinction between AI for administrative automation, clinical decision-making, and direct-to-patient AI. Poorly designed AI alerts risk becoming just another ignorable nuisance, while patient-facing AI could either empower individuals or drown them in notifications and misinformation. Before we anoint AI as healthcare's savior, it requires validation for accuracy, reliability, fairness, real-world usability, and its actual measurable impact on clinicians and patients. The real challenge is not whether AI will change healthcare but ensuring it does so responsibly-by integrating it thoughtfully into workflows, such that it is supporting rather than replacing clinical judgment, and maintaining accountability when things go wrong.