Left Behind: AI’s Uneven March in Head and Neck Surgery

A Widening Gap in Medical AI

Artificial intelligence is rapidly changing healthcare, but some specialties are being left out of the revolution. A new scoping review published in Nature examines the global landscape of deep learning in otolaryngology–head and neck surgery (OHNS), revealing a stark discrepancy between the field and other areas of medicine. While radiology and dermatology have embraced machine learning for diagnostics and planning, OHNS has seen relatively sparse application. Over 90% of the reviewed studies originated from high-income countries, with a significant portion focused only on imaging tasks. This lack of diversity in problem-solving, geographic representation, and clinical integration points to structural and systemic barriers that may be stunting the field’s future potential in AI.

Systemic Bias and Structural Setbacks

The review also uncovers deep-rooted challenges in how deep learning research is conducted and shared within OHNS. Most data used in these studies are not publicly available, limiting reproducibility and collaboration. Furthermore, only a small fraction of projects demonstrate real-world clinical integration—a critical hurdle for moving AI from promising concept to practice. Researchers emphasize that to close the AI chasm, the specialty must prioritize equitable data sharing, develop robust validation across diverse populations, and expand use cases beyond image classification. Without such steps, the potential for AI-enhanced care in diagnosing and treating conditions of the head and neck may remain an unrealized frontier.