AI Resilience Report forAthletic Trainers

Right now, AI is mostly augmenting athletic trainers (ATs), not replacing them. The National Athletic Trainers' Association is actively rolling out continuing-education courses that teach ATs how to use large language models in clinical practice while staying compliant with HIPAA and FERPA ^[1], which signals that AI is being treated as a professional tool, not a threat. In practice, ATs are using ChatGPT-style tools to design rehab programs, brainstorm injury-prevention ideas, organize scheduling, and speed up administrative paperwork ^[2] — exactly the record-keeping and reporting tasks flagged as highly automatable.

On the performance side, colleges like UF are using AI to sift through injury data and build predictive models that flag athletes at higher risk of injury ^[3], and a 2026 review in Frontiers in AI shows AI-powered wearables now support workload regulation, injury-risk estimation, and return-to-play decisions inside a "human-in-the-loop" framework ^[4]. Hands-on work — massage, taping, traveling with teams, and going to the ER with injured athletes — remains firmly human.

Adoption is moving quickly for documentation and analytics because commercial tools are cheap, widely available, and save real time. But it's moving more slowly at the bedside. State associations warn that AI raises serious ethical questions around patient privacy, algorithm bias, and responsible integration into athlete care ^[5], and even AT-authored guides caution that models can generate inaccurate information and must be double-checked before being trusted with a diagnosis ^[2].

The good news for students eyeing this career: the human parts of athletic training — trust, touch, split-second sideline judgment — are the hardest to automate and the most valued.