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In this episode of the Sports Medicine Broadcast, host Chase Morales sits down with Dr. Ashlyne Elliott and Dr. Layci Harrison following their presentation at the Sports Medicine Update. Together, they explore the rapid evolution of artificial intelligence across athletic training, health professions education, and daily clinical practice.
As athletic trainers face increasing administrative workloads and packed daily schedules, AI tools offer significant efficiency gains. However, utilizing these tools safely requires strong human oversight, ongoing critical appraisal, and diligent patient privacy standards.
1. The Administrative Assistant: Efficiency vs. Oversight
Athletic trainers are frequently strapped for time, balancing direct patient care with heavy documentation and administrative responsibilities. Dr. Ashlyne Elliott highlights that AI can function as an efficient assistant for creating initial drafts of emails, policy documentation, and routine administrative tasks.
However, AI outputs must always be supervised and verified. Failing to fact-check generated material risks introducing errors into clinical workflows, which can negatively impact patient care or create legal liabilities.“It can help with initial drafts, emails, or drafting policies. But if we’re not fact-checking and double-checking our work, we could get into gray areas that impact patient care negatively or leave us up to legal scrutiny.” — Dr. Ashlyne Elliott
2. Ambient AI Scribes & Clinical Risks
Automated ambient recording tools—designed to listen during clinical encounters and construct SOAP notes automatically—are gaining traction in healthcare environments. While these scribes can streamline documentation for clinicians seeing high patient volumes, uncritical reliance introduces severe risks.
Dr. Elliott referenced a scene from the medical television drama The Pit, where a busy provider relied on an automated scribe without reviewing the transcript. A transcription error regarding patient medications altered the overall plan of care. In real-world healthcare settings, even a small margin of AI error can lead to adverse drug interactions or life-threatening oversight.
3. Data Privacy and Predictive Bias
When evaluating AI applications and athlete management platforms, athletic trainers must scrutinize data handling and model development:
- Protected Health Information (PHI): Consumer-facing AI tools and free applications often store submitted data as public training material. Clinicians must review store disclosures and privacy agreements to protect sensitive patient records and maintain HIPAA compliance.
- Demographic Representation in Machine Learning: Predictive algorithms are shaped by the data used to train them. An AI model built primarily on data from Division I male collegiate athletes will not yield accurate predictive insights when applied to high school populations.
Dr. Layci Harrison urges clinicians to ask software vendors explicit questions regarding how their predictive models were developed before incorporating recommendations into practice.
4. Demystifying the “Black Box” Theory
Traditional statistical analysis (such as linear regression performed in SPSS) allows researchers to trace and audit every mathematical calculation. In contrast, advanced machine learning operates within a “Black Box”—processing complex variables in ways human reasoning cannot directly inspect.
Dr. Harrison stresses that regardless of how sophisticated an algorithm is, clinicians remain legally and ethically accountable for their treatment decisions.
- Clinical Responsibility: If an AI tool recommends an ACL rehabilitation protocol and a patient suffers an injury due to premature loading, citing “the AI generated the plan” does not offer legal protection. Clinicians must be able to independently justify every clinical decision.
5. AI in Education: Cultivating Critical Thinking
Dr. Harrison and Dr. Elliott emphasize that educators must guide students in using technology safely rather than attempting to restrict it. Students already use AI to study, design practice scenarios, and seek immediate feedback.
The priority for educators and preceptors is ensuring students maintain core clinical reasoning skills. While AI can accelerate workflow, clinicians must understand foundational principles to evaluate whether an AI output is accurate and appropriate for patient care.
Show Credits
| Role | Contributor |
|---|---|
| Host | Chase Morales |
| Audio Engineer | Shelby Gaytan |
| Equipment Provided By | Russell Sadberry |
| Recording Support | Bob Marley and the Memorial Hermann Rockets Sports Medicine Institute team |