Generative AI and large language models show promise as supervised decision-support tools in sports medicine, but current evidence does not support unsupervised clinical use.
A scoping review of 32 studies found that LLM accuracy varies substantially across sports medicine domains, with content validity ratios for sleep recommendations ranging from 0.33 (GPT-3.5) to 0.67 (GPT-4). Hallucination risk was rated critical for general-purpose chatbots but reduced in retrieval-augmented systems. The review concludes that while GenAI has potential, it is not yet safe for unsupervised use.
This article is automatically generated from the structured evidence profile behind the claim above. Scores reflect the quality and quantity of available research, not clinical advice.
Generative AI and large language models show promise as supervised decision-support tools in sports medicine, but current evidence does not support unsupervised clinical use. The current body of evidence comprises 1 study. EvidenceHub rates the overall confidence at 32/100 (low).
The Claim
Generative AI and large language models show promise as supervised decision-support tools in sports medicine, but current evidence does not support unsupervised clinical use.
This conclusion is most relevant to: Sports medicine practitioners, athletes, and coaches (studies included in scoping review).
What the Research Shows
The conclusion draws on 1 linked study. Highlights from the cited literature:
- ▸Generative artificial intelligence and large language models in sports medicine: a scoping review of applications, accuracy, and ethical implications. (Frontiers in public health, 2026) —
How It Works
The proposed biological pathway:
- ▸LLMs generate responses based on training data
- ▸Responses are evaluated for content validity against expert guidelines
- ▸Hallucination risk assessed for different system types
- ▸General-purpose chatbots show critical hallucination risk, retrieval-augmented systems reduce risk
- ▸Result: Unsupervised clinical use not supported
Who Might Benefit
Evidence fit by population:
- ▸Sports medicine practitioners, athletes, and coaches (studies included in scoping review)
Recommended Dose
N/A
Limitations & Caveats
Important context when interpreting this evidence:
- ▸Scoping review, not a systematic review with meta-analysis
- ▸Only 32 studies included, with limited validation studies
- ▸No sport-specific validation datasets available
- ▸Rapidly evolving technology may make findings outdated
Frequently Asked Questions
What is the accuracy of GPT-4 for sleep recommendations in sports medicine?▼
GPT-4 achieved a content validity ratio of 0.67 for sleep recommendations, which is considered acceptable, while GPT-3.5 had a lower CVR of 0.33.
Are large language models safe for unsupervised use in sports medicine?▼
No, the review found that current evidence does not support unsupervised clinical use due to hallucination risks and lack of sport-specific validation.
What are the main applications of GenAI in sports medicine?▼
Applications include training prescription, nutrition, rehabilitation, mental health support, injury prevention, clinical decision support, and academic writing.
What is the hallucination risk of general-purpose chatbots compared to retrieval-augmented systems?▼
General-purpose chatbots have a critical hallucination risk, while retrieval-augmented systems substantially reduce this risk.
References
- 1.Dergaa I, Dergaa MA, Razzak M, Ceylan Hİ, Stefanica V, Muntean RI, Guelmami N. “Generative artificial intelligence and large language models in sports medicine: a scoping review of applications, accuracy, and ethical implications..” Frontiers in public health, 2026. PMID: 42591395 DOI: 10.3389/fpubh.2026.1843535