Is AI Ready for Sports Medicine and Injury Recovery?

Athletes and teams want faster injury decisions, and AI promises exactly that kind of certainty. The problem is not whether these systems can find patterns in a dataset. It is whether they still work when the athlete is in front of the clinician, the coach wants a quicker answer, and the consequences of a wrong return-to-sport call land on real people rather than on a validation table.

The evidence map is crowded, not mature

• Lindskog et al. screened 8,677 records and included 97 studies, with 86.6% of them published between 2020 and 2026. Orthopaedics accounted for 70.1% of the literature and neurology for 18.6%, while 57.7% of studies focused on prediction or estimation tasks. Random Forest appeared in 29 studies, SVM in 27, and Decision Trees in 20. [1]
• Only 4 of the 97 studies used external datasets for validation, and only 1 attempted prospective interventional use. That one prospective example, Dandrieux et al., ran into 37% adherence, which is exactly the sort of deployment failure that no internal AUC can rescue. [1]
• Even the more granular monitoring data only moved the needle modestly: models trained on screening data alone reached an AUC of 0.73, and adding RPE, GPS, and wellness data increased it to 0.77. [1]

That is the central pattern for AI in sports medicine and injury recovery: rapid publication growth, familiar model families, and performance that looks encouraging inside the study but still leaves the clinician without proof that the tool will improve the next decision. The field is active, but the evidence base is still thin where it matters most.

Better scores do not settle the question

Leckey et al. reinforce the same caution from a different angle. In their scoping review, tree-based solutions performed best in about 60% of studies, and XGBoost was the strongest method in every paper where it was tested. But the more uncomfortable finding is the one clinicians should not ignore: logistic regression outperformed machine learning in 4 of 12 direct comparisons. That is not a victory for nostalgia; it is a reminder that model complexity does not automatically translate into better clinical utility. [2]

For injury prediction, this matters because the temptation is to treat a higher AUC as a finished product. In practice, the question is whether the model survives a different squad, a different season, a different staff member entering the data, and a different threshold for action. Leckey et al. make the point plainly enough: statistical performance can look strong while the real-world decision remains uncertain. [2]

Each workflow needs its own standard

Prediction deserves the most scrutiny because it has the largest evidence base and the most seductive claims. Diagnosis is different: orthopaedic AI medical devices have clearer regulatory pathways, but clearance is not the same thing as robust proof of clinical benefit. Rehabilitation and return-to-sport are more promising as support functions than as autonomous decision engines, because the likely value there is in structuring data, highlighting risk, and helping clinicians compare a plan against evidence-based guardrails rather than replacing judgment. [4]

That is also where the Dandrieux example keeps its force. A system can deliver feedback, but if adherence settles at 37%, the limiting factor is no longer the algorithm's ability to score a risk. It is whether the athlete, clinician, and performance staff actually use the information well enough for it to change the next training decision. [1]

Regulatory clearance is not clinical maturity

Lee et al. provide the clearest view of how quickly the device market is moving. They identified 70 orthopaedic AIMDs cleared through February 2025, with clearances rising 5.5-fold from 3.0 per year in 2017-2019 to 16.6 per year in 2022-2024. Yet only 8.6% had been validated through prospective clinical trials, and 22.8% had no clinical testing at all. [3]

The absence of recalls is reassuring only in a limited way, because a short market window can look safer than it really is. Lee et al. note that no orthopaedic AIMD had been recalled at the time of their analysis, but that should not be mistaken for settled safety or proven effectiveness. Clearance tells you a device entered the market under a regulatory pathway; it does not tell you that it improves return-to-play decisions or reduces harm in everyday sports medicine use. [3]

The most defensible role for AI right now is narrower than the marketing often suggests. In rehabilitation and return-to-sport support, it may help with objective assessment, risk prediction, personalized planning, data integration, and guideline support, but that framework describes what AI could contribute, not what has already been proven to change outcomes. Used under supervision, it can be a useful second set of eyes. Used as an autonomous authority, it is not ready yet. [4]

So the current readiness position is clear enough for a cautious clinician: AI in sports medicine and injury recovery is still early-stage, with promising internal metrics but too little external validation, too little prospective testing, and too little real-world workflow evidence to trust it for autonomous clinical decision-making. Some tools may already be useful as supervised adjuncts, but only when their validation status, population limits, and workflow burden are explicit.

References

1. “Artificial intelligence and machine learning in sports medicine: mapping clinical tasks and assessing clinical maturity — a scoping review” — BMC Sports Medicine, 2026
2. “Machine learning approaches to injury risk prediction in sport: a scoping review with evidence synthesis” — BJSM, 2026
3. “FDA-Cleared Artificial Intelligence Medical Devices in Orthopaedic Surgery” — 2026
4. “Bridging the Gap: Artificial Intelligence in Sports Medicine and Musculoskeletal Rehabilitation” — SEMS-Journal