Artificial intelligence and machine learning in sports medicine: mapping clinical tasks and assessing clinical maturity - a scoping review
Jakob Lindskog, Kristian Heder Ternell, Yinan Yu, Ida Lindman, Kristian Samuelsson, Eric Hamrin Senorski · 2026
Read Artificial intelligence and machine learning in sports medicine: mapping clinical tasks and assessing clinical maturity - a scoping review by Jakob Lindskog, Kristian Heder Ternell, Yinan Yu, Ida Lindman, Kristian Samuelsson, Eric Hamrin Senorski (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13263937.txt.
Description
Jakob Lindskog and co-authors present a 2026 scoping review of artificial intelligence and machine learning in sports medicine. They mapped which clinical tasks the tools target and judged how close each is to real clinical use. Their February 2026 search across three databases screened 8,677 papers and included ninety-seven.
Most work sat in orthopedics, about seventy percent, and neurology. Prediction and estimation models dominated, covering injury risk, image diagnosis, and recovery forecasts. Reported accuracy was often high, but most studies used old data and tested only within their own sample. Calibration was rarely reported, and only one study tried an actual prevention intervention. The authors advise treating these tools as support, not decision makers.
Catalog record
| CREATOR | Jakob Lindskog, Kristian Heder Ternell, Yinan Yu, Ida Lindman, Kristian Samuelsson, Eric Hamrin Senorski |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 74843 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |