Author Reply: Artificial Intelligence in Sports Medicine

Recommended Requirements and Essential Elements for Proper Reporting of the Use of Artificial Intelligence Machine Learning Tools in Biomedical Research and Scientific Publications

Essential elements required for proper use of artificial intelligence machine learning tools in biomedical research and scientific publications include (1) explanation justifying why a machine learning approach contributes to the purpose of the study; (2) description of the adequacy of the data (input) to produce the desired results (output); (3) details of the algorithmic (i.e., computational) approach including methods for organizing the data (preprocessing); the machine learning computational algorithm(s) assessed; on what data the models were trained; the presence of bias and efforts to mitigate these effects; and the methods for quantifying the variables (features) most influential in determining the results (e.g., Shapley values); (4) description of methods, and reporting of results, quantitating performance in terms of both model accuracy and model calibration (level of confidence in the model's predictions); (5) availability of the programming code (including a link to the code when available-ideally, the code should be available); (6) discussion of model internal validation (results applicable and sensitive to the population investigated and data on which the model was trained) and external validation (were the results investigated as to whether they are generalizable to different populations? If not, consideration of this limitation and discussion of plans for external validation, i.e., next steps). As biomedical research submissions using artificial intelligence technology increase, these requirements could facilitate purposeful use and comprehensive methodological reporting.

Artificial Intelligence, Machine Learning, and Medicine: A Little Background Goes a Long Way Toward Understanding

Artificial intelligence (AI) and machine learning refer to computers built and programed by humans to perform tasks according to our design. This is vital to keep in mind as we try to understand the application of AI to medicine. AI is a tool with strengths and limitations. The primary strength of AI is that it allows us to assimilate and process unlimited quantities of health care data. The limits of AI include the inability of machines to adapt in a human sense, the reality that machines lack human insight (i.e., clinical judgment or common sense), and the limitation that machine-learning algorithms are subject to the data on which they are trained. Thus, we must adapt to AI and machine learning. Next, because machine learning is a type of AI in which computers are programmed to improve the algorithms under which they function over time, we require insight to achieve an element of explainability about the key data underlining a particular machine-learning prediction. Finally, machine-learning algorithms require validation before they can be applied to data sets different from the data on which they were trained. As computers have become faster and more powerful, and as the availability of digital data has become immense, we can program our machines to analyze data and recognize patterns that, in sum, are a primary basis of medical diagnosis and treatment.