Editorial Commentary: Machine Learning and Artificial Intelligence Are Tools Requiring Physician and Patient Input When Screening Patients at Risk for Extended, Postoperative Opioid Use

Artificial Intelligence and Machine Learning May Resolve Health Care Information Overload

Biomedical information doubles almost every 2 months, and this very rate is expected to double by 2025. The result is information overload for clinicians and researchers. Today, artificial intelligence (AI) and machine learning (ML) research contribute to the deluge of information. In addition, AI large language models, although capable of automating scientific writing, are flawed. They hallucinate (make things up), are trained primarily on non-peer-reviewed content, raise ethical and legal issues, and lack human empathy. Still, when it comes to AI including ML, we are optimistic. The technology is improving rapidly. In the future, AI will help us manage unwieldy information by processing data, determining diagnoses, recommending treatments, and predicting outcomes. In research, AI and ML similarly promise efficient data analysis and literature review and will create new content in response to our instructions. Human touch will be required, and we will disclose use of AI proactively, including rationale for its use, our data input, our level of confidence in the output, and the patients or populations to whom the output may be applied. In addition, we will ensure data quality is high and bias is minimized. Most of all, we will provide essential reasoning, clinical and research guidance, and diligent oversight. Humans will remain accountable.

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.