Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders: an introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal

Abstract

Background and purpose — Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have become common research fields in orthopedics and medicine in general. Engineers perform much of the work. While they gear the results towards healthcare professionals, the difference in competencies and goals creates challenges for collaboration and knowledge exchange. We aim to provide clinicians with a context and understanding of AI research by facilitating communication between creators, researchers, clinicians, and readers of medical AI and ML research.

Methods and results — We present the common tasks, considerations, and pitfalls (both methodological and ethical) that clinicians will encounter in AI research. We discuss the following topics: labeling, missing data, training, testing, and overfitting. Common performance and outcome measures for various AI and ML tasks are presented, including accuracy, precision, recall, F1 score, Dice score, the area under the curve, and ROC curves. We also discuss ethical considerations in terms of privacy, fairness, autonomy, safety, responsibility, and liability regarding data collecting or sharing.

Interpretation — We have developed guidelines for reporting medical AI research to clinicians in the run-up to a broader consensus process. The proposed guidelines consist of a Clinical Artificial Intelligence Research (CAIR) checklist and specific performance metrics guidelines to present and evaluate research using AI components. Researchers, engineers, clinicians, and other stakeholders can use these proposal guidelines and the CAIR checklist to read, present, and evaluate AI research geared towards a healthcare setting.

Supplementary data

Supplementary data are available in the online version of this article, http://dx.doi.org/10.1080/17453674.2021.1918389

The authors would like to thank Professor Ion Androutsopoulos, Professor of Artificial Intelligence, Department of Informatics, Athens University of Economics and Business for his support and valuable comments.

Author contributions (according to CRediT (https://casrai.org/credit/)) JO: Conceptualization, data curation, formal analysis, investigation, methodology, visualization, writing—original draft, writing—review & editing. JPavlov: Conceptualization, formal analysis, investigation, methodology, writing—review & editing. JPrijs: Writing—review & editing. FIJ: Writing—review & editing. JD: Writing—review & editing. CL: Writing—review & editing. JH: Writing—review & editing. MG: Conceptualization, data curation, formal analysis, investigation, methodology, visualization, writing—review & editing. 

Acta thanks Sebastian Mukka and Anders Troelsen help with peer review of this study.