MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care |
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| Authors: | Tina Hernandez-Boussard Selen Bozkurt John P A Ioannidis Nigam H Shah |
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| Affiliation: | o1 Department of Medicine, Stanford University, Stanford, California, USA;o2 Department of Biomedical Data Science, Stanford University, Stanford, California, USA;o3 Department of Surgery, Stanford University, Stanford, California, USA;o4 Department of Statistics, Stanford University, Stanford, California, USA;o5 Meta-Research Innovation Center at Stanford, Stanford University, Stanford, California, USA |
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| Abstract: | The rise of digital data and computing power have contributed to significant advancements in artificial intelligence (AI), leading to the use of classification and prediction models in health care to enhance clinical decision-making for diagnosis, treatment and prognosis. However, such advances are limited by the lack of reporting standards for the data used to develop those models, the model architecture, and the model evaluation and validation processes. Here, we present MINIMAR (MINimum Information for Medical AI Reporting), a proposal describing the minimum information necessary to understand intended predictions, target populations, and hidden biases, and the ability to generalize these emerging technologies. We call for a standard to accurately and responsibly report on AI in health care. This will facilitate the design and implementation of these models and promote the development and use of associated clinical decision support tools, as well as manage concerns regarding accuracy and bias. |
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| Keywords: | reporting standards electronic health records artificial intelligence clinical decision support |
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