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MINIMAR (MINimum Information for Medical AI Reporting): Developing Reporting Standards for Artificial Intelligence in Health Care

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Date 2020 Jun 29
PMID 32594179
Citations 123
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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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References
1.
Challener D, Prokop L, Abu-Saleh O . The Proliferation of Reports on Clinical Scoring Systems: Issues About Uptake and Clinical Utility. JAMA. 2019; 321(24):2405-2406. DOI: 10.1001/jama.2019.5284. View

2.
Riley R, Ensor J, Snell K, Debray T, Altman D, Moons K . External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ. 2016; 353:i3140. PMC: 4916924. DOI: 10.1136/bmj.i3140. View

3.
Parthipan A, Banerjee I, Humphreys K, Asch S, Curtin C, Carroll I . Predicting inadequate postoperative pain management in depressed patients: A machine learning approach. PLoS One. 2019; 14(2):e0210575. PMC: 6364959. DOI: 10.1371/journal.pone.0210575. View

4.
von Elm E, Altman D, Egger M, Pocock S, Gotzsche P, Vandenbroucke J . The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007; 370(9596):1453-7. DOI: 10.1016/S0140-6736(07)61602-X. View

5.
Rothman B, Leonard J, Vigoda M . Future of electronic health records: implications for decision support. Mt Sinai J Med. 2012; 79(6):757-68. DOI: 10.1002/msj.21351. View