Gastroenterology and Artificial Intelligence: 4th Annual Global Artificial Intelligence Summit | August 2022-Clinicians Trust in AI, Fairness and Bias

Pdf Summary

AI bias and fairness in healthcare are important factors to consider when using artificial intelligence (AI) systems. Incomplete or biased data can lead to problematic outcomes in AI algorithms. Clinicians should understand the algorithm used, potential biases, and regulatory aspects to ensure trustworthy AI.<br /><br />To address bias, it is crucial to define the problem and its relevance to patients, endoscopists, or health systems. Understanding the algorithm's design specifications, performance evaluation, and training data is necessary. Model governance and transparency are also important, including traceability of data and explainability. Trustworthy AI requires a basic understanding of algorithms and awareness of bias.<br /><br />Model development stages offer an opportunity to assess data quality and mitigate bias before building the model. Security and compliance with medical device regulations, such as FDA and data protection regulations, are essential in ensuring fairness and legality.<br /><br />Federal efforts to address bias and fairness in AI include guidelines from the U.S. FDA, FTC, NIST, ONC, and OSTP. These organizations emphasize good practices, transparency, and independent evaluation to minimize discriminatory outcomes and promote the responsible use of AI in healthcare.<br /><br />In conclusion, AI bias and fairness in healthcare are critical considerations for clinicians. By understanding the algorithm, interrogating data quality, ensuring transparency, and complying with regulations, trustworthiness in AI can be achieved. With these measures in place, AI can be a valuable tool in improving patient care and outcomes.

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Sravanthi Parasa, MD

Keywords

AI bias

fairness in healthcare

artificial intelligence systems

biased data

problematic outcomes

algorithm understanding

regulatory aspects

trustworthy AI

data quality assessment

medical device regulations