Explicit representation standards	The FDA should explicitly mandate that AI/ML medical device submissions provide comprehensive demographic information, including race, ethnicity, age, gender, and socioeconomic status of the studied populations. This ensures that the algorithms are developed and validated on diverse datasets, minimizing the risk of biased outcomes and ensuring equitable representation. These statistics should be reported separately for training, validation, and deployment data.
Inclusive validation criteria	Establish clear guidelines for validation studies to include diverse and representative populations. Manufacturers should be required to demonstrate the performance of their AI/ML algorithms across various demographic groups, ensuring that the technology is effective for minorities, marginalized, and underrepresented communities.
Transparency requirements	Implement stringent transparency standards, mandating clear documentation of the machine learning techniques used, the sources of training data, and the rationale behind algorithmic decisions. Transparency ensures that end-users, healthcare providers, and regulatory authorities have insight into the algorithm’s decision-making processes.
Post-market surveillance for equity	Establish a robust post-market surveillance framework specifically focused on monitoring the performance of AI/ML devices in real-world settings. This ongoing evaluation should include an analysis of outcomes across populations representative of all patients for whom the tool is indicated, with a particular emphasis on detecting and addressing any emerging biases affecting minorities and under-represented groups.
Interdisciplinary review panels	Form interdisciplinary review panels within the FDA consisting of experts in data science, healthcare disparities, and ethics. This ensures a holistic evaluation of AI/ML submissions, incorporating perspectives that can identify and address potential biases and disparities in health outcomes.
Incentives for diversity and equity	Establish incentives, such as expedited review processes or regulatory benefits, for manufacturers who proactively address issues of representation, validation rigor, and bias mitigation in their AI/ML device submissions. This encourages industry-wide commitment to fairness and equity.