NSF Award
2326034
The increasing use of Clinical Artificial Intelligence/Machine Learning (AI/ML)-enabled Software as a Medical Device (SaMD) for healthcare applications, including medical imaging, is posing significant challenges for regulatory bodies in ensuring that these devices are valid, robust, transparent, explainable, fair, safe, and accurate. One of the major challenges is the phenomenon of data shift, which refers to a mismatch between the distribution of the data that was used for model training/testing and the distribution of the data to which the model was applied. This makes it difficult to generalize AI/ML-enabled SaMD across different healthcare institutions, different medical devices, and disease patterns, resulting in AI model performance deterioration, erroneous outputs, and adverse patient outcomes.<br/><br/>This grant focuses on developing novel methodologies for detecting data shifts in AI/ML-enabled SaMDs in medical cyber-physical systems for healthcare, using lung cancer nodule prediction with research and commercially available AI tools in controlled experimental settings. The project's objective is to create a framework that allows SaMDs to adapt through real-world learning, enhancing their safety and effectiveness in detecting lung cancer nodules. The innovative data shift detection algorithms will advance AI/ML-enabled medical cyber-physical systems, improving model accuracy and reliability to address real-world challenges in the adoption of medical AI/ML applications. Moreover, this grant is committed to promote diversity, equity, and inclusion in STEM fields by providing opportunities for underrepresented minority groups and female scholars-in-residence to work as research scholars at the FDA.<br/><br/>This research is supported by the Computer and Information Science and Engineering Directorate's Division of Computer and Network Systems (CISE/CNS) under the NSF Cyber-Physical Systems (CPS) program.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
