An ad hoc committee of the National Academies of Sciences, Engineering, and Medicine will organize a symposium to discuss current and future applications of Artificial Intelligence (AI) and Machine Learning (ML) in radiation therapy, medical diagnostics, and radiation occupation safety. Specifically, the symposium will include:

Conversations led by thought leaders from a few select fields outside of medical and occupational safety (e.g., computation, transportation, legal, military, etc.) to foster community connections.
Symposium participants will then discuss:

The critical role of human decision-making in the context of AI/ML (e.g., when do algorithms advise, extend, or supplant professional medical decisions).

Future directions and opportunities in AI/ML methods and technology to advance the fields of radiation therapy, medical diagnostics, and radiation occupational health and safety.

• Where has each subfield been focusing efforts in AI/ML?
• What leading/guiding practices are currently being used?
• Where would the field like to go?
• What is the biggest hurdle being faced by each subfield?

The key challenges in data quality control with respect to reproducibility, generalization of data sets, data/model drift, and trustworthiness of results with respect to each subfield of radiation therapy, medical diagnostics, and radiation occupational safety. This discussion will also include intentionality of data collection, detector development, and data set management for future AI/ML end algorithm applications and considered trustworthiness.

Current methodologies used when implementing AI/ML techniques for each subfield and discussions on ways to learn from other fields and identify key challenges and gaps in each subfield.

Possible ethical implications of AI/ML applications in each subfield and a community discussion on possible future directions to maximize benefits and minimize negative implications.

Tailored breakout sessions to cover specific applications of AI/ML including but not limited to: creating predictive models for estimating dose in occupational environments exposed to radiation, dose distribution models for treatment planning, predictive AI/ML techniques in cancer diagnosis and treatment, integration of AI/ML into accurate and ethical human decision making, AI/ML uses to address educational and workforce shortages of radiation medical professionals, and thresholds of uncertainty acceptable for use of AI/ML in radiation applications.

The symposium presentations and discussions will be summarized in a National Academies proceedings.