Digital Health Innovation and Informatics
Panel 10 - Artificial Intelligence and Deep Learning Within Radiation Oncology: Current Applications and Future Directions
1:00 PM - 2:30 PM
Location: Room 304
Session Type: Panel
1.50 AMA PRA Category 1 Credits™
1.50 CAMPEP Credits
1.50 MDCB Credits
This panel will serve as a follow up to our well-attended ASTRO 2017 panel "Artificial Intelligence within Radiation Oncology" . The panel will once again provide an introduction to artificial intelligence (AI), and potential applications within radiation oncology. Leveraging large amounts of unstructured data, AI has transformed the ways in which computers solve everyday problems like depositing checks (imaging classification), to searching the internet (natural language processing). Within radiation oncology, AI has the opportunity to revolutionize clinical decision-making, radiation physics and radiobiology. This panel will provide a background to AI, recent implementations of AI within radiation oncology and resources researchers/clinicians can use to harness their data to implement/develop AI solutions. (Dr. Yu, Yale School of Medicine). Building upon last year we will discuss new applications of AI on 1) Clinical decision support using EHR data/Medicare Claims (Dr. Choi, VaTech + Google Research Fellow, 2) Image guided radiation therapy (Dr. Aneja, Yale School of Medicine), and 3) genomic/radiobiologic data (Dr. Cloninger, UC-San Diego). We will discuss the limitations of AI systems and emerging methods improving the transparency/interpretability of AI algorithms to make them more accessible to clinicians and patients (Dr. Aneja, Yale School of Medicine). Lastly, we will discuss recent initiatives by the newly establish ACR Data Science Institute (DSI) to promote the safe and effective implementation of AI. (Dr. Thompson, OHSU, ACR DSI) After this panel, attendees will better understand applications and limitations of AI within radiation oncology along with ways to implement AI in their clinical/research efforts.
- Describe applications of AI to improve clinical decision making, image guided radiation therapy, and interpretation of genomic/radiobiologic data.
- Describe the ways in which AI solutions can be more interpretable and transparent to providers and patients.
- Describe resources needed to practically implement/develop AI solutions within their own clinical practice/institution.
1:00 PM - 1:20 PM
Artificial Intelligence 101: Basic Principles and Current Applications
Speaker: – Yale University
1:20 PM - 1:40 PM
Applications of Artificial Intelligence to Complex Genomic/RadioBiological Data: Implications For Patient Risk Stratification and Drug Discovery
1:40 PM - 2:00 PM
Improving the Interpretability of AI Algorithms: Unpacking the Black Box
Speaker: – Yale School of Medicine
2:00 PM - 2:20 PM
Professional Society AI Initiatives: ACR Data Science Institute
2:20 PM - 2:30 PM
Speaker: – Yale University