Course 1

Real-Time AI Monitoring & Early Detection of Immune-Related Adverse Events: AI in Immuno-oncology

Date of Live Event

May 2, 2024 - 12–1 p.m. ET 

AI is being investigated for real-time monitoring of patients receiving immunotherapy for immune-related adverse events (irAE). Earlier and more accurate detection of toxicities can vastly improve patient outcomes. The first webinar in our series will educate participants on the current application of AI in irAE monitoring and detection, as well as discuss future applications of these technologies across the field.

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Course 2

AI in Personalized Immunotherapies: AI in Immuno-oncology

Date of Live Event

May 15, 2024 - 12–1 p.m. ET

The vision of personalized medicine based on tumor-specific biomarkers comes closer to reality with each new scientific discovery. With the advent of AI, researchers now have a novel and expansive tool that can better analyze large-scale data sets for actionable markers that can fuel immunotherapy development and discovery. Attend this webinar to learn more about the novel application of AI in immunotherapy target discovery and how these advances could rapidly enhance clinical care.

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Course 3

Opportunities and Implications for IO Using AI and Digital Pathology: AI in Immuno-oncology

Date of Live Event

June 20, 2024 - 12:30–1:30 p.m. ET

Digital pathology is becoming an increasingly important technique for immunotherapy drug development and clinical care. As AI becomes further entrenched across the field of immunotherapy, researchers have begun considering how AI and digital pathology may coexist for enhanced pathologic analyses as well as scientific and clinical decision making. The focus of this webinar will be to better understand the benefits and caveats of mixing AI and digital pathologic analyses across the field of immunotherapy.

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Course 4

Social Determinants of Health with Large/Moderate Language Models on EHR Data: AI in Immuno-oncology

Date of Live Event

July 30, 2024 - 1:30–2:30 p.m. ET 

Social determinants of health are becoming increasingly understood as having significant impact in immunotherapy patient care. Many of these determinants are captured in the electronic health record (EHR), but very few methods can effectively analyze these data to better determine correlations between determinants and immunotherapy outcomes. This webinar will focus on new, prospective methods to analyze health record data for social determinant indicators that could help enhance immunotherapy administration and access.

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Course 5

Predicting Early Diagnosis and Response with Multimodal AI: AI in Immuno-oncology

Date of Live Event

Aug. 29, 2024 - 2:30–3:30 p.m. ET

Earlier cancer diagnosis, as well as rapid determination of tumor response after treatment, could lead to enhanced immunotherapy patient outcomes. Multimodal AI that can accept various data inputs, especially as treatments become more personalized, are prime for helping facilitate advances in cancer diagnosis and response classification. This webinar will describe current efforts in the development of multimodal AI for these purposes, with a specific focus on how these models can better serve immunotherapy clinical decision making.

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Course 6

Predicting Drug Resistance and Drug Repurposing with Imaging-Based AI: AI in Immuno-oncology

Date of Live Event

Sept. 25, 2024 - 1:30–2:30 p.m. ET

Increased understanding of immunotherapy mechanisms of action may enhance researchers’ ability to circumvent resistance and identify alternative therapeutic uses. Imaging-based AI that can closely analyze drug-specific structures and interactions are being developed to help expedite discoveries in these two critical areas. Come attend this exciting webinar where specific applications of imaging-based AI for better understanding of immuno-therapeutic mechanisms will be discussed.

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Course 7

Rationalized Patient Match and Clinical Trial Design with Large/Moderate Language Models on EHR Data: AI in Immuno-oncology

Date of Live Event

Oct. 15, 2024 - 1–2 p.m. ET

Thousands of IO clinical trials are currently available to cancer patients, all of which are aiming to identify the next generation of potential life-saving treatments. However, it can be difficult to design trials that will be able to efficiently enroll patients, especially in the field of immunotherapy where mechanisms of action have increased in complexity. This webinar will describe advances in AI application for analyzing electronic health record (EHR) data in order to better design immunotherapy clinical trials and more efficiently match potential patients.

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Course 8

Federated AI in Training Prediction Models: AI in Immuno-oncology

Date of Live Event

Nov. 13, 2024 - 12–1 p.m. ET

Data privacy and security has become critical for all researchers to consider as they generate significant amounts of data across their immunotherapy studies. A federated AI teaching approach across the field of immunotherapy, where data remains localized, may help alleviate the privacy and security concerns for using sensitive patient data. Advances in the development of federated AI development will be discussed during this webinar, with a specific emphasis on how immunotherapy prediction models could utilize these processes.

WEBINAR 8 FLYER

Course 9

AI-Foundational Models for Cancer Imaging Biomarkers: AI in Immuno-oncology

Date of Live Event

Dec. 17, 2024 - 1–2 p.m. ET

This webinar will explore the transformative impact of artificial intelligence, particularly deep learning, in medical imaging to advance cancer care. A central theme of the talk will be the power of foundation models—large-scale AI systems trained on broad, unlabeled datasets that can be fine-tuned for various clinical tasks. These models, which leverage self-supervised learning, excel in limited data scenarios, making them especially valuable in biomarker discovery. This webinar will also showcase the importance of open-source, transparent, and reproducible AI frameworks in healthcare. These principles are essential to build trust, ensure reliability, and foster collaborative innovation, enabling researchers to advance the field and accelerate the clinical translation of AI-driven imaging biomarkers.

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