Abstract 1084: Analyzing artificial intelligence (AI) policy for cancer research data programs

Abstract Purpose: The National Cancer Institute’s (NCI) Cancer Research Data Commons (CRDC) supports the cancer research community by providing cloud-based, secure storage and analytic tools for multiple cancer data types (e.g., genomic, proteomic, imaging, and clinical trial data). CRDC is beginning to integrate artificial intelligence (AI) solutions, such as using AI to annotate medical images and making AI models and resources available for cancer researchers. As AI policies emerge, CRDC must track relevant guidelines to determine how AI can safely and compliantly unlock value for researchers. Methods: CRDC conducted a landscape analysis to inform CRDC of potential AI risks and policies across three branches of the Federal government and international agencies. CRDC identified and analyzed policies from organizations focused on AI risk mitigation measures, general AI guidance, and AI strategy as relates to federal health agencies. Results: The following policies have the most implications for CRDC. In the Executive Branch, the Executive Order on Safe, Secure, and Trustworthy AI mandates federal agencies to ensure AI safety and trustworthiness. The National Institute for Standards and Technology developed AI evaluation criteria, risk assessments, and tools to help assess AI models. CRDC can follow this mandate and use the tools to begin developing an AI strategy. In the Legislative Branch, the Senate AI Working Group and House AI Task Force are starting to discuss specific AI legislation. Although legislation hasn’t passed, CRDC should track legislative updates that could impact CRDC compliance. In the Judicial Branch, the Supreme Court overturned the Chevron Deference. Although not directly related to AI, the decision could affect how CRDC interprets and acts upon AI guidance. Internationally, European countries developed accountability frameworks, evaluation criteria, tools, and risk assessments to identify roles and responsibilities and help assess AI models. While NCI is not obligated to follow European AI policies, these artifacts can inform CRDC in developing an AI strategy. Conclusion: AI policies continue to emerge and evolve. As guidance is solidified, it is important that CRDC continues to monitor and analyze requirements to mitigate risks and ensure responsible use of AI in cancer research. This ongoing analysis will inform CRDC, enhance trust within their user community, increase timely access to cancer data analytical tools, and contribute to the advancement of cancer therapies. Citation Format: Juergen Klenk, Dina Mikdadi, Bhavani Singh, Chelsea Owens, Eric Barner, Ross Campbell, Mary Sears, Ina Felau, Erika Kim, Tanja Davidsen. Analyzing artificial intelligence (AI) policy for cancer research data programs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1084.

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Journal
Cancer Research
Published
2025-04-21
DOI
https://doi.org/10.1158/1538-7445.am2025-1084
Primary Topic
Radiomics and Machine Learning in Medical Imaging
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article
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article

Abstract 1084: Analyzing artificial intelligence (AI) policy for cancer research data programs

Tanja M. Davidsen, Erika Kim, Ina Felau, Mary Sears et al.
Cancer Research
Radiomics and Machine Learning in Medical Imaging
article

Abstract 1084: Analyzing artificial intelligence (AI) policy for cancer research data programs

Tanja M. Davidsen, Erika Kim, Ina Felau, Mary Sears, Ross Campbell, Juergen Klenk, Bhavani S. Singh, Dina Mikdadi, Eric Barner, Chelsea Owens
article en

Abstract

Abstract Purpose: The National Cancer Institute’s (NCI) Cancer Research Data Commons (CRDC) supports the cancer research community by providing cloud-based, secure storage and analytic tools for multiple cancer data types (e.g., genomic, proteomic, imaging, and clinical trial data). CRDC is beginning to integrate artificial intelligence (AI) solutions, such as using AI to annotate medical images and making AI models and resources available for cancer researchers. As AI policies emerge, CRDC must track relevant guidelines to determine how AI can safely and compliantly unlock value for researchers. Methods: CRDC conducted a landscape analysis to inform CRDC of potential AI risks and policies across three branches of the Federal government and international agencies. CRDC identified and analyzed policies from organizations focused on AI risk mitigation measures, general AI guidance, and AI strategy as relates to federal health agencies. Results: The following policies have the most implications for CRDC. In the Executive Branch, the Executive Order on Safe, Secure, and Trustworthy AI mandates federal agencies to ensure AI safety and trustworthiness. The National Institute for Standards and Technology developed AI evaluation criteria, risk assessments, and tools to help assess AI models. CRDC can follow this mandate and use the tools to begin developing an AI strategy. In the Legislative Branch, the Senate AI Working Group and House AI Task Force are starting to discuss specific AI legislation. Although legislation hasn’t passed, CRDC should track legislative updates that could impact CRDC compliance. In the Judicial Branch, the Supreme Court overturned the Chevron Deference. Although not directly related to AI, the decision could affect how CRDC interprets and acts upon AI guidance. Internationally, European countries developed accountability frameworks, evaluation criteria, tools, and risk assessments to identify roles and responsibilities and help assess AI models. While NCI is not obligated to follow European AI policies, these artifacts can inform CRDC in developing an AI strategy. Conclusion: AI policies continue to emerge and evolve. As guidance is solidified, it is important that CRDC continues to monitor and analyze requirements to mitigate risks and ensure responsible use of AI in cancer research. This ongoing analysis will inform CRDC, enhance trust within their user community, increase timely access to cancer data analytical tools, and contribute to the advancement of cancer therapies. Citation Format: Juergen Klenk, Dina Mikdadi, Bhavani Singh, Chelsea Owens, Eric Barner, Ross Campbell, Mary Sears, Ina Felau, Erika Kim, Tanja Davidsen. Analyzing artificial intelligence (AI) policy for cancer research data programs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1084.

Cancer ResearchVol. 85(8_Supplement_1)
Deloitte (United States) (US), National Cancer Institute (US)
Openalex Percentile: Top 87%
Radiomics and Machine Learning in Medical Imaging
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