Potential Opportunities for AI Funding Under White House Pediatric Cancer Initiative
The Initiative
In 2019, the Trump Administration established the Childhood Cancer Data Initiative (CCDI), which obligated the federal government to contribute $50MM every year for ten years to the collection, generation and analysis of cancer data for cancer research purposes. Reinvigorating this mission, on September 30, 2025, the White House issued an executive order directing expanded use of AI and funding to accelerate pediatric-cancer research. Concurrently, the Department of Health and Human Services (HHS) announced it would double CCDI funding, raising the federal commitment from $50MM to $100M annually.
Under the executive order, overseen by the MAHA Commission and the Department of Health and Human Services (HHS), CCDI funding will be used to develop ”innovative ways to utilize advanced technologies such as AI to unlock improved diagnoses, treatments, cures, and prevention strategies for pediatric cancer.” Presently, the CCDI collects and aggregates electronic health records and claims data with the goal of informing clinical trial design and improving patient outcomes. Under the new initiative, the CCDI will build a foundational data infrastructure to implement AI for further use in diagnostics, and for the identification of treatment and prevention measures. The executive order also includes plans to establish a secure, anonymized pediatric health data commons to support broad sharing among federal agencies, academic institutions and private sector researchers.
While initial uses for this foundation model will be for pediatric cancer research, clear potential exists to apply use of this AI system to all forms of cancer and, eventually, different diseases and health incidents.
Where Initial Investments Will Go
The initial focus will be on identifying opportunities to accelerate the progress of AI-driven solutions at the CCDI, and funding research projects at National Cancer Institute-Designated Cancer Centers. These projects will be ones that prioritize: (1) improving data infrastructure by harmonizing data across multiple sources for AI-use; (2) enhancing data analysis for complex biological systems with AI tools; and (3) improving clinical trial design, access, and outcomes for patients by using AI to maximize utilization of information from clinical trials to improve accessibility to and recruitment for trials, as well as to optimize administration, and interpretation of clinical trial results.
Where SMB’s Fit
This broad initiative creates funding and contract opportunities for a plethora of SMBs in AI, technology, healthcare, data science, data processing, cybersecurity, biotech and health analytics. Realistically speaking, however, while the initiative is structured to encourage private-sector collaboration, government agencies are already approaching private AI research labs, biotech firms, and AI startups.
Competing against Big Tech and industry giants for the remaining opportunities will be a challenge, so here some concrete steps that SMBs can take to optimize their chances of receiving a slice of the $100MM pie, or seizing other related opportunities:
Know Your Niche: Inventory any existing or planned AI-linked oncology modules for readiness, or any supportive tech that you think can be used to implement health-first AI technologies. Rather than building an end-to-end pediatric cancer AI solution, SMBs may be more successful targeting a component of an overall system (e.g., a data harmonization model, an image preprocessing tool for body scan imagery, a document ingestion model to interpret medical records from different types of media).
Build or Refine Using Privacy-by-Design Principles: Health data, particularly health data of children, is among the most private data that an entity could collect, process or store. It is of paramount importance that all systems operate bearing that in mind, and implement privacy systems from initial iterations. Systems should be designed (or redesigned) with privacy, security, explainability, auditability and regulatory compliance in mind as retrofitting down the line may be expensive, or even impossible. Further, to the extent possible, AI protocols that favor privacy (e.g., federated learning and differential privacy) should be utilized for the sharing of health data.
Monitor Interoperability Standards & Policy Evolution: Engage with electronic health records providers and federal networks to ensure that any technology allows for both interoperability and secure data access in line with privacy and security laws. SMBs should closely monitor interoperability standards (e.g., FHIR) so solutions stay compatible. It may even be beneficial for SMBs to join a consortium to learn about changes in advance, and/or to be involved in drafting and shaping new standards for the future.
Compliance Certifications for Contract Eligibility: To demonstrate ability to properly handle sensitive data, or even to qualify for some federal contracts, SMBs may have to obtain HIPAA or FedRAMP certifications (as appropriate).
Risk Management & Liability: Anticipate legal and clinical liability issues, and ensure that automated decision making is either not incorporated into AI tools, or greatly limited, that all decision-support tools include disclaimers when used, and clear oversight pathways exist to keep clinician-in-the-loop (or human-in-the-loop, depending on the use case).
Risk management & liability: Be sure to anticipate legal and clinical liability issues. Decision-support tools should have disclaimers and clear oversight pathways (i.e., clinician in the loop).
Document validation & performance: Medical claims are held to a high level of scrutiny and must be validated through rigorous testing. However data is processed or analyzed, validation will be critical. SMBs should develop documentation for the validation of algorithms for regulatory review, including clear clinical off-ramps for human oversight.
Plan for Commercialization & Reimbursement: Whether or not an SMB scores a contract, or obtains funding for research, SMBs may still have the opportunity to sell products to hospitals and research institutions benefitting from grant money. SMBs should consider (1) who their buyer is and (2) how the buyer will pay (grants, subscriptions, licensing) to allow for a seamless pitch and go-to-market strategy.
Monitor Grant and Funding Calls: Stay alert to NIH, NCI, HHS, or MAHA Commission solicitations specifically targeting pediatric cancer and/or AI. These may be smaller awards that are friendly to SMBs. SMBs can monitor grants at grants.gov, and the NIH RePORTER database.
Network! (Strengthen Credentials and Partnerships): SMBs may have access to more opportunities by partnering with established cancer centers, children’s hospitals, or academic labs, which can provide access to sample data, domain expertise, and generally provide credibility (particularly for tech-first companies entering the biotech area for the first time). Further, industry groups may gather cross-sector participants to coordinate responses and share data access opportunities. SMBs can join these groups to gain visibility, and avail themselves to any early notice of collaboration opportunities among the other organizations in the group. Finally, networking with policymakers, and sharing knowledge during public comment periods pertaining to AI tools’ safety, transparency, or other data innovations can establish SMB founders as thought leaders, and on the forefront of relevant technology, which could lead to calls for help down the line.
Ethical Positioning & Public Perception: Transparency is a huge buzz word in AI for good reason. The public wants to know that their data is not being misused, or being used in a manner that will disrupt their privacy, or cause social harm. Pediatric cancer is some of the most sensitive data, and technology designed to aid in diagnosis, treatment, and prevention of childhood cancers must be trustworthy. The public wants it to work right the first time, and every time. Therefore, SMBs should always strive to foster an ethical reputation, transparency, and community trust. Public communication and ethical data use practices are critical to creating and maintaining trust needed to score these important jobs.
Conclusion
The executive order signals that the government is serious about infusing money into AI in the biohealth sector, but how and when that will happen remain to be seen.
The White House’s directive is broad and aspirational, but the rules, grants and mechanisms to carry out the executive order will take weeks and months to develop and implement. Furthermore, while funding can be fast, any oversight–including by HHS and the FDA–will likely slow forward progress. SMBs will face stiff competition from large tech and biotech companies with established credibility and resources. Therefore, SMBs may wish to partner with a larger company to take advantage of this opportunity. SMBs should also bear in mind the sensitive nature of the data being collected and analyzed, and build any systems implementing privacy-by-design principles so that data is protected from the very first iteration of any MVP. Privacy, consent and data governance policies will likely be laboriously scrutinized during the selection process, and good governance up front may make the difference between getting a contract or not.
