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The Roadmap Forward

A working framework for how AI can actually help cure cancer — and what's standing in the way.

Part 1

Support & Scale AI Tools to Accelerate Cancer Cures

The AI capabilities that we have today contain huge untapped potential to accelerate science. We must support the companies and projects leading the charge.

Part 2

Double Down on Concrete Areas of Promise in Oncology

Target the clear areas of promise in oncology: better implementing the tools we already have and eliminating disparities in access to the state of the art.

Part 3

Tackle the Main Blockers to Medical Progress that are Limiting AI Tools

Though there are many promising AI-powered oncology projects, all will fail to meet their potential when operating in current structural constraints.

AI vs Cancer explores why artificial superintelligence (ASI) won’t deliver on the cancer curing hype, and details the many data, economic, systemic, and institutional challenges that bottleneck progress. But if ASI isn’t the answer, where do we go from here to actually have a chance at defeating cancer and how can AI help? This roadmap outlines the work that is already being done and towards this problem, but also highlights the real issues that we need to address if we are going to cure cancer.

This roadmap is also an invitation - to everyone who reads it to help us to refine the ideas and to collectively bring momentum to the issue. Cancer has proven an intractable enemy for over 600 million years since multicellular life appeared. If we want to change that then it will require input from every sector of humanity to bring ideas, skills and the demand for political and societal changes.

Why Now is Ideal

Beyond the urgency to tackle cancer given the human cost of patients dying every day, we sit at a unique point in modern history that makes going on the offense against disease more promising than ever. U.S. healthcare has long been trapped in what Scott Alexander calls a Moloch problem: a coordination failure where every stakeholder sees the system collapsing yet no one can escape the dynamics driving it down. Hospitals are closing, insurers are in death spirals, pharmaceutical companies face a $236 billion patent cliff by 2030, science funding is waning, doctors are burnt out, and patients can't access care, all while healthcare consumes 27% of federal spending and threatens the nation's fiscal and geopolitical footing.

It’s clear that tinkering with reform at the edges is no longer viable economically or politically. The same resignation haunts cancer specifically: after Nixon's 1972 moonshot and Obama's in 2016, mortality rates have barely budged, breeding a fatalism that curing disease is simply unsolvable. But there has been no better time to shake off fatalism and go on the offense. Ironically, a system in late-stage collapse also creates the conditions to rebuild. Culturally, within US politics, there is an appetite for radical institutional disruption, with polling during elections showing 83% wanting substantial change or complete upheaval .

Further, existing AI capabilities are already set to radically disrupt the workforce and incumbent businesses, whether for the better or worse remains unclear. What is clear is the combination of collapsing healthcare, political appetite for disruption, and the availability of AI's tools to reduce friction, align incentives, and manage coordination at scale, creates a once in a generation window to explore new avenues that may at last enable us to develop a concrete path to better healthcare and a genuine plan to cure cancer.

Part 1

Support & Scale AI Tools to Accelerate Cancer Cures

The AI capabilities that we have right now contain immense promise to accelerate science. There are countless companies, non-profits and academics hard at work developing AI tools often coupled with novel methods of measuring human biology to tackle key bottlenecks in oncology.

From the Cancer AI Alliance’s development of a federated AI framework to empower researchers to learn from deidentified patient data, to the National Cancer Institute’s Digital Twin development, non-profits, large pharmaceutical companies, small startups, government institutions and academics are all hard at work on AI tool development.

While BigTech has tried to claim the mantle of building, these are the true builders we should be celebrating, resourcing and scaling. Below is a sample of some of the key actors working at various points from bench to bedside to unlock the power of AI to deliver real benefits to patients.

AI Drug Discovery & Target Identification

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Developing AI tools to find disease targets and design drugs to block them, faster than traditional lab methods.

AI Drug Discovery & Target Identification

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Merck*, Novartis*, Pfizer*, Sanofi*, Roche*, Daiichi Sankyo*, AstraZeneca*, Isomorphic Labs, In Silico Medicine, Recursion Pharmaceuticals, Schrodinger, Xaira Therapeutics, Insitro, Relay Therapeutics, Exscientia, BigHat Biosciences, Terray Therapeutics, Compugen, Atomwise, Nimbus Therapeutics, ATOM Consortium

AI Genomics

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Developing AI tools to read and analyze genetic data at massive scale to uncover drivers of cancer and disease

AI Genomics

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UKB Genomics- Amgen*, AstraZeneca*, GSK*, Johnson & Johnson*, Memorial Sloan Kettering-IMPACT, AACR Project GENIE, Broad Institute, Wellcome Sanger Institute, Foundation Medicine, Sophia Genetics, Tempus AI, Veracyte (Hardware: Illumina, 10x Genomics, Oxford Nanopore Technologies)

AI Proteomics & Biomarkers

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Develop AI tools to map the proteins the body produces to uncover early or hidden signals of disease presence, progression, and treatment response

AI Proteomics & Biomarkers

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UKB Pharma Proteomic Project- Alden Scientific, Amgen*, AstraZeneca*, Biogen, Bristol Myers Squibb*, Calico Life Sciences, Roche*, GSK*, Isomorphic Labs, Johnson & Johnson*, MSD, Novo Nordisk*, Pfizer*, Regeneron*, Takeda*, Parker Institute for Cancer Immunotherapy, Human Tumor Atlas Network (Hardware: Olink, SomaLogic, Seer Bio, Alamar Biosciences, Nautilus Biotechnology, NanoString, Standard BioTools,)

AI Toxicity Prediction

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Developing AI tools trained on compounds already known to be safe or harmful, predict whether a new compound will be toxic in humans in advance.

AI Toxicity Prediction

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University of Vienna ToxCoder, Axiom Bio, Simulations Plus, Certara, Astra Zeneca, Instem, Tox21 & ToxCast, Lhasa Limited, Chemaxon

AI In Silico Modeling & Virtual Cells

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Building AI-powered digital replicas of cells and biological systems to simulate experiments and better study basic biology.

AI In Silico Modeling & Virtual Cells

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Microsoft Research (BioEmu), Genetech/Roche, Google DeepMind, CZ Biohub, Dassault Systèmes, Arc Institute, Broad Institute, D.E. Shaw Research, Allen Institute for Cell Science, Human Cell Atlas, NVIDIA (BioNeMo)

AI Drug Repurposing

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Using AI tools to mine existing approved drugs and natural compounds for hidden potential against diseases.

AI Drug Repurposing

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Broad Institute/PRISM Program, EveryCure, NuMedii, BenevolentAI, Lantern Pharma, Healx, DreamBio/ OpenTargets

AI Clinical Trials & Regulatory Affairs

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Using AI to accelerate, improve and redesign how trials are run, analyzed, and approved by replacing slower conventional methods.

AI Clinical Trials & Regulatory Affairs

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NCI Digital Twins Consortium, Friends of Cancer Research (ai.RECIST), Core Cancer Europe, IQVIA, Medidata, ConcertAI, Unlearn.AI, QuantHealth, Saama Technologies, Flatiron Health, Medable, Certara, PAREXEL, Velsera, Hologen, AiCure, TriNetX

AI Early Detection, Liquid Biopsies & Imaging

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Using AI to process blood, scans, and tissue samples to detect cancer earlier than current methods.

AI Early Detection, Liquid Biopsies & Imaging

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DeepCell, Delfi Diagnostics, GRAIL, Guardant Health, Exact Sciences, Freenome, C2i Genomics, Volition, OHSU Knight Cancer Institute, Owkin, PathAI, PaigeAI, Proscia, Ibex Medical Analytics, Visiopharm, Mindpeak, Caris Life Sciences, Personalis

AI Clinical and Surgical

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Bringing AI into the clinic and operating room to guide decisions, improve precision, and personalize care delivery.

AI Clinical and Surgical

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Cancer AI Alliance, Cancer Research Institute, Intuitive Surgical, Activ Surgical, Caresyntax, Komodo Health, Champions Oncology, RaySearch Laboratories, Varian, Elekta, Accuray RefleXion, MD Anderson Lyda Hill Department of Bioinformatics, Cancer Research UK, Dana-Farber Cancer Institute Profile Program, Parker Institute for Cancer Immunotherapy, Mayo Clinic, Alan Turing Institute, BostonGene

Advocacy for AI Tools in Oncology

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Leading cancer organizations pushing for AI-driven advances to reach patients through policy, funding, and clinical adoption.

Advocacy for AI Tools in Oncology

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American Association of Cancer Research, American Society of Clinical Oncology, Stand Up To Cancer, Friends of Cancer Research

*Major pharmaceutical companies are integrating AI throughout discovery, drug development, and internal processes. The specifics of these programs have limited visibility due to corporate considerations.

Part 2

Double Down on Concrete Areas of Promise in Oncology

In parallel, it is also important to double down on the most promising areas for progress in oncology.

Early Detection & Prevention
  • Resource reduction efforts for known drivers of cancer such as smoking, obesity, and alcohol consumption
  • Increase access to proven screening (mammography, colonoscopy/stool tests, low-dose CT for high-risk smokers) and prevention measures (HPV & Hepatitis B vaccination)
  • Research and address environmental carcinogens (PFAS contamination, benzene, occupational exposures, and air particulate matter) through policy interventions
  • Scale and insurance coverage of multicancer early detection blood tests. Also resource large scale studies to determine if they deliver mortality benefits
  • Research promising drugs to prevent cancer in high risk groups defined by genetics or new multi-omic risk analyses coming online
  • Invest in methods, such as blood tests and real time monitoring, to detect minimal residual disease or cancer resistance as soon as emerges/ in real time to enable rapid intervention
Data & Clinical Trials
  • Build a national cancer data commons linking genomic, imaging, treatment, and outcomes data to end the fragmentation blocking AI-scale discovery
  • Develop novel incentives to generate large, longitudinal datasets for example buying individual naming rights to datasets, creation of philanthropic consortia, public-private partnerships, government investment in data moonshots
  • Replace single-drug trials with newer, adaptive platform trials (basket/umbrella/master protocol) testing multiple drugs simultaneously in biomarker-selected populations
  • Establish a non-profit pharma accelerator to push scientifically credible but commercially unprofitable drugs (rare cancers, pediatric, off-patent combinations) through FDA approval
Improving Research & Treatment
  • Continue innovation in radiation oncology (proton beam therapy, MR-guided adaptive radiotherapy, FLASH radiotherapy, etc) & surgical oncology (real-time margin assessment, fluorescence-guided imaging, robotics) while increasing supply of services and democratizing access to treatments
  • Develop a robust supply chain for existing generic chemotherapy drugs and invest in novel methods of manufacturing cutting edge treatments to bring down costs (Allogenic CAR-T to bring cost from $500k to $10-20k, personalized mRNA cancer vaccines)
  • Resource innovation in promising areas of drug development (PROTACs, ADCs, bispecific T cell engagers, next generation checkpoint inhibitors, tumor microenvironment targeting, radiopharmaceuticals, etc)
  • Increase access to functional profiling and testing (patient derived organoids and tumor xenografts)
  • Launch initiatives like Human Genome Projects to study the proteome, virome, microbiome, metabolome and immunome in oncology to help unlock precision medicine
  • Resource survivorship medicine (study of cancer survivors) and also convergence between cancer biology and aging biology
Metascience & Institutional Reform
  • Restructure NIH toward an ARPA-H model with empowered program officers funding high-risk, unconventional bets that peer review currently filters out & create high-novelty funding tracks
  • Address the preclinical replication crisis through mandatory electronic laboratory notebook use for federally funded research, pre-registration, funded replication studies, and incentives for publishing null results
  • Implement outcome-linked reimbursement for cancer therapies: link drug payments to confirmed long-term remission and quality-of-life outcomes rather than per-dose volume. Align pharmaceutical incentives with patient benefit rather than treatment duration
  • Dedicated FDA Oncology Approval Track with conditional approvals, mandatory confirmatory trials, and fast-tracked withdrawal when those trials fail
  • Address financial toxicity directly: Oncology accounts for only 7% of total healthcare spending. Pursue cost-effectiveness frameworks and drug price negotiation, as well as broader systemic reform to ensure citizens do not go bankrupt for cancer treatment

Part 3

Tackle the Top 10 Blockers to Medical Progress Imposing Limits on the Utility of AI Tools

AI's utility in advancing cures for disease is limited by the following blockers:

Data Creation

Data Creation

01 How do we create data to build a real-time map of what healthy actually looks like?
Reliable, high-resolution definition of disease, requires reliable high-resolution definition of health. Until we know what deviations in biology are normal and which drive disease, we cannot tease the signal of disease from that of health. We need funding and coordination for more population scale, longitudinal, multiomic baselines for human health across age, sex, ethnicities, geographies, and environments. In parallel, we also need to push the frontier of clinical measurement capabilities and integration of established methods into clinical practice.
02 How do we create data to compress biological timescales across health and diseases? How do we know something is working in an individual before the disease plays out?
This is the highest-leverage and most valuable technical problem in medicine. If we can validate surrogate biomarkers and real-time biological readouts, we could both collapse decade-long trials into years, making drug development for prevention and early treatment financially viable. It would also enable individualized medicine, at scale. This will require at least a single cycle of human disease to validate, but will position us well for the future. Further, even without full validation, construction of well-founded surrogate biomarkers is better than the vacuum of information guiding treatment today. Confidence in a surrogate can be built incrementally, based on biological plausibility and retrospective correlation with hard endpoints, even before prospective validation is complete.
03 How do we generate data to build preclinical models that actually predict human outcomes?
Cells and mice fail us. The hard limit on experimentation is our inability to study human biology reliably without directly studying humans or their primary cells and tissues, but we are still far short of that limit. Progress starts with optimizing experimental capabilities for human-derived organoids, patient-derived xenografts, and Phase 0 microdosing trials, all of which dramatically improve predictive value over standard cell and mouse models. Ultimately, innovating in and scaling our capacity to measure human biology is the deeper solution, explored in Question 1.
04 How do we build AI models trained on comparable data rather than publication bias? How can we capture and integrate negative results, failed experiments, and unpublished data?
Essentially this is solving the garbage in, garbage out problem in distinct ways. The first is capturing what's missing: negative results, failed experiments, and unpublished data that currently disappear into filing cabinets. Mandated electronic laboratory notebooks with enforcement for all federally-funded research is one concrete mechanism. The second is making existing data comparable across labs and experimental iterations, which requires standardization of reagents, protocols, batch correction, and reporting. The field has pursued solutions for decades with limited success, suggesting both technical and incentive structure difficulties. Tackling both begins with rewarding meaningful data generation, not just positive results.
Economics and Incentives

Economics and Incentives

05 How do we make preventing or curing disease more profitable than treating it chronically?
Models such as value-based care and outcome-based pricing take on new meaning in the age of AI, which can help to democratize data collection, and improve both transparency and analysis of patient outcomes. Further, political will for payment reform and elimination of administrative waste with AI could generate savings that can be reinvested in experimenting with different reimbursement models. As discussed explored in the Why Now is the Right Time section, AI-enabled outcome tracking, the fiscal crisis forcing change, insurance death spirals, hospital bankruptcies, and an imminent patent cliff create a unique window for experimenting with new models.
06 How do we fund the therapeutic development that markets won't fund but need to succeed?
Markets reliably fail to deliver potentially life-saving treatments to patients from antibiotics, to rare diseases. The antibiotic crisis showed that philanthropy can step in when markets fail, but philanthropy alone isn't sustainable. The more durable answer lies in changing the economics. As AI and next-generation measurement techniques like multiomics bring down the cost of clinical trials (and potentially enable n=1 studies) the financial barrier to researching commercially unviable treatments falls significantly. This opens the door to a broader funding ecosystem: government grants, ARPA-H style high-risk bets, single-payer systems with different incentive structures, and philanthropic endowment models designed to generate returns that sustain the work beyond the initial gift. Decentralized science, community-governed research funding coordinated through digital infrastructure (such as Decentralized Autonomous Organisations), is an emerging addition to this ecosystem and improving the economics of clinical research may finally unlock its ability to scale.
07 How do we incentivize genuinely disruptive science rather than incrementalism?
Metascience, the application of scientific methods to improve science itself, has established clearly that peer review and grant funding, systematically reward incremental work by established institutions over high-variance bets by unconventional thinkers. ARPA-H represents the most promising institutional attempt to correct this, modeling the risk tolerance of venture capital with the rigor of science. Beyond funding, AI offers a genuinely novel lever: tools that systematically surface underexplored hypotheses, flag assumptions baked into dominant paradigms, and identify promising contrarian findings buried in the literature. While the default development of AI is automation of creativity, alternative development pathways exist to augment it. AI tools could help counteract the institutional gravity that pulls science toward safe, fundable, publishable questions. The remaining challenge is downstream: disruptive findings still face the same peer review gatekeepers that filtered them out in the first place, suggesting that funding reform and publication reform need to advance together.
Institutions, Systems and Coordination

Institutions, Systems and Coordination

08 How do we modernize the FDA to build a regulatory framework that is personalized, not population-averaged?
The 20th century FDA was designed for a simple world of one disease, one drug, one mechanism, one target, one population-averaged result. That world no longer reflects the state of biology. As diseases fracture into molecularly distinct subtypes, a drug that works remarkably well for 30% of patients may fail its population-level endpoint, or pass it while delivering negligible benefit to the majority. Either outcome highlights a regulatory framework mismatched to science. FDA modernization requires at least three shifts:
  1. From binary (yes/no) to conditional approval with mandatory real-world evidence collection
  2. From population-averaged endpoints to adaptive enrichment designs that identify responder subgroups during trials rather than after
  3. From generic disease categories to molecularly defined indications

None of these are radical, and in fact, versions of each exist within current FDA authorities but are chronically underused. The harder question, addressed below, is evidentiary: personalized approval requires personalized evidence, and generating that without massive trial sizes remains an unsolved problem modernization must confront honestly rather than ignore.

09 How can we accelerate development without compromising safety?
The tension between acceleration and safety can be partially resolved by sequencing. The current model demands certainty before approval, but then learns relatively little afterward. A better model lowers the bar for initial approval based on early biological signal, but raises the bar for mandatory confirmatory trials with hard endpoints, real-world evidence collection, and rapid market withdrawal when confirmatory data fails. Accelerated FDA approval pathways already embody this logic, but weak confirmatory trial enforcement has undermined and eroded public trust. Regulators using the carrot more than the stick gives the appearance that “acceleration” may simply mean a faster way for drug-makers to profit. Public skepticism is reinforced by the revolving door between FDA reviewers and industry. There are real, unsolved challenges in the balancing act between development and safety. Shorter trials mean less power to detect rare adverse events which can only be observed at scale. No approval model fully resolves this. Further, ultimately safety is a deeply personal question. Different people have different risk tolerances, yet are constrained by a single risk tolerance (FDAs). Respecting individual autonomy while protecting against corporate exploitation of patient desperation is the genuine design challenge.
10 How do we prevent AI from supercharging the existing misalignments rather than fixing them?
AI accelerates existing incentive structures. As we saw with AI scribes upcoding under the guise of improving the patient experience, even good intentions are insufficient protection when the underlying system rewards the wrong outcomes. AI’s turbocharging ability accelerates the speed asymmetry where misalignments compound far faster than institutional reforms. Early governance and prophylactic reforms are vital wherever possible. As explored above, rapid technological disruption hitting a system in late-stage collapse creates genuine political and economic conditions for reform that have not existed before. Seizing this window requires us to get specific about what gets rebuilt and in whose interest, and what objective functions we choose to optimize a new system for. These decisions will determine if US healthcare accelerates off of a cliff, or if it begins a redemptive journey of repair.

An Invitation to Change

As stated in the introduction, this roadmap is an invitation. The conclusions reached here are deliberately incomplete. It is meant as a stimulus for discussion and feedback, not a comprehensive plan. Much of it may be misguided or wrong. What's missing from current discourse is not diagnosis of the problem, which is abundant, but solution-oriented generative thinking. Ideas are too often killed by criticism before they're properly examined, when iteration and collaborative troubleshooting would serve us better. Even if AI progress stopped today, the disruption already underway will force us to reimagine how we work, live, and organize our institutions.

Meeting the moment will require us to move beyond listing grievances into a mode of generative criticism, where the question is not why an idea is wrong rather what are the positive elements to amplify and identifying how it could be different or better. In that spirit, I welcome feedback on what warrants further development in this plan or how plans could be improved, better prioritized, or more complete. A follow-up piece will incorporate this input in the months ahead.

Help Build the Roadmap

Part 1

Support & Scale AI Tools to Accelerate Cancer Cures

Part 2

Double down on the most promising areas for progress in oncology

Part 3

Tackle the Main Blockers to Medical Progress that are Limiting AI Tools

If you're a researcher, clinician, technologist, policymaker, patient advocacy organization, philanthropist, or other stakeholder with a perspective to share, we'd love to hear from you.

Your input will help us develop a practical roadmap for AI in cancer research. Sign up to access a private feedback survey and receive invitations to curated workshops where we'll bring the roadmap to life.

Thank you! We'll be in touch soon.

© 2026 Emilia Javorsky, MD, MPH, Future of Life Institute