1.1 About AICANcer Patient Advocacy Group

The rapid adoption of AI in oncology brings an unexpected hero in the fight to save millions of lives. AI holds the potential to revolutionize cancer care, accelerating drug discovery, enhancing diagnosis, and personalizing treatment — for real. However, this power comes with inherent risks: algorithmic bias, erosion of patient control and security, incomplete data, and the continuation of health inequities.

Without the active, informed voice of the patient community, AI development risks creating a critical gap between innovation and the patient: with solutions that are technically brilliant but ethically flawed, and clinically unequal.

AICANcer Patient Advocacy Group is a 501(c)(3) patient-led non-profit organization, dedicated to ensuring that AI in cancer is developed with principles of equality, integrity, empathy, and a patient-centered focus. Together we are defining the future of AI in oncology.

Our Vision

A future where every cancer patient benefits from equitable, transparent, and patient-centered AI solutions, driven by integrity and the active, informed partnership of a unified, diverse patient community.

Our Solutions

AICANcer de-risks innovation for industry and builds trust for patients through four core solutions — each designed to put the patient voice inside the AI development lifecycle, not at the end of it. Together they form a complete system: from education and guidelines to partnerships and policy.

• AI-Literate Patient Advocates: We train a highly diverse, carefully selected cohort of patient advocates. The certified advocates gain a deep, non-technical understanding of AI in general, AI development lifecycles, AI in oncology, and patient data policy and regulation. The training prepares them to challenge, inform, and partner with companies, cancer centers, and policy makers — actively shaping the future of AI in oncology from the ground up. The advocates receive an honorarium for the work AICANcer engages them in.
• AI in Cancer Explained: A knowledge library on our website that demystifies AI for patients and caregivers, giving every patient the understanding they need to ask the right questions about their care, their data, and their rights. Launching Q3–Q4 2026 at aicancerpatient.org.
• The AICANcer Patient Guidelines: Our ten-point benchmark, recommended for adoption by AI companies, cancer centers, and research institutes — built on equality, integrity, empathy, and patient-centered focus. See Part 3 of this guide for the full framework.
• Cross-Sector Partnership Programs: Securing formal MOUs with AI companies and cancer centers to move from abstract ethical commitments to demonstrable integrity. The patient is the expert in their experience, and their voice is the guide for responsible progress.
• Policy and Regulation: Representing cancer patients by ensuring we have a standing seat at the table where patient data policy and regulation in regards to AI is being discussed.

Governance

AICANcer is newly founded and led on a volunteer basis by cancer patient and tech leader, Adiba Zeito. Our Board of Directors brings together expertise in AI, entrepreneurship, venture capital, healthcare policy, law and data privacy.

• Adiba Zeito, President — Founder, AICANcer Patient Advocacy Group  LinkedIn
• Deborah Magid, Chair — Co-Founder and Managing Director, NextStar Venture Partners  LinkedIn
• Ann Moravick, Treasurer — Founder and CEO/President, Rx4good  LinkedIn
• Curtis Mo, Secretary — Partner, DLA Piper  LinkedIn
• Andrew Serwin, Member at Large — Co-Chair, Global Data Protection, Privacy and Security Practice, DLA Piper  LinkedIn

1.2 Our Founder's Story

A life shaped by passion, resilience, and reinvention — and every chapter of it led here.

From War-Torn Lebanon to Sweden

Adiba was born in Beirut, Lebanon — during the war. When she was seven years old, her family fled. They left everything they knew and started a whole new life in Sweden.

She grew up advocating for her independence in a conservative household. She learned early that you do not wait for a seat at the table — you build the table yourself. That lesson shaped everything that came after.

She built a career she loved with a fierce passion at the centre of the technology and startup ecosystem in Stockholm. In 2011, Adiba initiated and launched Innovative Sweden — a world tour exhibition that brought her to Silicon Valley where she decided to stay. She had the great fortune of being recruited as the youngest CEO in Silicon Valley Forum's over thirty-year history and she loved every minute of it.

The First Cancer — and the Second

In 2005, at 27, she was diagnosed with early-stage breast cancer. A lumpectomy, lots of radiation, antihormonal therapy, and the quiet hope that it was behind her. It wasn't. Three years later, two tiny new tumors appeared in the scarring from her lumpectomy — so they removed the whole breast and treated her with high-dose chemotherapy. Five years of clear mammograms followed, and she thought she was finally done with cancer. That she was now a survivor.

The Day Everything Changed

In 2015, Adiba was 37 and planning for IVF — finally trying to have the baby she had dreamed of for years. Before starting the IVF process the fertility clinic required a mammogram. It showed that Stage 0 cancer had developed in her other, previously healthy breast. A mastectomy was recommended. While recovering from that surgery, she was rushed to the ER with shortness of breath — where a CT scan revealed something the mammograms for years had not: spots on her spine, pelvis, and femur. Her cancer had quietly spread to her bones. Stage 4. Metastatic. Terminal. The doctors said two to three years.

Alex — The Reason She Keeps Going

Even after her Stage 4 diagnosis, Adiba still wanted to become a mother. She and her ex-husband had saved three embryos before her ovaries were removed. There was hope sitting in a fertility clinic's freezer. On October 26th, 2018 — thanks to an incredible, selfless woman who carried her boy — Adiba became a mamma.

Two Worlds. One Mission.

Adiba started working in the tech and startup world when it started becoming a thing — at the end of the 90s. For over two decades, she had a front-row seat to some of the most extraordinary technological innovations in history — in Stockholm and in Silicon Valley. She worked alongside visionaries and entrepreneurs who changed the world with innovation and relentless ambition.

And for over twenty years, she has also been a cancer patient. She knows what it feels like to be on the receiving end of a system that is brilliant in some places and deeply broken in others. She has also been a patient advocate, created awareness campaigns, raised funds for research, and sat on boards of cancer organizations such as Susan G. Komen in San Francisco, METAvivor, and as a member of the MBC Alliance.

When AI began entering oncology with serious momentum, she found herself at the intersection of two worlds that she had the unique opportunity to bridge: the world of the cancer patient who understands the stakes from the inside, and the world of the technology leader who understands how these technologies are built, what incentives drive them, and what can happen when the humans they are supposed to serve are not part of the design conversation.

2.1 The Promise — What AI Is Already Doing in Oncology

Let us start with what is real and genuinely exciting, because the case for AI in oncology is not exaggerated. It is, if anything, underappreciated by most of the people whose lives it will affect most directly.

Cancer is one of the most complex biological challenges in medicine. It does not behave the same way in every patient, every tumor type, every body, or every environment. For decades, the tools available to oncologists have been blunt instruments applied to a challenge of extraordinary precision. AI changes that — and across every area of oncology, from imaging to drug discovery to treatment planning, it is already doing so.

Imaging & Early Detection

In lung cancer detection — the leading cause of cancer-related mortality worldwide — a 2025 meta-analysis in npj Precision Oncology, Nature Portfolio reviewed 315 studies and found that AI correctly identifies cancer in 86% of cases where cancer is present, and correctly rules it out in 92% of cases where it is not — performing consistently across screening, staging, and prognosis. In plain terms: when AI reviews a lung scan, it gets it right the vast majority of the time. (Yuan et al., npj Precision Oncology, 2025) AI-integrated CT lung cancer screening is now being implemented nationally across multiple countries, with the US requiring insurance coverage for high-risk individuals. (Yuan et al., npj Precision Oncology, 2025; Mulshine & Pyenson, Frontiers in Oncology, 2026)

In pancreatic cancer — projected to become the second leading cause of cancer death in the US by 2030, with 85% of cases currently diagnosed too late for curative treatment — AI is offering genuine and recent hope. Mayo Clinic's REDMOD model, published in Gut, BMJ British Medical Journal Group (April 2026), detected pancreatic cancer on routine CT scans up to three years before clinical diagnosis, identifying 73% of prediagnostic cancers a median 16 months early — nearly doubling specialists' detection rates without AI. (Mukherjee et al., Gut, BMJ Group, April 2026; DOI: 10.1136/gutjnl-2025-337266)

In digital pathology, third-generation AI algorithms analyze whole slide images to identify cancer subtypes and guide treatment selection. In April 2025, Roche received the first-ever FDA, US Food and Drug Administration Breakthrough Device Designation for a computational pathology companion diagnostic — the VENTANA TROP2 RxDx Device for non-small cell lung cancer. This AI analyzes stained tissue slides and computes a quantitative TROP2 protein score that determines whether a patient is likely to benefit from Datroway (datopotamab deruxtecan-dlnk). It is currently on an expedited FDA, US Food and Drug Administration review pathway, not yet fully approved for clinical use. (Roche press release, April 29, 2025) Notably, no audited demographic breakdown of the training data has been published.

Drug Discovery & Repurposing

Traditional drug development takes 10–15 years, costs approximately $2 billion, and succeeds only 10% of the time. AI is compressing that timeline dramatically. AlphaFold — Google DeepMind's protein structure prediction AI, awarded the Nobel Prize in Chemistry in 2024 — predicted the 3D shapes of over 200 million proteins, unlocking drug targets previously invisible to researchers. This foundational work, published in Nature in 2021, is now enabling researchers to design molecules that interact with cancer-specific targets with unprecedented precision. (Jumper et al., Nature, 2021)

Beyond new drug discovery, AI is also unlocking existing medicines. EveryCure — a nonprofit co-founded by Dr. David Fajgenbaum at the University of Pennsylvania — uses its AI-powered MATRIX platform to scan over 66 million drug-disease pairs to identify new uses for already-approved medicines. In February 2026, ARPA-H, Advanced Research Projects Agency for Health selected EveryCure to receive up to $76 million in a new three-year phase of funding to advance its most promising drug repurposing opportunities into preclinical and clinical validation — building on an earlier $48.3M ARPA-H, Advanced Research Projects Agency for Health award that funded development of the MATRIX platform itself. EveryCure has already advanced repurposed treatments for rare cancers including angiosarcoma. (everycure.org)

In 2025, AI identified 307 validated synergistic drug combinations for pancreatic cancer alone — one of the most treatment-resistant cancers we know. (Pourmousa et al., Nature Communications, 2025)

Precision Treatment & Dosing

AI models are moving oncology toward what the NCI, National Cancer Institute now defines as the 2026+ standard: the right patient, the right target, the right drug, the right dose, the right treatment sequence. AI is resolving 61% of variant discrepancies between pathologists interpreting the same genomic data. (Suehnholz et al., Cancer Discovery, AACR, 2024) Digital twins — AI-generated models of individual patients — are achieving 17% radiation dose reductions for glioblastoma patients: less toxicity, same efficacy. (Chaudhuri, Hormuth et al., Frontiers in AI, 2023) Tempus published a study in JCO Precision Oncology demonstrating that its advanced features including tumor-normal matching and RNA sequencing identified actionable findings in 12% of patients missed by standard testing — and launched a first-of-its-kind pan-cancer algorithm using RNA expression data to expand access to PARP inhibitor therapy beyond patients identified by traditional DNA testing alone. (Tempus AI / JCO Precision Oncology, May 2026) On May 31, 2026, Tempus launched Tempus Preview — delivering initial genetic mutation predictions including MSI-H, EGFR and FGFR fusions within 24 hours of tissue sample receipt, using the Paige Predict AI model. For patients waiting for genomic sequencing results that currently take days or weeks, this closes a critical gap between diagnosis and treatment decisions. (Tempus AI, May 2026) The SCORPIO model — developed by Mount Sinai and Memorial Sloan Kettering — predicts immune checkpoint inhibitor outcomes across 21 cancer types using only routine blood tests, without requiring expensive genomic testing. (Yoo et al., Nature Medicine, January 2025)

Clinical Trial Matching

80% of clinical studies fail to meet their enrollment goals. One in three Phase III trials are terminated due to poor enrollment, and today only 7% of eligible cancer patients enroll in clinical trials. The RECTIFIER study (Unlu et al., JAMA, February 2025) demonstrated that AI-assisted clinical trial prescreening nearly doubled enrollment rates and reduced time-to-eligibility from 50 to 15 days. HopeLLM, an AI clinical trial matching tool, is already in production at City of Hope. Tempus Next establishes a new category of real-time clinical intelligence, raising biomarker testing rates for ALK, EGFR and PD-L1 across six cancer scenarios — directly addressing one of the most critical access gaps in oncology. (Tempus AI, May 2026)

Continuous Monitoring

AI-powered monitoring tools — from wearables to smart pills to implanted biosensors — are generating continuous streams of patient data in real time. These tools can detect treatment complications earlier, track tumor response between appointments, and in theory provide a far richer picture of how a patient is actually doing than a quarterly clinic visit ever could. How that data is collected, consented to, secured, and used for AI training are questions that do not yet have adequate answers — which is precisely why they are addressed directly in the AICANcer Patient Guidelines.

Multimodal AI — The Full Picture of a Patient

In oncology, physicians routinely draw on multiple sources simultaneously — CT scans, MRI, PET imaging, pathology slides, genomic sequences, and clinical records — to reach a diagnosis or treatment decision. Multimodal AI mirrors this approach, integrating all of these data types into a single model to improve diagnostic accuracy, treatment selection, and outcomes prediction in ways that single-source AI simply cannot match.

A comprehensive review published in Briefings in Bioinformatics (Oxford Academic, January 2025) — covering 651 articles — describes multimodal deep learning as "a cornerstone of precision oncology." Studies consistently demonstrate that multimodal AI enhances accuracy and interpretability across disease subtyping, survival prediction, and treatment response assessment — tasks that matter most to the patient sitting in the oncologist's office. The literature confirms that AI models leveraging multiple data sources are particularly well-suited to the reality of clinical oncology, where no single data type tells the whole story. (Yang et al., Briefings in Bioinformatics, 2025; DOI: 10.1093/bib/bbae699)

Multimodal AI also carries important implications for the AICANcer Patient Guidelines. When genomic data, imaging, clinical history, and real-world data are fused together, the re-identification risk increases and consent implications multiply — making Guideline #4 (Data Privacy) and Guideline #6 (Informed Consent) most urgently relevant.

2.2 The Numbers That Define This Moment

Every statistic below is attributed to its real source — so you can cite them with confidence and trace them independently.

2.3 The Risk If We Get This Wrong

The same capabilities that make AI so powerful in oncology are the capabilities that make getting it wrong so dangerous. This is not a theoretical challenge. It is already happening.

Bias at Scale

The Testing Gap

The evidence that AI amplifies racial disparities in healthcare is no longer a single study — it is a confirmed pattern. In 2019, a landmark study in Science (Obermeyer et al.) demonstrated that a widely-deployed clinical algorithm — used by hospitals and insurers to manage care for about 200 million patients annually — was systematically biased: Black patients assigned the same risk score were considerably sicker than White patients. The algorithm used healthcare costs as a proxy for health need, but because less is historically spent on Black patients, it falsely concluded they were healthier. Remedying the bias would have increased the proportion of Black patients receiving additional care from 17.7% to 46.5% — a reduction of more than half. A 2024 systematic review of 30 studies confirmed a significant association between AI utilization and an exacerbation of racial disparities across health and healthcare outcomes (KFF, Kaiser Family Foundation, 2024). A 2024 University of Michigan study (PLOS Global Public Health) found that medical testing rates for white patients are up to 4.5% higher than for Black patients with identical age, sex, medical complaints and triage scores — bias baked directly into AI training data. And a 2025 Cedars-Sinai study (npj Digital Medicine) found that most large language models made dramatically different treatment recommendations for the same condition depending on a patient's race. These are not legacy failures. They are happening with the generation of AI now being piloted in oncology.

A comprehensive systematic review — The Algorithmic Divide — published by Mayo Clinic researchers in the Journal of Racial and Ethnic Health Disparities (December 2024) documented AI-driven racial disparities across healthcare, confirming that the diagnostic and treatment gap is largest precisely for the populations who already face the greatest barriers to cancer care.

The Privacy Illusion

The assumption that de-identified health data is private is increasingly unsupported by evidence. Research has demonstrated that 99.98% of Americans could be correctly re-identified in any dataset using just 15 demographic attributes (Rocher, Hendrickx & de Montjoye, Nature Communications, 2019) — and that was before the current era of wearables, continuous biomarker monitoring, and AI-powered data linkage. In 2026, a single medical record sells for 10 to 20 times more than a stolen credit card number on the dark web (Kroll Data Breach Outlook, 2025), and healthcare was the most breached sector of any industry in 2024, accounting for 23% of all breach cases handled by Kroll — up from 18% in 2023.

Deskilling

A concerning pattern is also emerging in clinical settings: the first documented evidence of AI-related deskilling. When clinicians rely heavily on AI recommendations over time, research shows they can begin to lose independent diagnostic skills (Budzyń, Mori et al., Lancet Gastroenterology & Hepatology, 2025). Physicians shown erroneous AI recommendations achieved 73% diagnostic accuracy versus 85% in a control group without AI.

The Consumer AI Trap

A separate and equally urgent challenge is the consumer AI landscape. 230 million people ask ChatGPT a health-related question every week (ARISE 2026). Cancer patients are among them — using AI tools to understand their diagnosis, research treatment options, and prepare for appointments. Used thoughtfully, this can be genuinely valuable: AI can help a patient generate a list of questions they never would have thought to ask their oncologist. Used without awareness of its limitations, it carries real risk. Research shows patients cannot distinguish between a doctor's response and an LLM's response, and are equally likely to follow potentially harmful advice as sound advice (Shekar et al., NEJM AI 2025;2(6)). The solution is not to stop using these tools. It is to understand what they are good for — and what they are not.

The Core Tension

AI in oncology is an unexpected hero in the fight against cancer. It holds real, transformative potential to save lives that would otherwise be lost.

It also holds the potential to replicate and amplify every inequality that already exists in cancer care — at scale, at speed, and invisibly.

Both of these things are true simultaneously. The question is not whether to embrace AI in oncology. The question is whether we have the courage and the commitment to ensure it works for every patient — not just the ones whose data happened to be in the training set. That is what the AICANcer Patient Guidelines are built to ensure.

The AICANcer Patient Guidelines are the foundational document of our work. They represent two years of deep research, expert consultation, and patient experience distilled into ten specific, actionable benchmarks. These Guidelines move beyond vague ethical language — they name what we recommend, why it matters, and what it looks like in practice.

4.1 Equality & Integrity — Solutions in Action

Diverse Training Data (Guideline #1)

• NCI, National Cancer Institute Synthetic Data Initiative (2024): Computer-generated patient data specifically designed to fill demographic gaps for understudied communities — nci.nih.gov
• International Health Cohorts Consortium (IHCC): 69 cohorts, 34 million+ people from Africa, Asia, Australia, North and South America, and Europe — ihccglobal.org
• AFRICAI Repository (MICCAI 2024): Publicly available cancer imaging datasets from the African continent — MICCAI 2024 Open Data
• iOncology.ai (AIIMS New Delhi + CDAC Pune): Deep learning cancer image analysis trained on 500,000 images from Indian patient populations, validated in district hospitals across India — aiims.edu

Proactive De-Biasing (Guideline #2)

• Pfizer Ethicara: Internal AI bias review tool that audits all machine learning models for demographic bias at the design stage — before deployment, not after — pfizer.com
• STANDING Together Consensus (The Lancet Digital Health, 2025): International consensus recommendations for tackling algorithmic bias and promoting transparency in health datasets
• FUTURE-AI Framework (BMJ 2025): 30-criteria international consensus framework for fair and unbiased clinical AI — BMJ 2025;388:e081554 (DOI: 10.1136/bmj-2024-081554)

Access Equality (Guideline #3)

• Tempus AI: AI-driven clinical trial matching now deployed at community oncology centers, not only academic medical centers — tempus.com/oncology
• ARGO (African Research Group for Oncology): NCI, National Cancer Institute-recognized consortium with 28 institutions in Nigeria building Africa-specific oncology AI data infrastructure — argo-research.org
• NIH DSI-Africa / WASHA Takwimu: Hub-and-spoke data science training across Ghana, Nigeria, Tanzania, and Uganda — nih.gov

Data Privacy (Guideline #4)

• Cancer AI Alliance (CAIA): $40M federated AI infrastructure — Dana-Farber, Fred Hutch, Memorial Sloan Kettering Cancer Center, Johns Hopkins; supported by AWS, Microsoft, NVIDIA, Google — canceralliance.ai
• EUCAIM (EU Cancer Images): EU-wide cancer imaging infrastructure with federated architecture — data stays within home institutions — cancerimage.eu
• NCI, National Cancer Institute Federated Learning Pilot (2024–2025): Testing AI models predicting chemotherapy response without patient data leaving its institution — nci.nih.gov

4.2 Transparency & Consent — Solutions in Action

Explainability (Guideline #5)

• Memorial Sloan Kettering Cancer Center Responsible AI Governance: Published governance framework including Model Information Sheets, AI Registry, and governance committee — first published RAI governance at scale in oncology (Nature npj Digital Medicine, Nature Portfolio, 2025) — Stetson et al., npj Digital Medicine, 2025
• CHAI, Coalition for Health AI Responsible AI Framework: Comprehensive guidance for health systems on transparency requirements, model documentation, and patient communication — chai.org
• Ferrum Health — Real-World Evidence: Validates AI tools against each hospital's actual patient population: Algorithm output + Baseline characteristics + Clinical ground truth. Over 80% of FDA, US Food and Drug Administration-cleared AI tools underperform in real-world deployment — Ferrum Health catches this gap — ferrumhealth.com

Informed Consent (Guideline #6)

• ASCO, American Society of Clinical Oncology AI Task Force Principles (May 2025): Six guiding principles including explicit consent requirements — ASCO AI Task Force Principles
• FDA, US Food and Drug Administration Predetermined Change Control Plans (Dec 2024): Finalized guidance establishing that AI training data use should be documented before deployment — FDA Predetermined Change Control Plans Guidance (Dec 2024)
• Council of Europe Framework Convention on AI (2024): World's first legally binding international AI treaty — Council of Europe — Framework Convention (CETS 225)

Accountability (Guideline #7)

• EU AI Act (Healthcare provisions August 2026): High-risk classification of healthcare AI — data quality, bias analysis, human oversight, post-market monitoring, liability documentation — digital-strategy.ec.europa.eu
• FDA, US Food and Drug Administration AI/ML SaMD Lifecycle Management (2025): Developer accountability for post-market performance monitoring and change control — fda.gov
• GAO AI Accountability Framework: US Government Accountability Office framework addressing governance, data, performance, and monitoring — gao.gov

4.3 Empathy & Human-Centered Outcomes — Solutions in Action

Quality of Life & PROs (Guideline #8)

• AICANcer's Vision: Real-Time Voice-Reported Patient Experience: A voice-based wearable AI companion that lets cancer patients talk about their experience continuously — no numbers, no scales, no clipboard surveys. Data stays in the clinical record. We call on the technology and healthcare industry to build this on the patient's terms — aicancerpatient.org
• ASCO, American Society of Clinical Oncology CancerLinQ: Collecting patient-reported data alongside clinical data to build the infrastructure for PRO-validated clinical AI — CancerLinQ (ConcertAI)
• FDA, US Food and Drug Administration Patient-Focused Drug Development Guidance: Requires patient experience data in medical product development — FDA Patient-Focused Drug Development Guidance

Human Oversight (Guideline #9)

• Memorial Sloan Kettering Cancer Center AI Governance Committee + AI Registry: Reviewed 33 live clinical nomograms, sunset 2 due to evolved evidence, documented override protocols. AI project intake up 63% in 2024 — mskcc.org
• CMS (Centers for Medicare and Medicaid Services) WISeR Model (January 2026): Six-year pilot using AI for prior authorization across 17 outpatient services in 6 states. Does not yet cover oncology, but sets the precedent AICANcer is tracking closely. We recommend a patient appeal framework before AI enters any oncology coverage decision — cms.gov

Patient Feedback (Guideline #10)

• AICANcer Cross-Sector MOU Program: Formal partnership agreements with AI companies and cancer centers that make the Patient Feedback Loop a practical commitment — aicancerpatient.org
• NCCN, National Comprehensive Cancer Network Policy Summit on DEI in Cancer Workforce (Sept 2024): Establishing policy framework for community-inclusive AI development — NCCN Clinical Practice Guidelines

4.4 The Global AI Governance Landscape

AI governance in healthcare is a global conversation. From Seoul to São Paulo, from Nairobi to Brussels, governments and health bodies on every continent are establishing frameworks that shape how AI is built, deployed, and governed in healthcare. Here is the current global picture.

United States

• FDA — AI/ML Software as Medical Device (SaMD) Lifecycle Management Guidance (Draft, January 2025): First comprehensive total-product-lifecycle recommendations for AI medical devices — covering design, development, validation, transparency, and post-market performance monitoring. Over 1,200+ AI-enabled medical tools cleared as of 2026. Still in draft as of June 2026 — fda.gov
• HHS AI Strategy (December 2025): Department of Health and Human Services strategy integrating AI across all HHS operations — FDA, CMS, CDC, NIH — with emphasis on governance, responsible use, workforce readiness, and equity principles. CMS launched its Strategic Framework 2026–2031 positioning it as an "AI-first" organization — hhs.gov
• White House National Policy Framework for AI (March 2026): Calls for a unified federal approach to AI regulation. Current federal direction is innovation-first and deregulatory — the Biden AI Executive Order was rescinded January 2025, and the current administration is seeking to preempt some state AI laws. No comprehensive federal healthcare AI legislation passed as of June 2026 — whitehouse.gov
• State-Level Activity — The Most Active Governance Layer in the US: In 2025, 47 states introduced more than 250 healthcare AI bills — 33 became law in 21 states. In 2026, 43 states have already introduced over 240 bills. Consistent themes: patient disclosure and consent, clinician accountability, prohibition of AI making independent diagnoses, payer use of AI in prior authorization. Key laws: Texas TRAIGA (January 2026) — mandatory AI disclosure to patients; California AI Transparency Act SB 942 (January 2026); Colorado SB 24-205 (June 2026) — first broad high-risk AI statute in the US; Illinois — prohibits AI from making independent therapeutic decisions (effective August 2025)
• CMS (Centers for Medicare and Medicaid Services) WISeR Model (January 2026): First federal AI deployment in healthcare coverage decisions — six-year pilot across 17 outpatient services in 6 states — cms.gov
• NIST AI Risk Management Framework: Voluntary standards and technical frameworks shaping AI governance through stakeholder-driven processes. NIST's Zero Drafts pilot accelerating standards for AI dataset documentation, testing, and transparency — nist.gov
• CHAI (Coalition for Health AI) + Joint Commission RUAIH Certification (June 2026): First-of-its-kind voluntary Responsible Use of AI in Healthcare certification for US health systems — five pillars: governance, data management, risk and bias reduction, safety monitoring, and transparency — chai.org / jointcommission.org
• ASCO, American Society of Clinical Oncology AI Task Force Principles (May 2025): Six guiding principles for responsible AI in oncology from the world's leading oncology professional organization — asco.org

United Kingdom

• MHRA National Commission into the Regulation of AI in Healthcare (June 2026): Landmark public consultation (760 contributors) confirming public demand for ongoing monitoring, transparency, accountability, human oversight, and patient voice at the structural center of AI governance. All ten AICANcer Patient Guidelines activated. Final Commission recommendations due summer 2026 — gov.uk/MHRA

European Union

• EU AI Act — Healthcare Provisions (August 2026): Healthcare AI classified as high-risk — triggering mandatory data governance, bias analysis, human oversight, post-market monitoring, transparency, and liability documentation. Approximately 75% of commercial healthcare AI will be treated as high-risk. Conformity assessment for medical devices extended to August 2027–2028 via the Digital Omnibus package. AI literacy obligation in effect from August 2026 — digital-strategy.ec.europa.eu
• European Health Data Space (EHDS): Cross-European health data infrastructure with FHIR/OMOP standards, federated architecture, and consent built in — health.ec.europa.eu

Australia

• Therapeutic Goods Administration (TGA) — AI Medical Device Guidance (February 2026): New guidance clarifying when AI-enabled software qualifies as a regulated medical device. Key principle: if software uses AI to influence clinical decisions, it falls within the regulatory framework. Mandatory guardrails proposed for AI in high-risk settings — tga.gov.au

Canada

• Health Canada — Machine Learning-enabled Medical Device (MLMD) Guidance (February 2025): Clear rules for Predetermined Change Control Plans allowing AI models to update within agreed guardrails without requiring a new licence each time. January 2026: expanded powers to impose Terms and Conditions on medical device licences for post-launch risk monitoring. No federal AI statute as of June 2026 — provincial initiatives advancing — canada.ca

South Korea

• AI Basic Act (effective January 2026): First comprehensive national AI legislation in Asia — risk-based framework classifying healthcare as high-impact, requiring risk assessments, disclosure obligations, human oversight, and bias monitoring. South Korea became the first Asian country with a comprehensive AI governance law — korea.kr

Japan

• AI Promotion Act (May 2025) + Healthcare Data Reforms (2025–2026): Innovation-first, principle-based approach — lighter than the EU and designed to accelerate adoption while shaping behavior. Parallel tracks: amendments to the Personal Information Protection Act and a dedicated Software as Medical Device (SaMD) framework advancing in 2026. Aligns with the Hiroshima AI Process and international codes of conduct — japan.go.jp

Brazil

• ANVISA + Brazilian AI Plan 2024–2028: Brazil's health regulator ANVISA applies risk-based rules to AI-enabled software as a medical device under Resolutions 657/2022, 751/2022, and 830/2023. The national Brazilian AI Plan 2024–2028 steers health-focused AI initiatives. Privacy governance via the LGPD (Lei Geral de Proteção de Dados Pessoais). No AI-specific statute yet — Bill No. 2,338/2023 under ongoing debate — anvisa.gov.br

Middle East

• UAE & Saudi Arabia: Gulf states investing approximately $100 billion annually by 2026 into AI infrastructure. Saudi Arabia's HUMAIN initiative builds sovereign cloud infrastructure keeping sensitive health data within national borders. The UAE's Falcon model is a cornerstone of global open-source AI. Both countries enforce strict data localization laws — health data governed locally. GCC accounts for over 60% of Middle East and Africa AI healthcare spend — uae.gov

Africa

• South Africa Draft National AI Policy (2026): Central vision — AI for inclusive economic growth. Pillars include ethical and inclusive AI, diverse datasets, ethical oversight, human rights assessments, disability inclusion, and responsible AI for healthcare. Note: the initial draft was withdrawn in April 2026 after AI-hallucinated citations were discovered in its reference list — a striking real-world example of the transparency and accountability failures AICANcer advocates against. New version in development — dcdt.gov.za
• Nairobi AI Forum 2026: Highlighted Africa's shift from AI consumer to AI creator — with a focus on sovereign AI, healthcare impact, and democratization of access across the continent

Global Bodies

• Council of Europe Framework Convention on AI (CETS No. 225, 2024): The world's first legally binding international AI treaty — enshrining human rights, transparency, and accountability as legal obligations — coe.int
• UN General Assembly Resolution on AI (March 2024 — unanimous, 193 states): First UN consensus on AI governance with human rights as the foundation — un.org
• WHO Global Initiative on AI for Health (GI-AI4H): Working to harmonize global regulatory standards for health AI across 193 member states — equity, inclusion, human rights, and privacy at the forefront. New Global Strategy on Digital Health and AI 2026–2030 in development — who.int
• International Consensus Framework (ICF) — New AI Principle (June 2025): Six leading international organizations representing patients, physicians, pharmacists, nurses, hospitals, and the pharmaceutical industry adopted the first joint ethical principle on responsible use of health data and AI — ifpma.org

Organization	Role / What They Do	Website	Guidelines
Tempus AI	Precision medicine platform named to TIME's 10 Most Influential Health and Life Science Companies of 2026. Tempus Next — a new category of real-time clinical intelligence — raised ALK, EGFR and PD-L1 biomarker testing rates across six cancer scenarios. Hub platform upgraded with agentic AI for clinical workflows. FDA approved Tumor Only xT CDx. Tempus Preview launched May 31, 2026 — delivers initial genetic mutation predictions within 24 hours of tissue sample receipt, closing the critical gap between test order and actionable insights. (Tempus AI, May 2026)	tempus.com/oncology	#1 #3 #10
Flatiron Health	Oncology-specific real-world evidence and EHR data — feeds real-world data into AI training pipelines	flatiron.com	#1 #2
PathAI	AI-powered pathology diagnostics for cancer — training data demographics are EIA-critical	pathai.com	#1 #2 #5
Paige AI (Tempus 2025)	Computational pathology AI — world's largest oncology AI foundation model post-merger	paige.ai	#1 #2 #5
ConcertAI	Real-world oncology data and clinical AI — RWE for oncology trials	concertai.com	#1 #4 #10
Lunit	AI biomarker analysis for oncology trials — pathology and radiology AI	lunit.io	#1 #2 #5
EveryCure	Nonprofit — MATRIX AI platform scanning 66M+ drug-disease pairs. $76M ARPA-H, Advanced Research Projects Agency for Health funding (Feb 2026). Breast cancer program active.	everycure.org	#3 #8

Organization	Role / What They Do	Website	Guidelines
Memorial Sloan Kettering	Published Responsible AI governance framework — AI Registry, Model Info Sheets, governance committee (npj Digital Medicine, Nature Portfolio, 2025)	mskcc.org	#7 #9 #10
Dana-Farber Cancer Institute	Co-founder Cancer AI Alliance (CAIA) — federated learning infrastructure	dana-farber.org	#4 #9
Mayo Clinic	REDMOD — AI detecting pancreatic cancer up to 3 years early (Gut, BMJ British Medical Journal Group, April 2026); Algorithmic Divide systematic review (2026)	mayoclinic.org	#1 #2 #5
Johns Hopkins / Sidney Kimmel	CAIA co-founder — precision oncology and AI governance research	hopkinsmedicine.org	#1 #4 #7
Moffitt Cancer Center	NCI, National Cancer Institute Federated Learning Pilot — AI predicting chemotherapy response without data leaving institution	moffitt.org	#4

Organization	Role / What They Do	Website	Guidelines
Pfizer	Ethicara — internal AI bias review tool auditing all ML models at design stage before deployment	pfizer.com	#1 #2
AstraZeneca	AI integration in oncology R&D — computational pathology data at ASCO, American Society of Clinical Oncology 2025	astrazeneca.com	#1 #5
Roche / Genentech	VENTANA TROP2 RxDx Device — first-ever Breakthrough Device Designation for computational pathology companion diagnostic for NSCLC (April 2025). In accelerated review, not yet fully approved.	roche.com	#1 #5 #7
Epic Systems	Cosmos Foundation Model — 350M real-world clinical records. AICANcer recommends a published demographic audit.	epic.com	#1 #2 #4

Organization	Role / What They Do	Website	Guidelines
Coalition for Health AI (CHAI, Coalition for Health AI)	Primary US institutional reference for responsible healthcare AI — co-authored Joint Commission guidance	chai.org	#1–#10
The Joint Commission	On June 1, 2026, Joint Commission launched the first-of-its-kind Responsible Use of AI in Healthcare (RUAIH) certification — a voluntary program recognizing US healthcare organizations that demonstrate governance, safeguards, monitoring, and education for responsible AI use. Organized around five areas: governance, data management, risk and bias reduction, safety monitoring, and transparency. Building on its 2025 initial guidance developed with CHAI, the certification directly validates the framework the AICANcer Patient Guidelines are built on.	Joint Commission RUAIH Certification	#1–#10
ASCO, American Society of Clinical Oncology	AI Task Force Principles for Responsible AI in Oncology (May 2025) — CancerLinQ PRO data infrastructure	asco.org	#2 #5 #7 #9
NCCN, National Comprehensive Cancer Network	Policy Summit on DEI in Cancer Workforce (Sept 2024) — framework for community-inclusive AI development	NCCN Clinical Practice Guidelines	#1 #3 #10
MHRA — Medicines and Healthcare products Regulatory Agency (UK)	June 2026: landmark public consultation (760 contributors) confirming patient voice, transparency, accountability and human oversight as non-negotiable public demands. All ten AICANcer Patient Guidelines activated. Final Commission recommendations due summer 2026.	gov.uk/MHRA	#1–#10
Council of Europe	Framework Convention on AI (CETS No. 225, 2024) — world's first legally binding international AI treaty	coe.int	#4 #5 #6 #7

Organization	Role / What They Do	Website	Guidelines
Ferrum Health	Platform for hospitals to continuously validate and monitor clinical AI performance against their own patient population — vendor-neutral ground truth on any FDA-cleared, CE-marked, or homegrown model. Founded by Pelu Tran after a personal family tragedy involving undeployed cancer detection AI. 43% of FDA-approved AI devices validated without real patient data.	ferrumhealth.com	#2 #7 #9 #10
Cancer AI Alliance (CAIA)	$40M federated AI infrastructure — Dana-Farber, Fred Hutch, Memorial Sloan Kettering Cancer Center, Johns Hopkins; AWS, Microsoft, NVIDIA, Google	canceralliance.ai	#4 #9

Organization	Role / What They Do	Website	Guidelines
EUCAIM	EU-wide cancer imaging infrastructure — federated architecture, data stays within home institutions	cancerimage.eu	#1 #3 #4
IHCC	International Health Cohorts Consortium — 69 cohorts, 34M+ people from 5 continents	ihccglobal.org	#1 #3
ARGO	African Research Group for Oncology — NCI, National Cancer Institute-recognized, 28 institutions in Nigeria building Africa-specific oncology AI data	argo-research.org	#1 #3
AFRICAI Repository	Publicly available cancer imaging datasets from the African continent (launched MICCAI 2024)	MICCAI 2024 Open Data	#1 #3

6.1 ARISE State of Clinical AI 2026 — Key Findings

The ARISE State of Clinical AI 2026 (Brodeur et al., Stanford/Harvard, January 2026) is the most comprehensive annual synthesis of clinical AI evidence, reviewing 131 studies across six domains. Published by Dr. Peter Brodeur (Beth Israel Deaconess Medical Center / Harvard Medical School), Dr. Ethan Goh (Stanford University), Dr. Adam Rodman, and Dr. Jonathan H. Chen — with contributions from a multidisciplinary group across Stanford, Harvard, and affiliated health systems.

• 95.5% of FDA, US Food and Drug Administration-cleared AI device summaries omitted demographic data (660 of 691 devices cleared 1995–2023) (PR) — Cahan et al., JAMA Health Forum, 2025; cited in ARISE 2026, Slide 5
• 91.3% of FDA, US Food and Drug Administration-cleared AI devices lacked bias assessments entirely (631 of 691 devices) (PR) — Cahan et al., JAMA Health Forum, 2025; cited in ARISE 2026, Slide 5
• Up to 22.2% (95% CI 21.6–22.8%) of real clinical AI cases had potentially severely harmful recommendations across 31 LLMs — 76.6% were errors of omission, the invisible failure mode (PR) — Wu, Goh et al., NOHARM benchmark, arXiv, December 2025; cited in ARISE 2026
• 230 million people ask ChatGPT a health question every week. Patients cannot distinguish AI advice from physician advice and are equally likely to follow both regardless of accuracy (PR) — ARISE 2026 / Shekar et al., NEJM AI 2025;2(6)
• Deskilling: First documented evidence that clinicians over-relying on AI begin to lose independent diagnostic skills. AI-trained physicians shown erroneous recommendations: 73% accuracy vs 85% control (PR) — Budzyń, Mori et al., Lancet Gastroenterology & Hepatology, August 2025

6.2 The Scale of the Opportunity

• $341B — Precision oncology market projected by 2035 (PR) — Research and Markets, 2025
• $106B — Precision oncology clinical trial market, projected to reach $159B by 2029 (PR) — Novotech CRO / Research and Markets, 2024–25
• 1,200+ — AI-enabled medical tools cleared by FDA, US Food and Drug Administration as of 2026 — a tenfold increase from 2020 (PR) — FDA, US Food and Drug Administration data / ARISE 2026
• 65–70 — AI-designed drug candidates currently in human trials — zero have received full FDA, US Food and Drug Administration approval yet (PR) — Wilczok & Zhavoronkov, Clinical Pharmacology and Therapeutics, 2025
• 2× — AI clinical trial prescreening doubled enrollment rates and cut time-to-eligibility from 50 to 15 days (PR) — Unlu et al. RECTIFIER, JAMA, February 2025 (DOI: 10.1001/jama.2024.28047)

6.3 The Equity Gap

• 86.5% of GWAS Catalog participants in 2023 were of European descent. By September 2024, this had risen to 94.48% — with every non-Asian, non-European population below 1% (PR) — Corpas, Popejoy, Fatumo et al., Cell Genomics, December 2024 (PMC11770215)
• 0.47% of 2023 GWAS participants were labeled "African" (continental, excluding African American and Afro-Caribbean). The paper calls this "unacceptably low" (PR) — Corpas, Popejoy, Fatumo et al., Cell Genomics, December 2024 (PMC11770215)
• 1 in 600 (0.17%) of all GWAS study participants are of Arab ancestry — despite Arabs representing 5% of the world population. Six Arab countries — Algeria, Iraq, Libya, Oman, Somalia, and Syria — have zero genomic data (PR) — Bhattacharya, Alkuraya, Fahed et al., Genome Medicine, November 2023 (PMC10664351)
• 83% of TCGA (The Cancer Genome Atlas) patients — the major cancer-specific genomic database — are of European ancestry, compared to 6% each of African and East Asian ancestry, showing the same imbalance found in GWAS but specific to cancer genomics (PR) — Rajagopal & Olopade, Cell Reports Medicine, August 2020 (PMC7446667)
• 32% higher colorectal cancer mortality in rural Appalachia than any other US regional population — the highest cancer mortality disparity by US geography. Standard AI clinical ML models trained on national datasets systematically underperform for this population because rurality, insurance status, and employment — the most important predictors of CRC survival in Appalachia — are absent from most training data (PR) — Montgomery, Shanker et al., Scientific Reports, July 2025
• 80% of cancer patients receive care in community oncology settings, not academic centres where most AI is built (EX) — Lidia Fonseca, Chief Digital & Technology Officer, Pfizer; Jeff Legos, Chief Oncology Officer Pfizer, 2025
• 3% — Middle East and Africa's share of the precision oncology market, despite disproportionate global cancer burden — Research and Markets, 2025
• 85% of pancreatic cancer cases diagnosed too late for curative treatment — projected #2 US cancer killer by 2030 (PR) — Mukherjee et al., Gut, BMJ Group, April 2026 (DOI: 10.1136/gutjnl-2025-337266)
• 99.98% of Americans can be correctly re-identified in any dataset using 15 demographic attributes (PR) — Rocher, Hendrickx & de Montjoye, Nature Communications, 2019 (DOI: 10.1038/s41467-019-10933-3)
• 23% — Healthcare accounted for 23% of breaches handled by Kroll in 2024 — the most breached sector, up from 18% in 2023 (PR) — Kroll Data Breach Outlook, February 2025

6.4 AI Promise — The Evidence

• 86% — AI achieves 86% sensitivity in lung cancer image-based diagnosis across 315 studies (PR) — Yuan et al., npj Precision Oncology, 2025 (DOI: 10.1038/s41698-025-01095-1)
• 73% of prediagnostic pancreatic cancers identified by REDMOD a median 16 months early — nearly doubling specialists' detection rates (PR) — Mukherjee et al. / Mayo Clinic, Gut, BMJ Group, April 2026 (DOI: 10.1136/gutjnl-2025-337266)
• 17.6% higher cancer detection rate when AI supported radiologists in nationwide German mammography study — 463,094 women, 119 radiologists (PR) — Eisemann et al. (PRAIM), Nature Medicine, January 2025 (DOI: 10.1038/s41591-024-03408-6)
• 61% of genomic variant discrepancies between pathologists resolved by AI (PR) — Li et al., CancerVar, Science Advances
• 17% radiation dose reduction achieved by digital twin AI for glioblastoma patients (PR) — Chaudhuri, Hormuth et al., Frontiers in Artificial Intelligence, 2023 (DOI: 10.3389/frai.2023.1222612)
• 307 validated synergistic drug combinations identified by AI for pancreatic cancer alone in 2025 (PR) — Pourmousa et al., Nature Communications, April 2025 (DOI: 10.1038/s41467-025-56818-6)
• 66M+ drug-disease pairs scanned by EveryCure's MATRIX AI platform (PR) — EveryCure / ARPA-H, Advanced Research Projects Agency for Health, 2026
• 12% of cancer patients had actionable genomic findings identified by advanced tumor-normal matching and RNA sequencing that were missed entirely by standard testing (PR) — Tempus AI & The Oncology Institute, JCO Precision Oncology, March 2026

6.5 Expert Voices

On the State of Clinical AI — ARISE 2026

On Bias & Equity

On Innovation & Promise

6.6 Key Sources & Further Reading

ARISE & Most Recent Evidence

• Brodeur, Goh, Rodman, Chen et al. / ARISE State of Clinical AI 2026 — The most comprehensive annual synthesis of clinical AI evidence — 131 studies, six domains. ARISE 2026 — Full Report (131 slides, open access) / arise-ai.org
• Mukherjee et al. / REDMOD (Gut, BMJ British Medical Journal Group, April 2026) — AI detecting pancreatic cancer up to 3 years before clinical diagnosis. gut.bmj.com
• Yuan et al. (npj Precision Oncology, Nature Portfolio, 2025) — Meta-analysis of AI for lung cancer image-based diagnosis — 315 studies
• Eisemann et al. / PRAIM (Nature Medicine, Jan 2025) — Nationwide real-world AI mammography screening study
• Unlu et al. / RECTIFIER (JAMA, Journal of the American Medical Association, February 2025) — AI clinical trial prescreening doubles enrollment rates
• Wu, Goh et al. / NOHARM benchmark (arXiv, December 2025) — Clinical safety evaluation of 31 LLMs: up to 22.2% potential severe harm, 76.6% from omission — arxiv.org/abs/2512.01241
• Shekar, Pataranutaporn, Maes et al. (NEJM AI, 2025;2(6)) — Patients cannot distinguish low-accuracy AI medical advice from physician advice — ai.nejm.org/doi/abs/10.1056/AIoa2300015
• Subramanian, Litt, Mamtani / ASCO 2026 Abstract 9000 (Penn Medicine / Abramson Cancer Center) — Patient-facing AI resources about cancer found lacking — ASCO Post
• Santos Teles, Chimonas, Mao et al. (JMIR Cancer 2026;12:e81346) — Cancer patients' attitudes and beliefs toward AI: 49.7% concerned; comfort gradient 80.2%→61.5% — PMC12872120
• Corpas, Popejoy, Fatumo et al. (Cell Genomics, December 2024) — Genomic GWAS diversity: 86.5% European (2023), African 0.47%, rising to 94.48% European by 2024 — PMC11770215
• Bhattacharya, Alkuraya, Fahed et al. (Genome Medicine, November 2023) — Massive underrepresentation of Arabs in GWAS: 1 in 600 participants — PMC10664351
• Rajagopal & Olopade (Cell Reports Medicine, August 2020) — TCGA ancestry composition: 83% European, 6% African, 6% East Asian — PMC7446667
• Montgomery, Shanker et al. (Scientific Reports, July 2025) — Rurality outranks tumor grade in Appalachian CRC AI; 32% higher mortality gap — nature.com/articles/s41598-025-11074-y
• Cahan et al. (JAMA Health Forum, 2025) — Benefit-risk reporting for FDA-cleared AI devices: 95.5% no demographic data; 91.3% no bias assessment — JAMA Health Forum
• EveryCure / ARPA-H, Advanced Research Projects Agency for Health (February 2026) — AI drug repurposing platform — everycure.org

Governance & Policy

• ASCO, American Society of Clinical Oncology AI Task Force Principles (May 2025) — asco.org
• CHAI, Coalition for Health AI / Joint Commission Initial Guidance (2025) — chai.org / Joint Commission RUAIH Certification
• MHRA National Commission — Public Views on AI in Healthcare (June 11, 2026) — UK Government landmark public consultation; 760 contributors; foundational evidence base for UK AI healthcare regulation — gov.uk/MHRA
• EU AI Act Healthcare Provisions (2024) — digital-strategy.ec.europa.eu
• Council of Europe Framework Convention on AI (2024) — Council of Europe — Framework Convention (CETS 225)
• FDA, US Food and Drug Administration AI/ML SaMD Lifecycle Management (2025) — fda.gov/medical-devices

6.7 Patient Information & Attitudes

What Patients Find When They Search for Information About AI and Cancer

Presented at ASCO Annual Meeting 2026 (Abstract 9000) by researchers at the Abramson Cancer Center, University of Pennsylvania.

• 320 webpages and videos from Google and YouTube were screened for common cancer-and-AI search queries
• 1 in 4 results (31% of webpages; 19% of videos) were relevant to patients at all
• Of those, only 1 in 3 was high quality (33% of webpages; 23% of videos) — assessed using the validated DISCERN consumer health information tool
• Median reading level: college level — compared to the AMA and NIH recommended 6th–8th grade standard
• Only 15% of relevant resources mentioned hallucination or misinformation risk
• Most content focused on how hospitals and clinicians use AI — not on how patients can safely use AI tools themselves

Source: Subramanian, Litt, Mamtani — ASCO Annual Meeting 2026, Abstract 9000, Abramson Cancer Center, University of Pennsylvania. ASCO Post / Penn Medicine

How Cancer Patients Actually Feel About AI in Their Care

Cross-sectional survey of oncology patients at Memorial Sloan Kettering Cancer Center, September–December 2024. Published JMIR Cancer, February 2026.

• Patient comfort with AI follows a clear gradient — highest where AI is furthest from life-and-death decisions, lowest where it is closest to clinical judgment:
• 80.2% comfortable with AI in cancer screening
• 78.2% comfortable with AI for exercise guidance; 74.8% for diet
• 70.4% comfortable with AI in diagnosis assistance
• 64.8% comfortable with AI in treatment planning
• 61.5% comfortable with AI for prognosis — the lowest comfort level of all clinical touchpoints
• 49.7% of cancer patients at MSK were at least somewhat concerned about AI use in their care. Top concerns: loss of human interaction, and medical errors.

Source: Santos Teles, Bryl, Chimonas, Mao et al. — Memorial Sloan Kettering Cancer Center. JMIR Cancer 2026;12:e81346 (PMC12872120)

AICANcer's Own Work

• AICANcer Patient Guidelines — the ten-point benchmark detailed in Part 3 of this guide. Further detailed guidelines and supporting materials available on request via the contact form at aicancerpatient.org
• AI-Literate Patient Advocate Program — active now. Interested patients can apply via our application interest form
• AI in Cancer Explained — A knowledge library for patients and caregivers, will be publicly available, launching Q3–Q4 2026 — aicancerpatient.org

AI & Technology Terms

For Patient Advocates

• Become an AI-Literate Patient Advocate: AICANcer's training program gives you the knowledge to challenge, inform, and partner with companies, cancer centers, and policymakers on behalf of every cancer patient. No technical background required — you bring what no researcher or vendor can: lived experience. Our cohort is deliberately diverse across cancer type, cancer stage, race, ethnicity, geography, age, and background. We are cancer type and stage agnostic. Advocates receive an honorarium for the work AICANcer engages them in. The program is active — apply by submitting our application interest form and we will be in touch directly.
• Use AI tools wisely — and help others do the same: 230 million people ask ChatGPT a health question every week. AI can be genuinely useful for generating questions to bring to your oncologist, understanding medical terms, or finding out what clinical trials exist. What it cannot do is replace clinical judgment. Use AI to prepare for conversations with your care team. Not to replace them. AICANcer's AI in Cancer Explained knowledge library helps you navigate exactly this — launching Q3–Q4 2026 at aicancerpatient.org
• Ask your oncology team: Which AI tools are being used in your care, what were they trained on, and who is responsible if they get something wrong. These are your rights. You are allowed to ask.
• Ask about biomarker and genomic testing: If you have not had biomarker or genomic testing, ask your oncologist whether it is appropriate for your cancer type. Precision medicine — and the AI being built to power it — depends on diverse, representative data. Patients from minority communities are tested at significantly lower rates, which means their data is missing from the registries that train AI. Every patient who gets tested and contributes to research is helping to build a more equitable foundation for future cancer care. Getting tested can open doors to targeted therapies and clinical trials — and it makes the system better for everyone who comes after you.
• Share your experience: Your lived experience is the most powerful evidence for why better data collection tools are needed. When our website is fully live, you can share your story at aicancerpatient.org — or reach out to us now via the contact form

For Cancer Organizations & Foundations

• Endorse the AICANcer Patient Guidelines as part of your AI governance stance — and signal to vendors that patient-centered standards matter to you.
• Partner with us — reach out via the contact form at aicancerpatient.org

For Clinical Communities & Cancer Centers

The clinical AI market is intensely competitive — thousands of vendors need your deployment credibility. That is your leverage. Use it to set the terms.

Before signing any AI contract, consider asking for: an audited demographic breakdown of training data, an Equality Impact Assessment across patient subgroups, a Model Card, documented liability at every decision node, and evidence of validation against patient-reported outcomes — not just clinical endpoints. Validate every tool against your own patient population, not just the academic datasets used for FDA clearance.

Build an AI Registry. Establish override protocols. Engage CHAI and consider pursuing the Joint Commission's new Responsible Use of AI in Healthcare (RUAIH) certification — launched June 2026, the first voluntary AI certification exclusively designed for healthcare organizations. Cite the AICANcer Patient Guidelines alongside it. The patient voice belongs at your procurement table — and we are here to help put it there. Partner with us via the contact form at aicancerpatient.org

For AI Companies & Technology Partners

The patients your tools are designed to serve are not at the end of your development process. They should be at the beginning of it.

• How many of our Guidelines are you applying? Are you addressing Guideline #3 on access equality, #5 on explainability, #7 on accountability — or are you working toward all ten? The AICANcer Patient Guidelines are not a compliance checklist. They are a blueprint for building AI that patients can trust. Which leads us to our ask:
• Bring us in from day one — not as a checkbox, but as a design partner. The patient voice de-risks your development and builds the trust your product needs to succeed. Partner with us via the contact form at aicancerpatient.org