The Next Decade in AI & Medicine:
10 Transformative Breakthroughs You Need to Know (2025–2035)

“We set out with the mission of solving intelligence and then using it to solve everything else.”
— Sir Demis Hassabis, CEO, Google DeepMind (Exchange4media, February 2026)

The Grand Finale That Isn’t

Every expedition worth its salt ends not with a full stop, but with a horizon. Think of Shackleton’s crew finally spotting dry land after months of ice and misery — not the end of the story, but the beginning of what the story made possible. That’s where we are right now with AI in science and medicine. Over the course of this eight-part series, we’ve tracked a revolution: AlphaFold cracking a 50-year-old biological mystery, AI racing drug candidates from idea to clinic in eighteen months instead of five years, neural networks reading medical scans faster and sometimes more accurately than the radiologists who trained for a decade to do it. We’ve been honest about the limitations — the algorithmic bias, the regulatory bottlenecks, the yawning equity gaps — because optimism without honesty is just marketing copy.

But now, standing at the edge of what’s already been achieved, the terrain ahead is arguably more breathtaking than anything we’ve traversed. The next decade — roughly 2025 to 2035 — is shaping up to be the period when the early proof-of-concepts get welded into the infrastructure of how humanity discovers, diagnoses, and heals. The bets placed in the lab are coming due at the clinic. The philosophical questions about who benefits, who decides, and who owns the intelligence are about to get brutally practical. And somewhere in the middle of all this, a few extraordinary things are going to happen that nobody quite predicted.

This is not a forecast of certainties. Science doesn’t work that way, and neither does technology adoption, health policy, or human behavior. What this is, instead, is a guided tour of the most credible trajectories — the near-term developments that feel nearly inevitable, the emerging frontiers that feel almost impossible, and the big-picture questions that will define whether this revolution serves everyone or just the fortunate few.

Pack accordingly.

Chapter One: The Drugs Are Coming — Slowly, Triumphantly, With Fine Print

Let’s start where the money is, because the money tells you something honest about where the field actually is versus where the press releases say it is.

In 2025, the AI-powered drug discovery sector drew $3.3 billion in venture funding — and that’s on top of headline-grabbing partnerships like Generate:Biomedicines’ $1 billion collaboration with Novartis and Isomorphic Labs’ $600 million-plus deal to integrate AlphaFold directly into drug design workflows (Drug Discovery News, 2025). The announced “biobucks” — milestone-contingent deal values — exceeded $15 billion across the year. Impressive numbers. But here’s the fine print the press releases don’t lead with: the actual upfront cash in those deals averaged about 2% of the headline figure (Drug Target Review, 2025). The industry is betting on AI’s potential, not yet paying for its performance.

That nuance matters a great deal. As of early 2026, no AI-designed drug has received full regulatory approval. What the field does have is a genuinely remarkable pipeline. Insilico Medicine’s Rentosertib — formerly known by its code ISM001-055, officially named in March 2025 by the United States Adopted Names Council — moved from AI-identified target to Phase IIa human trial in under three years, a pace that left veteran pharmaceutical researchers doing double-takes (ScienceDirect, 2025). Phase IIa results for its idiopathic pulmonary fibrosis indication published in Nature Medicine in June 2025 showed encouraging early efficacy signals in a disease with no curative therapy (ScienceDirect, 2025). A companion candidate, ISM5411, went from scratch to preclinical readiness in twelve months (Empower School of Health, 2025). Exscientia, meanwhile, reports that its AI-driven design cycles run roughly 70% faster and require ten times fewer synthesized compounds than industry norms (ScienceDirect, 2025).

So what does the next decade actually look like for this sector? Ben Liu, founder and CEO of Formation Bio, put the real bottleneck in sharp focus in a 2026 TIME interview: “The biggest problem in bringing new medicine to patients hasn’t been drug discovery for a long time” (TIME, 2026). The constraint, he argued, is clinical trials — the multi-year, multi-hundred-million-dollar gauntlets that even brilliantly designed molecules must run. Formation Bio is applying AI to accelerate administrative tasks in trials — patient recruitment, regulatory filings, drug-indication matching — claiming up to 50% reductions in trial duration. If that holds at scale, the next decade could see AI’s early-pipeline gains finally translate into the late-stage victories that would constitute an undeniable proof of concept.

The FDA, for its part, has stopped sitting on the sidelines. In January 2025 it issued landmark draft guidance titled Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products, establishing a seven-step credibility framework and mandating lifecycle maintenance plans for AI models used in submissions (Drug Target Review, 2025). In December 2025, it qualified its first AI-based tool approved for use in drug development clinical trials — a cloud-based platform helping pathologists score liver biopsies in NASH/MASH trials (Drug Target Review, 2025). These are not dramatic announcements. They are the unglamorous regulatory plumbing that will, quietly, make everything else possible.

The realistic near-term forecast: the first AI-co-designed small molecule drug receiving full approval somewhere between 2026 and 2030. Not a revolution overnight. More like a decade-long ratchet, clicking forward one clinical milestone at a time.

Chapter Two: The Hospitals Are Already Changing (Even If They Won’t Admit It)

While the pharmaceutical world plays the long clinical trial game, medical imaging has been living in a different time zone entirely. Of the nearly 1,000 AI and machine learning devices the FDA has authorized for use in healthcare, roughly 75% are deployed in medical imaging (IU Medicine Magazine, 2025). That number isn’t a projection — it’s an installed fact, present today in radiology departments from Indianapolis to Heidelberg.

Ninety percent of U.S. health systems surveyed in 2025 reported deploying AI for imaging and radiology in at least limited areas (PMC, 2025). At Level I trauma centers running AI-triage protocols, clinicians report that AI-flagged X-rays are being read 20 to 30 minutes faster on average than those arriving through standard worklist order — a margin that genuinely matters when an acute pulmonary embolism or an intracranial bleed is in the frame (IntuitionLabs, 2025). The global AI medical imaging market, estimated at $7.52 billion in 2025, is projected to reach $26.16 billion by 2030 (PMC, 2025). That is not hype arithmetic; that is existing procurement decisions playing out over a known deployment timeline.

And yet, the Philips 2025 Future Health Index found that while 85% of radiologists express optimism about AI in healthcare, 63% remain concerned about algorithmic bias, and an equal proportion worry about who bears legal liability when an AI-assisted diagnosis goes wrong (Philips, 2025). Only 59% of patients share their physicians’ enthusiasm (Philips, 2025). The technology is arriving before the social and legal frameworks designed to hold it accountable. This isn’t a crisis — it’s a pattern that has repeated across every major medical technology, from the introduction of X-rays to the widespread adoption of electronic health records. But it means the next decade’s real work in clinical AI won’t be primarily technical. It will be governance, liability law, reimbursement codes, and patient trust-building. The algorithms are ready. The institutions are catching up.

Beyond radiology, the frontier is expanding rapidly. Ambient AI scribes — tools that transcribe clinical conversations into structured notes in real time — achieved 100% adoption activity across every health system surveyed in 2025, with 53% reporting high success (PMC, 2025). Cleveland Clinic’s deployment of Bayesian Health’s sepsis detection AI achieved a 46% increase in identified sepsis cases and a ten-fold reduction in false positives (IntuitionLabs, 2025). At Mass General Brigham, physicians piloting AI scribes reported a 40% relative drop in self-reported burnout (IntuitionLabs, 2025). These are not published abstracts. They are operational outcomes from one of the most scrutinized medical systems on the planet.

The next decade will see the boundaries of clinical AI expand decisively beyond imaging: multimodal systems that synthesize imaging data with genomic profiles to tailor oncology treatment; AI-enabled voice analysis for neurological condition screening; wearable-integrated AI monitoring post-surgical patients continuously rather than episodically. The question is no longer whether AI belongs in the clinic. The question is who decides how it behaves once it’s there.

Chapter Three: The Emerging Frontiers — Where Science Gets Truly Weird and Wonderful

Now we leave the near-term and walk toward the genuinely strange. Not science fiction — science that is actively being funded, staffed, and published, but that will require another decade or two to fully flower.

Virtual Cells: Biology’s Next Grand Challenge

AlphaFold gave us virtual proteins. The next Holy Grail is the virtual cell — a fully computational replica of a living cell that can be perturbed, queried, and experimented upon without touching a single pipette. In September 2025, the Allen Institute launched its CellScapes initiative, aiming to move cellular biology “from snapshots to storylines, uncovering rules that govern how cells make decisions, transition states, and form tissues” (Allen Institute, 2025). AI researcher Kasia Kedzierska of the Allen Institute put it plainly when discussing the ambition: “People want this kind of moment for biology,” referring to the ChatGPT-scale breakthrough that might finally make the virtual cell a practical tool (Science, 2025).

The Arc Institute’s inaugural Virtual Cell Challenge in 2025, which attracted over 5,000 registrants from 114 countries and more than 1,200 teams submitting results, asked competitors to predict the effects of silencing specific genes in human embryonic stem cells — a task difficult enough that organizers openly said they didn’t expect it to be “a slam dunk” (Arc Institute, 2025). This isn’t the language of a solved problem. But it’s the language of a field that has identified the right problem, built the right infrastructure, and started the clock.

Separately, in November 2025, researchers from the Allen Institute and Japan’s RIKEN Center presented a full-scale simulation of the mouse cortex — 9 million biophysical neurons and 26 billion synapses, run on the Fugaku supercomputer — as a proof of concept for what whole-brain computational modeling can achieve (GeekWire, 2025). Extending this approach to human neuroscience is a decade-long project at minimum. But the door, as Allen Institute’s Anton Arkhipov noted, is now open.

Anti-Aging and Longevity: AI Meets Biology’s Deepest Puzzle

Perhaps nowhere is the intersection of AI and ambition more audacious than in longevity research. In May 2025, scientists at Scripps Research published results in Aging Cell showing that an AI tool identified anti-aging drug candidates, more than 70% of which significantly extended the lifespan of Caenorhabditis elegans — the workhorse model organism of aging science (Scripps Research, 2025). Co-senior author Michael Petrascheck, professor at Scripps Research, explained the significance: “This study shows that artificial intelligence can help us go beyond the traditional ‘one-drug, one-target’ mindset. By embracing the complexity of polypharmacological targeting, we were able to identify compounds that produce stronger and more reliable effects on lifespan than anything we’ve seen in previous screens” (Scripps Research, 2025).

Insilico Medicine’s Rentosertib — yes, the same AI-designed drug targeting idiopathic pulmonary fibrosis — is also being watched for its potential anti-aging applications, having been built on the identification of TNIK, a protein linked to both disease and aging processes (AiWire.net, 2025). Alex Zhavoronkov, Insilico’s founder and CEO, speaking at Bank of America’s Breakthrough Technology Dialogue in Singapore in February 2025, predicted it will become commonplace to live not just to 120, but to live well to 120 — a distinction between lifespan and healthspan that has become the defining ambition of the field (Bank of America, 2025).

Researchers from IIT-Delhi have deployed AgeXtend, an AI platform identifying geroprotective molecules that work through multiple biological aging pathways simultaneously (AiWire.net, 2025). NewLimit, co-founded by Coinbase CEO Brian Armstrong, is using machine learning to identify gene programs capable of partially reprogramming aged cells toward more youthful behavior — without erasing their identity (AiWire.net, 2025). None of these are approved therapies. All of them represent scientifically serious bets that would have been technically impossible to place five years ago.

Pandemic Preparedness: The Lesson We’re Still Learning

COVID-19 demonstrated, with terrible clarity, what happens when the scientific infrastructure for detecting, modeling, and responding to novel pathogens is inadequate. AI is being systematically woven into that infrastructure now, before the next crisis rather than after it. Real-time genomic surveillance tools that can flag novel variant emergence, AI models that can predict viral evolution and identify potential zoonotic spillover events, and federated learning platforms that can train outbreak prediction models across siloed national health datasets without sharing raw patient data — these are active research programs, not grant proposals. The next decade should see these systems mature from research tools into operational public health infrastructure, though doing so will require geopolitical cooperation that is, to put it diplomatically, not guaranteed.

Chapter Four: The Philosophical Fault Line — Who Owns the Intelligence?

Every technological revolution eventually arrives at a reckoning about power. The printing press didn’t just change literacy — it changed who controlled information and therefore who controlled culture. The internet didn’t just change communication — it concentrated advertising revenue in the hands of two or three companies while destabilizing the institutions that had previously profited from information scarcity. AI in science and medicine is not immune to this dynamic. In fact, it may be uniquely vulnerable to it.

Consider the asymmetry of access. The computational infrastructure required to train frontier biomedical AI models — the GPU clusters, the proprietary training datasets, the annotated genomic and imaging databases — is concentrated in a small number of technology giants and well-funded biotechnology companies. AlphaFold’s protein structure database is open and extraordinary; but AlphaFold was a philanthropic anomaly, not a business model. Isomorphic Labs, the DeepMind spinout, is a for-profit enterprise. The $600+ million expansion of its AlphaFold-integrated drug design capabilities is not primarily about making medicines cheap and globally accessible (Drug Discovery News, 2025).

This concentration creates a plausible but uncomfortable scenario: a world where AI genuinely accelerates the development of transformative treatments, but where those treatments are priced at levels that make them accessible only in wealthy healthcare systems. Precision oncology therapies that tailor treatment to individual genomic profiles are already among the most expensive interventions in medicine. AI-accelerated drug design could produce more of them, faster. Whether it produces them for everyone is a political question masquerading as a technical one.

Dario Amodei, CEO of Anthropic, articulated the optimistic counter-vision in his 2024 essay Machines of Loving Grace. His concept — what he calls the “compressed 21st century” — posits that powerful AI could allow humanity to make in five to ten years all the progress in biology and medicine that would otherwise have taken a full century (Amodei, 2024). The vision includes not just breakthrough therapies but their democratization: a world where AI-powered diagnostics and personalized medicine become as accessible as smartphones rather than as exclusive as private hospitals. It is an inspiring vision. It is also a vision that will not self-actualize. It requires deliberate policy choices: public funding of open-science AI infrastructure, international regulatory cooperation, differential pricing commitments, investment in AI-capable health workforces in low- and middle-income countries.

The philosophical question at the heart of this debate is genuinely deep: Is an intelligence that serves humanity most powerfully when it is open and shared, or when it is proprietary and profit-motivated to perform? The history of pharmaceutical R&D — where the vast majority of drugs are developed for markets in high-income countries while neglected tropical diseases affecting hundreds of millions of people remain chronically underfunded — should make anyone thoughtful about assuming that market incentives alone will deliver equitable outcomes.

Sir Demis Hassabis, speaking at the India AI Impact Summit in February 2026, addressed this tension with characteristic directness: “We need to embrace the incredible opportunities that AI is going to bring. I’m especially passionate about areas of science and medicine; I think it’s going to revolutionize those fields, obviously with our work on AlphaFold and other things.” He was equally emphatic about the need for humility: “This is something that we have to approach with understanding that we don’t have all the answers yet as to how this technology is going to develop and be deployed into the world” (Exchange4media, 2026).

The tension between enthusiasm and humility is not rhetorical. It is the operational condition under which every consequential decision about AI governance will need to be made in the decade ahead.

Chapter Five: The Bigger Picture — What Scientific Work Becomes

One of the questions this series has circled without landing is perhaps the most human one of all: What does it mean to be a scientist in the age of AI?

The caricature answer is “obsolescence.” The serious answer is more interesting. The history of scientific instrumentation suggests that powerful new tools don’t eliminate scientific expertise; they redirect it. The invention of the mass spectrometer didn’t make chemists unnecessary — it made chemistry capable of questions that had previously been inaccessible. The sequencing of the human genome didn’t eliminate geneticists — it created an entirely new branch of the discipline. AI is doing something analogous, but at a broader and faster scale.

What AI demonstrably does well: identifying patterns in high-dimensional data that human cognition cannot hold simultaneously; generating candidate hypotheses from the literature at a pace no individual researcher can match; predicting molecular properties with increasing accuracy; automating the administrative and analytical work that currently consumes enormous fractions of researchers’ time. What AI demonstrably does poorly: designing novel experiments that require conceptual leaps no prior data could support; exercising judgment about what questions are worth asking in the first place; navigating the ethical and social dimensions of research decisions; and — most fundamentally — wanting things. Science, at its heart, is a human activity driven by human curiosity, human values, and human stubbornness in the face of intractable problems.

The next decade will produce, almost certainly, some discoveries that are genuinely AI-led — where the computational system identifies a drug target, designs a candidate molecule, predicts its clinical profile, and routes it toward regulatory submission with minimal human intervention in the core scientific steps. Insilico Medicine is already close to this model. The question of whether such a system “discovered” something, and what that means for how we attribute scientific credit and structure scientific careers, is not resolved. But it is increasingly urgent.

For young scientists entering the field now, the practical implication is this: the competitive advantage of the next decade will not be the ability to perform analysis that AI can perform faster. It will be the ability to ask questions that AI cannot ask — questions that require moral imagination, contextual judgment, and genuine curiosity about human experience. The scientist who can partner effectively with AI tools while bringing those distinctly human capacities to bear is not threatened by this revolution. They are its most important beneficiary.

Chapter Six: The Next Decade — A Realistic Map of the Terrain

So what, concretely, should we expect?

By 2027: First regulatory approval of an AI-co-designed small molecule drug, likely in oncology or a rare disease with accelerated approval pathways. Routine AI triage integration in the majority of U.S. and European radiology departments. The first AI-assisted surgical systems achieving widespread clinical use beyond highly specialized centers. National AI-for-health strategies becoming standard policy infrastructure across G20 countries.

By 2030: Precision medicine programs incorporating AI-driven polygenic risk scoring becoming standard of care in cancer screening and cardiovascular prevention in high-income countries. The first whole-cell computational model capable of making experimentally validated predictions about drug responses at the cellular level — a watershed moment for fundamental biology. Federated AI networks enabling pandemic surveillance at the genomic level across dozens of countries, operating in something close to real time.

By 2035: The longevity field producing its first credible clinical evidence that biological aging processes can be systematically slowed in humans, not merely theorized. AI models matching or exceeding human expert performance across the full spectrum of diagnostic radiology — not just specific tasks, but the integrated clinical judgment that currently defines the specialty. And, if the most ambitious timelines hold, the first glimmers of what Dario Amodei called the “compressed 21st century” — a rate of biological and medical discovery that looks, in retrospect, like the period in which medicine changed as much as it had in the prior hundred years (Amodei, 2024).

None of these are guarantees. All of them are active bets being placed with real capital, real talent, and real patients at stake.

The Horizon That Remains

We began this series with a simple provocation: AI is quietly transforming science and medicine in ways that will affect all of us. Eight posts, tens of thousands of words, and a great many verified facts later, that provocation looks less like a hook and more like an understatement.

The transformation is not quiet anymore. It is in the clinic, in the regulatory framework, in the venture capital term sheets, in the graduate school curriculum, and increasingly in the experience of patients who are being diagnosed more accurately, routed to care more efficiently, and — cautiously, provisionally, promisingly — treated with compounds that could not have been designed without computational intelligence.

What this transformation is not is inevitable in its best form. The difference between a future where AI-accelerated medicine serves everyone and a future where it serves only those who can afford it is not a technological difference. It is a political, ethical, and institutional one. The algorithms will be capable of the former. Whether the structures we build around them deliver it is up to us.

Hassabis, reflecting on AlphaFold’s legacy at the 2026 India AI Impact Summit, said something worth holding onto: “Almost every branch of science and medicine can be impacted by AI, and we hope that AlphaFold will just be the first example of amazing advances that have been enabled by it” (Exchange4media, 2026).

AlphaFold was the first. It will not be the last. And every one of those advances will arrive with the same dual nature — extraordinary potential and real responsibility, sitting side by side, waiting to see which one we take more seriously.

The expedition continues. The horizon keeps moving. That, it turns out, is the point.

Key Takeaways

1. No AI-designed drug has yet received full regulatory approval, but the pipeline is maturing fast — the first approval is likely before 2030.
2. AI is already operational in radiology and clinical documentation at scale, with 90% of major U.S. health systems deploying it in at least limited imaging applications.
3. Virtual cell modeling and AI-powered longevity research represent the next grand scientific frontiers, with serious investment and early results.
4. The equity question — who benefits from AI-accelerated medicine — is the decade’s defining governance challenge, not a secondary concern.
5. The scientist of the next decade is not replaced by AI. They are liberated to ask better questions than any AI can ask for them.

Glossary

• Geroprotectors: Substances — drugs, natural compounds, or other agents — that slow biological aging and reduce the risk of age-related diseases.
• Virtual Cell: A comprehensive computational model of a living cell capable of making experimentally validated predictions about cellular behavior.
• Polypharmacology: The simultaneous targeting of multiple biological pathways or proteins by a single drug or compound.
• Federated Learning: A machine learning approach in which AI models are trained across multiple decentralized devices or datasets without transferring raw data, protecting patient privacy.
• Compressed 21st Century: Dario Amodei’s concept (2024) describing AI’s potential to accelerate biological and medical progress so rapidly that a century’s worth of discovery could occur within a decade.
• Healthspan: The period of life spent in good health and full function, as distinguished from lifespan (total length of life).
• Biobucks: Milestone-contingent deal values in pharmaceutical partnerships — the headline number in a licensing deal that will only be fully paid if multiple clinical and commercial milestones are achieved.

Reference List

• Amodei, D. (2024, October). Machines of loving grace. Dario Amodei. https://www.darioamodei.com/essay/machines-of-loving-grace
• Allen Institute for Cell Science. (2025, September 17). Allen Institute launches CellScapes initiative to transform our understanding of how human cells build tissues and organs. https://alleninstitute.org/news/allen-institute-launches-cellscapes-initiative-to-transform-our-understanding-of-how-human-cells-build-tissues-and-organs/
• Arc Institute. (2025). Virtual Cell Challenge 2025 wrap-up: Winners and reflections. https://arcinstitute.org/news/virtual-cell-challenge-2025-wrap-up
• Drug Discovery News. (2025, October). How AI is transforming drug discovery. https://www.drugdiscoverynews.com/ai-is-transforming-drug-discovery-16706
• Drug Target Review. (2026, January). AI in drug discovery: 2025 in review. https://www.drugtargetreview.com/article/192951/ai-in-drug-discovery-2025-in-review/
• Exchange4media. (2026, February). AGI could arrive within five years: Google DeepMind CEO Demis Hassabis. https://www.exchange4media.com/digital-news/agi-could-arrive-within-five-years-google-deepmind-ceo-demis-hassabis-152176.html
• Fortune. (2026, February 11). Google’s Nobel-winning AI leader sees a ‘renaissance’ ahead. https://fortune.com/2026/02/11/demis-hassabis-nobel-google-deepmind-predicts-ai-renaissance-radical-abundance/
• Hassabis, D. (2025, CBS 60 Minutes interview). As quoted in: Google DeepMind founder says AI could cure all disease by 2035. Fanatical Futurist. https://www.fanaticalfuturist.com/2025/11/google-deepmind-founder-says-ai-could-cure-all-disease-by-2035/
• IntuitionLabs. (2025, November 6). AI in radiology: 2025 trends, FDA approvals & adoption. https://intuitionlabs.ai/articles/ai-radiology-trends-2025
• IntuitionLabs. (2025, October). AI in hospitals: 2025 adoption trends & statistics. https://intuitionlabs.ai/pdfs/ai-in-hospitals-2025-adoption-trends-statistics.pdf
• IU Medicine Magazine. (2025, Winter). How radiology is becoming a leader in adopting AI. https://medicine.iu.edu/magazine/issues/winter-2025/how-radiology-is-becoming-a-leader-in-adopting-ai
• Lawrence, R., Dodsworth, E., et al. (2025, May). Artificial intelligence for diagnostics in radiology practice: A rapid systematic scoping review. eClinicalMedicine, 83, 103228. https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(25)00160-9/fulltext
• Liu, B. (2026). As quoted in: AI could reshape clinical trials — and the business of pharma. TIME. https://time.com/7372610/ai-drug-clinical-trials/
• Ogden, A., et al. (2025, November). Leading artificial intelligence-driven drug discovery platforms: 2025 landscape and global outlook. ScienceDirect. https://www.sciencedirect.com/science/article/abs/pii/S0031699725075118
• Petrascheck, M. (2025, May). As quoted in: AI pinpoints new anti-aging drug candidates. Scripps Research. https://www.scripps.edu/news-and-events/press-room/2025/20250529-petrascheck-ai-anti-aging.html
• Philips. (2025, November 20). AI in radiology: Three keys to real-world impact. https://www.philips.com/a-w/about/news/archive/features/2025/ai-in-radiology-three-keys-to-real-world-impact.html
• PMC. (2025). Adoption of artificial intelligence in healthcare: Survey of health system priorities, successes, and challenges. JAMIA Open. https://pmc.ncbi.nlm.nih.gov/articles/PMC12202002/
• PMC. (2025). Navigating the AI revolution: Will radiology sink or soar? Japanese Journal of Radiology, 43(10), 1628–1633. https://pmc.ncbi.nlm.nih.gov/articles/PMC12479635/
• Scripps Research. (2025, May 29). AI pinpoints new anti-aging drug candidates. https://www.scripps.edu/news-and-events/press-room/2025/20250529-petrascheck-ai-anti-aging.html
• Wilczok, D. (2025). Deep learning and generative artificial intelligence in aging research and healthy longevity medicine. Aging (Albany NY), 17, 251–275. https://doi.org/10.18632/aging.206190
• Zhavoronkov, A. (2025, February). As quoted in: Longevity science: How anti-aging medicine is advancing. Bank of America. https://business.bofa.com/en-us/content/breakthrough-technology/longevity-science-advances.html
• Zhavoronkov, A., & Leung, C. Y. (2025). Engineering the future of longevity R&D: The case for AI-driven, integrated biotechnology ecosystems. Aging and Disease. doi:10.14336/AD.2025.1313

Additional Reading List

1. Amodei, D. (2024). Machines of Loving Grace. Dario Amodei’s personal essay on the positive potential of powerful AI in biology and medicine. https://www.darioamodei.com/essay/machines-of-loving-grace
   
2. Wilczok, D. (2025). Deep learning and generative artificial intelligence in aging research and healthy longevity medicine. Aging (Albany NY), 17, 251–275. https://doi.org/10.18632/aging.206190
   
3. Drug Target Review. (2025). AI in drug discovery: 2025 in review. A rigorous, data-driven assessment of the year’s milestones and remaining gaps. https://www.drugtargetreview.com/article/192951/ai-in-drug-discovery-2025-in-review/
   
4. Science (AAAS). (2025). Can AI capture the mind-boggling complexity of a human cell? https://www.science.org/content/article/can-ai-capture-mind-boggling-complexity-human-cell
   
5. PMC / JAMIA Open. (2025). Adoption of artificial intelligence in healthcare: Survey of health system priorities, successes, and challenges. https://pmc.ncbi.nlm.nih.gov/articles/PMC12202002/
   

Additional Resources

1. Google DeepMind — AlphaFold Protein Structure Database: https://alphafold.ebi.ac.uk/ — Over 200 million protein structures, freely available to researchers worldwide.
   
2. Allen Institute for Cell Science — CellScapes Initiative: https://alleninstitute.org/division/cell-science/ — Open science tools and data for the next generation of cellular biology research.
   
3. Arc Institute — Virtual Cell Challenge: https://arcinstitute.org — Home of the Virtual Cell Challenge and open computational biology tools.
   
4. FDA — AI/ML Action Plan for Medical Devices and Drug Development: https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices — The U.S. regulatory framework governing AI in healthcare.
5. Scripps Research — Petrascheck Lab (Aging Research): https://www.scripps.edu — Source of the 2025 AI-driven anti-aging drug candidate study.

JR

JR is the founder of AI Innovations Unleashed—an educational podcast and consulting platform helping educators, leaders, and curious minds harness AI to build smarter learning environments. He has 22 year of project management experience (PMP certified) and an AI strategist who translates complex tech into practical, future-focused insights. Connect with him on LinkedIn, Medium, Substack, and X—or visit him @ aiinnovationsunleashed.com.

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