Personalized Medicine Powered by AI: Tailored Treatments Based on Genetics

Personalized medicine also known as precision medicine is transforming pharmaceutical research by shifting from a “one-size-fits-all” model to tailored treatments based on an individual’s genetic makeup. Advances in genomics over the past two decades have made it possible to identify genetic drivers of disease and drug response, and now artificial intelligence (AI) is amplifying these insights. The convergence of AI and genomics promises to equip pharma companies with powerful tools to develop targeted therapies faster, improve patient outcomes, and reduce the trial-and-error in prescribing. In fact, the momentum behind this approach is evident: according to the Personalized Medicine Coalition, 34% of all new medicines approved by the FDA in 2022 were personalized therapies  a dramatic rise that underscores the growing importance of genomics-guided drugs. ¹

This article explores the evolution of personalized medicine in pharma and how AI is accelerating genomics-based R&D, with case studies, regulatory implications, and a look at the global landscape including China’s ambitious strides in AI-powered precision medicine.

Evolution of Personalized Medicine and Genomics in Pharma

Personalized medicine isn’t brand new, it's been around for a while. But thanks to recent scientific advances, it’s now becoming a big part of how new medicines are developed. Unlike traditional treatments that are designed for the "average" patient, personalized medicine focuses on tailoring treatments to each person’s unique genetic makeup and specific biological markers.

A seminal early example came in the late 1990s with the cancer drug trastuzumab (Herceptin), an antibody therapy effective only in breast cancer patients whose tumors overexpress the HER2 gene. Herceptin’s approval guided by a companion genetic test for HER2  marked the first FDA-approved therapy explicitly tailored to a genetic subgroup.² This demonstrated that understanding a patient’s genes could dramatically improve treatment efficacy, ushering in a “targeted therapy” paradigm.

Since then, the sequencing of the human genome and the advent of high-throughput technologies have yielded a trove of biomarkers and gene variants linked to disease and drug response. Oncology has led the way – today targeted therapies are available for breast, lung, colorectal cancers and more, each home in on specific molecular drivers.³

Personalized medicine isn’t limited to cancer. In areas like heart health, mental health, and more, scientists have found that our genes can affect how we respond to certain medications.This enables doctors to select more effective treatments while minimizing potential side effects. For drug companies, it also means they can create more targeted medicines for specific groups of patients, often with better success during clinical trials. In fact, many new drugs today include genetic information on their labels to guide treatment. According to the Personalized Medicine Coalition, about one in four recently approved drugs is tied to a specific genetic marker. What started as a new idea has now become a key part of how medicines are developed — and with the help of AI, it’s only going to grow.

How AI Is Speeding Up Genomics-Based Drug Discovery

Genomics gives us the biological blueprint of diseases, but making sense of this complex information isn’t easy. That’s where AI steps in. With its power to quickly analyze massive datasets, AI is transforming how we discover and develop new medicines, especially those tailored to a person’s genetic profile.

1. Finding New Drug Targets Faster

Traditionally, discovering the right gene or protein to target with a drug was like searching for a needle in a haystack. AI can scan through millions of genetic variations and other biological data in a matter of hours. This kind of analysis could take human researchers years.

Using machine learning models, scientists can now quickly spot patterns and identify genes or proteins linked to a disease. It accelerates the research process while increasing the likelihood of discovering effective therapies. Much of the current AI research in pharma focuses on using these tools to better understand diseases and find more effective targets for therapy.

2. Smarter Clinical Trials Through Patient Matching

AI is also changing how clinical trials are designed. Instead of enrolling large groups of general patients, researchers can use AI to group people by shared genetic traits or predicted responses to drugs. This allows trials to include only those who are most likely to benefit, which saves time, reduces costs, and improves outcomes.

• This strategy has given rise to innovative clinical trial designs, including basket trials, where a single drug is evaluated across multiple diseases linked by a common genetic mutation.

• Umbrella trials, which test several drugs in one disease based on patients' genetic differences
  

By matching patients more accurately with treatments, AI makes clinical trials smaller, faster, and more focused.

3. Personalized Treatments at the Bedside

AI also supports clinicians in selecting the most effective treatment tailored to each individual patient. With the large amount of data from genetic tests, medical records, and wearable devices, it is difficult for humans to process everything efficiently. AI can analyze all this information quickly and suggest the most suitable therapy.

For example, in cancer care, AI can examine a tumor’s genetic profile and recommend the drug most likely to help. This approach is being used not only in cancer but also for rare genetic conditions, immune disorders, and other complex illnesses.

The aim is to provide each patient with the right medicine based on their unique biology. This helps avoid trial-and-error prescribing, reduces side effects, and increases the chances of a successful outcome.

What This Means for the Future

Artificial intelligence is driving the shift toward precision medicine by supporting personalized care through data-informed insights. It helps researchers discover new drug targets, supports more efficient and focused clinical trials, and allows healthcare providers to offer highly personalized treatments.

This shift is already influencing how the pharmaceutical industry operates. With continued progress, AI has the potential to reshape how we understand and treat disease on an individual level.

AI-Powered Precision Medicine in Action: Real Case Studies

The integration of AI with genomics is revolutionizing how new drugs are discovered and developed. From small biotech startups to leading pharmaceutical companies, real-world examples are showing just how effective AI-powered precision medicine can be.

1. Finding a Treatment for a Rare Genetic Disease

A standout example comes from Deep Genomics, a biotech company that used AI to uncover a treatment for Wilson’s disease, a rare inherited disorder. The company trained dozens of AI models on genomic data to understand how specific gene mutations lead to disease.

In 2019, Deep Genomics used this platform to analyze over 200,000 genetic variations. It discovered that a particular mutation in the ATP7B gene didn’t just affect the protein’s function, but actually stopped its production completely something that had been overlooked before.⁴

The AI system then scanned large chemical libraries and identified a promising drug candidate, DG12P1, an RNA-based therapy designed for patients with that exact mutation. This marked one of the first times an AI system played a central role in every step  from discovery to drug design — for a rare condition. Clinical trials followed soon after, showing the potential of AI to deliver precise therapies for ultra-specific patient groups.

2. Using AI to Treat Relapsed Childhood Cancers

Another powerful example comes from a study published in Nature Medicine, where researchers focused on children with aggressive cancers that didn’t respond to standard treatments.

The team, led by First Ascent Biomedical, used an AI platform that grew tumor samples from each child, tested over 100 existing drugs on them, and used AI to match each patient with the most effective treatment.

In this pilot, five out of six children experienced strong clinical improvements, with longer periods without disease progression compared to their previous therapies. This approach removed the guesswork from treatment decisions and helped doctors quickly identify the best drug combinations based on both genetic and drug response data.

3. Big Pharma’s AI Strategy for Precision Oncology

Major pharmaceutical companies are also embracing AI to refine their R&D strategies. Roche, a global leader in cancer care, combines its drug development expertise with AI tools and vast real-world data from its diagnostic partners — including Foundation Medicine and Flatiron Health.

Roche’s system uses AI to uncover subtle disease patterns and genetic markers that may go unnoticed by human researchers. These insights help design highly targeted therapies, aligned with specific patient groups identified through genetic testing.⁵

This approach creates a continuous feedback loop: diagnostic data informs AI insights, which then guide the design of personalized drugs. The company’s pipeline now includes multiple therapies developed alongside genomic testing tools, ensuring patients receive treatments tailored to their individual biology.

Other pharma leaders like Pfizer, Novartis, and AbbVie are also investing in AI for everything from identifying cancer drug targets to improving patient recruitment for trials. Partnerships are growing too  such as Illumina’s collaboration with Tempus to train AI on massive genomic datasets. Their goal: make sure every patient battling a complex condition is matched with the best treatment based on their genetic profile.

R&D and Regulation in the Age of AI-Driven Precision Medicine

AI and personalized medicine are reshaping how drug companies approach research and development. Genomics and data science are now key in finding drug targets and selecting the right patients. To keep up, many pharma companies are upgrading their tech systems and building teams that include AI and genetics experts.

They’re also working with tech firms and developing companion diagnostics, knowing that targeted treatments often need a genetic test alongside the drug.

Regulators like the FDA and EMA are adapting too. They've supported new trial designs that use genetic markers, and over 90 FDA-approved drugs now include genetic info on their labels.

However, AI brings challenges. Regulators are focused on how to assess algorithms, ensure data quality, and prevent bias. Privacy laws and informed consent are also critical when working with genetic data.

In short, while AI offers exciting opportunities, pharma must balance innovation with responsibility, safety, and transparency.

Global Outlook: China’s Growing Role in AI-Powered Precision Medicine

AI-powered precision medicine is advancing worldwide. Countries like the U.S., UK, and members of the EU are running large-scale programs to collect genomic data from millions of citizens to support research and develop targeted treatments. Nearly 40 countries now have their own national precision medicine strategies.

Among these, China has become a major player. In 2016, China made precision medicine a national priority, investing over $9 billion — a scale far larger than similar efforts elsewhere. This funding has rapidly boosted China’s capabilities in both genomics and AI.

One of China’s strengths is its ability to generate and process massive amounts of genetic data. The Beijing Genomics Institute (BGI) operates the world’s largest DNA sequencing facility, and the China National GeneBank holds hundreds of millions of genetic samples. This provides a huge advantage for training AI systems to detect disease risks and tailor treatments.

Chinese companies are also at the forefront. Startups like iCarbonX combine DNA, health, and lifestyle data to create personalized wellness plans using AI. Others, such as WuXi NextCODE and Huawei, are building advanced cloud platforms to support national precision medicine programs.

China's biotech sector is growing fast. Local companies are now designing AI-powered drugs for cancer and rare diseases, and major tech firms like Tencent and Alibaba are developing healthcare AI tools. Some AI-designed drugs are already in Chinese clinical trials. Analysts predict the AI-genomics market in China will grow by nearly 48% annually through 2030.⁶

While challenges like data privacy and global collaboration remain, China’s scale, government support, and tech infrastructure position it as a leader in the future of personalized medicine.

For global pharma, this presents both competition and opportunity. Many Western firms are now partnering with Chinese institutions to advance genomics and AI. In the long term, these cross-border efforts could accelerate innovation and bring life-changing treatments to patients worldwide.

Conclusion

AI-powered personalized medicine is no longer just an idea — it’s already changing how drugs are developed and delivered. By helping decode complex genetic data, AI is speeding up the shift from one-size-fits-all treatments to targeted therapies that work for individual patients.

Pharma companies using AI for tasks like target discovery and patient selection are seeing better results and gaining an edge. Success stories, from rare disease drugs to AI-guided cancer treatments, show real promise.

But this progress also brings new challenges. AI tools must be properly tested, regulations updated, and patient data protected. With major players like China investing heavily, the global race is on.

In the end, combining AI and genomics is paving the way for smarter, more personal healthcare — where each treatment is tailored to a person’s unique genetic makeup.

References

Personalized Medicines Topped 25 Percent of New Drug      Approvals for Eighth Year in a Row in 2022, Accounting for 34 Percent of      New Molecular Entities Approved - The Personalized Medicine Coalition. The Personalized Medicine Coalition. https://www.personalizedmedicinecoalition.org/personalized-medicines-topped-25-percent-of-new-drug-approvals-for-eighth-year-in-a-row-in-2022-accounting-for-34-percent-of-new-molecular-entities-approved/

Jordyn Kreutzfeldt, Rozeboom, B., Dey, N.,      & De, P. (2020). The trastuzumab era: current and      upcoming targeted HER2+ breast cancer therapies. American Journal of Cancer Research, 10(4), 1045. https://pmc.ncbi.nlm.nih.gov/articles/PMC7191090/

Tan, A. C., & Tan, D. S. W. (2022). Targeted Therapies for Lung Cancer Patients With Oncogenic Driver      Molecular Alterations. Journal of      Clinical Oncology: Official Journal of the American Society of Clinical      Oncology, 40(6), 611–625. https://doi.org/10.1200/JCO.21.01626

AI. (2019). DEEP GENOMICS NOMINATES INDUSTRY’S      FIRST AI - DISCOVERED THERAPEUTIC CANDIDATE | Deep Genomics. Deep      Genomics. https://www.deepgenomics.com/news/deep-genomics-nominates-industrys-first-ai-discovered-therapeutic-candidate/

Roche granted FDA Breakthrough Device Designation      for first AI-driven companion diagnostic for non-small cell lung cancer -. (2025, April 29). BioSpace. https://www.biospace.com/press-releases/roche-granted-fda-breakthrough-device-designation-for-first-ai-driven-companion-diagnostic-for-non-small-cell-lung-cancer?utm_

Pauwels, E.,      & Vidyarthi, A. (2017). Who Will      Own The Secrets In Our Genes? A U.S. -China Race in Artificial      Intelligence and Genomics. https://www.nist.gov/system/files/documents/2018/10/19/who_will_own_the_secrets_in_our_genes_woodrow_wils

About the Author

Nurah Ekhlaque is a freelance medical writer with a Master’s in Biotechnology from Guru Ghasidas University, India. With over three years of experience, she specialises in crafting research-based, engaging content for the healthcare and life sciences sectors.
Her research experience includes working as a Research Assistant at Saarland University, Germany, and as a trainee at AIIMS, India, where she developed expertise in molecular biology techniques like immunohistochemistry and confocal imaging. In addition to writing, Nurah mentors aspiring medical writers, guiding them to create effective healthcare content.