Artificial intelligence is moving from the laboratory into the clinic, helping researchers tailor cancer treatments and uncover overlooked uses for existing medicines. Some of these technologies have already helped patients, while others remain in clinical trials or research labs, according to researchers working on the front lines of the field.
The most closely watched development comes from Moderna and Merck, which announced positive topline results from a Phase 3 melanoma trial on Aug. 19. The study tested intismeran autogene, also known as V940 or mRNA-4157, alongside Keytruda in 1,137 people with high-risk melanoma that surgeons had completely removed. The combination met its primary endpoint for recurrence-free survival and a key secondary endpoint measuring distant metastasis-free survival.
Merck and Moderna said this was the first positive Phase 3 readout for an individualized neoantigen therapy and the first positive Phase 3 result for an mRNA-based cancer therapy. The approach starts with a sample of a patient's tumor. Researchers analyze its unique mutations and use an algorithm to select targets that may help the immune system recognize the cancer. The resulting individualized therapy can encode up to 34 neoantigens, and Moderna has said the V940 program uses integrated AI algorithms during development.
Earlier results offer additional context. In a smaller Phase 2b study with longer follow-up, intismeran plus Keytruda reduced the risk of recurrence or death by 49 percent compared with Keytruda alone, and reduced the risk of distant metastasis or death by 59 percent. Those results came from a much smaller patient group, making the larger Phase 3 trial an important step forward. Still, intismeran remains investigational, and the FDA has not approved it as a melanoma treatment. The companies plan to present full findings at an international medical meeting and share them with regulators, while the study continues to track overall survival.
A different group of researchers is asking whether useful treatments already exist. Dr. David Fajgenbaum co-founded the nonprofit Every Cure to pursue that possibility. According to Every Cure's 2025 annual report, about 18,000 recognized diseases exist worldwide, and roughly 4,000 have FDA-approved medications, leaving an enormous number of diseases with limited treatment options. Every Cure uses AI to scan biomedical knowledge and look for connections between existing medicines and other diseases they might potentially treat. The organization says its system can generate tens of millions of predictions in less than a day, after which researchers examine the most promising possibilities.
The federal Advanced Research Projects Agency for Health, or ARPA-H, is backing this approach through a project called MATRIX. MATRIX uses machine learning and artificial intelligence to predict which FDA-approved drugs could potentially treat other diseases, and researchers then validate promising candidates through laboratory or clinical work. AI does not prove that a drug will work for another illness. Instead, it can help researchers decide where to look next, potentially narrowing an otherwise enormous search.
Fajgenbaum has seen firsthand what finding a new use for an existing drug can mean. Kaila Mabus developed multicentric Castleman disease at 13 and became severely ill despite chemotherapy. In 2020, her doctors tried ruxolitinib, a drug already used for certain blood disorders but not FDA-approved for Castleman disease. She began improving within months and was declared in remission in January 2021. AI did not identify her treatment, but her case shows why Every Cure wants to use AI to uncover promising drug-disease connections much faster and on a far larger scale.
At Columbia University Fertility Center, artificial intelligence has taken on a very different challenge. Researchers developed the Sperm Tracking and Recovery system, known as STAR, which combines high-speed imaging with an AI detection model and microfluidics. STAR was designed for patients with azoospermia or cryptozoospermia, conditions where sperm may appear absent or exist in extremely small numbers. The system examines a semen sample to find sperm that might otherwise be missed.
Other AI systems can search microscopic images or identify subtle biological signals that people could easily miss. Some of these technologies have already helped patients, while others remain in clinical trials or research labs. Researchers caution that it is important to separate promising science from treatments patients can actually receive today. Still, what researchers are accomplishing would have been difficult to imagine a few years ago, and the pace of development suggests AI will play an increasingly central role in how cancer and other diseases are treated.