Healthcare

With ChatGPT, Ayrin Santoso found a way to navigate care for her mother, Fifi, who lives in the Indonesian city of Surabaya. Ayrin, a tax professional working for OpenAI in San Francisco, had already been using ChatGPT regularly, driven by a duty she feels to her aging parents: “How can I actually still be there and help them when they need something?”

After a life-threatening rare autoimmune clotting disorder left Rich Kaplan facing kidney damage, his prescribed medication stalled for months because an insurer demanded evidence the drug worked for a condition as uncommon as catastrophic antiphospholipid syndrome. Kaplan used ChatGPT to assemble a referenced synthesis of trials, case reports, and studies, paired with a plain-language note explaining what was at stake; the packet ultimately helped secure approval through third-party arbitration. Since then, he has used ChatGPT as a practical tool for navigating the ongoing complexity of chronic illness, checking potential drug interactions against his medication list, flagging risks tied to kidney disease, and translating dense medical records into clear summaries and questions for clinicians. For Kaplan, the value is agency: turning scattered information and bureaucratic friction into actionable support that helps him pursue better care and stay actively involved in his own health decisions.

Junevity is a new biotech co-founded by entrepreneur Rob Cahill, UCSF scientists Janine Sengstack and Hao Li, and veteran CEO John Hoekman, with a mission to reprogram aging cells using small interfering RNA (siRNA). Cahill’s path from software founder to bioinformatics and lab work was shaped by family experiences with aging and Parkinson’s, pushing him toward preventive, disease-modifying therapies. From day one, Junevity made AI part of its operating system: every employee gets ChatGPT, and OpenAI models augment the company’s genomics and machine-learning pipelines. The team estimates its first development candidate, a liver-targeted siRNA program for Type 2 diabetes and obesity, can reach key milestones at 2–6x lower cost and 2–3x faster timelines than typical industry norms.

Probably Genetic funds free at-home genetic testing and counseling, pairing it with an AI-driven symptom intake that converts long, free-form narratives into structured phenotype data and candidate diagnoses. For Elizabeth “Betsy” Minium and her daughter Kali, that approach ended a 31-year search marked by inconclusive labels and dead ends, culminating in a Pitt-Hopkins syndrome diagnosis tied to loss of function in one copy of the TCF4 gene.

After breast cancer, Allison Leeds found that medicine could explain a surgical plan but offered little guidance on what recovery would feel like day to day, emotionally. She began using ChatGPT in early 2025 to synthesize more than 11,000 patient experiences drawn from public forums, then paired those insights with research in color science and neuroaesthetics to explore how visual environments can shape stress and pain. The result became a new tool for recovery: weekly art cards that combine color palettes with mantras, designed to arrive in cadence with the hardest parts of healing. Shared with support groups, the cards quickly resonated with patients and caregivers and grew into Leeds’s business, built between surgeries with AI acting as a research partner and product scaffold. Her broader body of work, The Healing Spectrum, is now exhibited at SFMOMA, extending the same idea: recovery needs information for the heart, not only the body.

At Scripps Research, PhD candidate Marco Uytiepo studies how neural circuits store experience by analyzing immense electron-microscopy datasets and reconstructing brain wiring across millions of synapses. Trained primarily as an experimental scientist, he taught himself to code and now uses ChatGPT to accelerate the unglamorous work that makes modern neuroscience possible: building data pipelines, debugging scripts, and automating 3D reconstruction workflows across tools like MATLAB, Python, and Blender. Work that previously required days of iteration can be reduced to rapid cycles that produce usable code and clearer visualizations, supporting faster hypothesis testing and more experiments within a single project.

Dr. Fleurique Franke, a general practitioner in the Netherlands, uses custom GPTs to streamline routine work in her clinics while improving the quality and tone of patient communication. By drafting clear, guideline-based responses to hundreds of e-consult messages and tailoring explanations to different reading levels, ChatGPT helps her team respond faster without losing empathy or precision. The time saved, roughly a third of her daily message workload, is reinvested in care, training, and follow-up, showing how AI can modernize clinical workflows while keeping medicine humane and accessible.

Clinical Mind AI is a Stanford-origin platform and research program led by Marcos Rojas Pino that trains clinical reasoning through realistic, decision-based simulations delivered by text and real-time voice. Built on OpenAI’s API and developed from an early laptop prototype into a full platform, it lets instructors author cases and gives learners a telehealth-style interface with an electronic health record for documenting histories, reviewing images, and ordering tests. The system targets two core gaps that separate novices from experts: asking effective questions to gather data and recognizing which findings matter for diagnosis, treatment, referral, and disposition decisions.