AI in Healthcare

Today's Stories

1. 1

   Israel's medical data becomes prize for Pfizer, OpenAI, tech giants

   Multinational pharmaceutical and AI companies including Pfizer and OpenAI are pursuing access to Israeli medical data to train AI models and develop new drugs. Israel's centralized health system and digitized patient records—held by health funds and government institutions—have made the country an attractive source for this sensitive data. Medical data is critical for training AI systems that can improve drug discovery, disease diagnosis, and personalized treatment. For Israel's tech sector, this positions the country as a supplier of high-value health information to global leaders, though the arrangement raises questions about data privacy and sovereignty over sensitive citizen health records.

   The terms and safeguards under which Israeli health data is shared—including whether individuals consent, how identifiable data is protected, and which institutions control access—will shape both the pace of AI-driven medical breakthroughs and the precedent for other countries with digitized health systems.

2. 2

   Bristol Myers Squibb Expands Nvidia AI Partnership for Drug Discovery

   Bristol Myers Squibb announced it will deploy Nvidia's next-generation DGX SuperPOD AI supercomputer to build what it calls the most powerful AI factory in life sciences, expanding its existing collaboration with Nvidia to accelerate drug discovery and development. Drug development is typically slow and expensive; if Bristol Myers' AI efforts succeed in cutting development time and costs even by 1%, the impact could be meaningful across its dozens of active pipeline programs. The company already faces major patent cliffs ahead for key drugs like Eliquis and Opdivo, so faster, cheaper drug discovery could help offset revenue pressure from generic and biosimilar competition.

   Bristol Myers has said it is already seeing benefits from its long-standing AI-related efforts, and this new supercomputer investment could help further boost efficiency. The company is also advancing newer medicines including milvexian (a next-gen anticoagulant) and pumitamig (a cancer drug in multi-indication testing) that could help mitigate patent cliff impact.

3. 3

   Throne Science raises $10M for AI toilet camera tracking gut health

   Throne Science, which makes an AI-powered toilet camera to track gut health and hydration, raised $10 million（約16億円） in a Series A round led by Will Ventures. The round brings the startup's total funding to nearly $18 million（約29億円） since its 2023 inception. The company was founded by CEO Scott Hickle and former Whoop co-founder and CTO John Capodilupo. Throne uses computer vision analysis and multiple AI models trained by gastroenterologists to measure health metrics from stool and urine. The company recently launched a beta Gut Health AI coach that uses large language models to conduct conversational interviews with users after episodes of poor gut health, helping identify patterns between diet, lifestyle, and digestive outcomes—a capability Capodilupo views as central to the product's value because longitudinal data on individual users is what enables personalized insights.

   Throne's longer-term goal is to develop an at-home system capable of identifying early warning signs of colorectal cancer, bladder, and kidney cancers (a capability still under development and not yet offered). The company is running validation studies with researchers at Harvard University, Stanford University, and the University of Chicago, and has been accepted to present its first academic abstract showing its visual AI assesses stool form with accuracy on par with board-certified gastroenterologists.

4. 4

   AI drug discovery hits data wall as models need negative results, not just wins

   AI is shifting pharmaceutical research from physical screening of molecular libraries to computational prediction of drug candidates, but the approach is exposing a critical bottleneck—most publicly available datasets contain only positive results, leaving AI models trained on incomplete information that lacks the failed experiments needed to avoid bias and improve reliability. Bringing a new drug to market takes 10–15 years and costs $1 billion（約1600億円） to $2.5 billion（約4000億円） with failure rates upward of 90%; AI is the industry's biggest bet to reduce timelines and improve success rates. However, without access to negative data (compounds that don't bind, failed experiments), models cannot be adequately trained to make accurate predictions, and fabricated or manipulated data—easier to create with generative AI—could have "potentially disastrous consequences" when used to train models.

   No drug discovered primarily through AI-driven design has yet received full FDA approval, though Belcher expects that to change within the next two to three years. The future state is fully autonomous labs that cycle through prediction, testing, and optimization while feeding results back into AI models—but this requires interoperable lab systems and structured, comprehensive datasets that most labs do not yet have.

5. 5

   Pfizer, Medtronic, Hinge Health offer AI healthcare exposure

   An investment screener identified three publicly traded companies—Pfizer (market cap US$142.5b), Medtronic (US$105.0b), and Hinge Health (US$5.9b)—as stocks with meaningful exposure to artificial intelligence applications in healthcare, spanning drug discovery, surgical robotics, and digital musculoskeletal care. Healthcare AI addresses a structural need across systems facing cost pressure, with potential to improve diagnostics, treatment decisions, and productivity. Pfizer's AI-powered R&D partnerships and drug pipeline, Medtronic's AI-guided surgery and robotics (Hugo system), and Hinge Health's motion-tracking digital platform and Enso wearable each represent different entry points to this theme for investors seeking long-term structural trends rather than short-term headlines.

   Pfizer's dividend sustainability (not fully covered by earnings or free cash flow), Medtronic's execution on AI initiatives amid diabetes spin-off and margin pressure, and Hinge Health's shift from devices to software-based care amid analyst guidance raises and index inclusions—each carries distinct risks that could affect growth and valuation going forward.

6. 6

   J&J hits $25.3bn Q2 sales, eyes $100bn milestone; Eli Lilly bets $3.8bn on psychedelics

   Johnson & Johnson reported Q2 2026 sales of US$25.3bn, up 6.6%, and raised its full-year guidance to US$101.1bn—putting it on track to exceed US$100bn in annual revenue for the first time. Separately, Eli Lilly agreed to acquire AtaiBeckley for up to US$3.8bn, marking its entry into psychedelic-based drug discovery for treatment-resistant depression. Bristol Myers Squibb is deploying a second NVIDIA-powered AI system that will deliver up to ten times greater performance per megawatt than its predecessor, positioning it as the life sciences industry's most powerful privately owned AI infrastructure. J&J's guidance upgrade underscores sustained momentum in its core pharmaceutical and medical device businesses at a scale few companies match. Eli Lilly's psychedelic bet signals confidence that novel mechanisms can address treatment-resistant depression, a condition that persists even after multiple standard therapies fail. BMS's AI infrastructure expansion directly supports drug discovery—a bottleneck in pharmaceutical R&D where computational power and efficiency are now central competitive levers.

   J&J's progress toward the US$100bn revenue threshold across 2026; whether AtaiBeckley's investigational psychedelic therapies advance through clinical development; and whether BMS's tenfold efficiency gain translates to faster candidate identification or reduced discovery timelines.