AI in Healthcare

Today's Stories

1. 1

   Hospital seeks on-prem MLOps platform for production model monitoring

   A hospital operating a fully on-premises OpenShift cluster is evaluating ClearML and OpenShift AI for managing machine learning models built by multiple internal teams, as well as models running at external vendors. The organization is building a self-service platform with centralized boundary policies to let teams work independently while enforcing guardrails around access control and resource limits. Healthcare organizations must keep patient data on-premises for compliance and security reasons, ruling out cloud-based MLOps tools. The hospital's challenge—monitoring both internal models and external vendor models for production issues like data drift and bias—reflects a real constraint in regulated industries where real-time visibility across the full model lifecycle is critical for patient safety.

   The hospital has identified a gap in existing platforms: neither ClearML nor OpenShift AI provides the production monitoring depth needed (drift detection, bias tracking, live per-model dashboards). The ability to monitor vendor models where only input/output data feeds are available—a common scenario in healthcare partnerships—appears to be an unmet need in the current tooling landscape.

2. 2

   Genomics expert Schadt leaves Pathos AI, joins Pfizer as first AI Scholar

   Eric Schadt, a computational biology expert and former chief scientist at Pathos AI, is departing that role to advise Pfizer as its first AI Scholar while also launching a new stealth-mode startup. Schadt will remain Dean for Precision Medicine at the Icahn School of Medicine at Mount Sinai. Schadt is a veteran biotech entrepreneur with a track record spanning roles at Roche, Merck, Pacific Biosciences, and Sema4; he has published over 500 scientific papers with an H-Index of 165 and has appeared on lists of the world's most influential scientists. His advisory role at Pfizer signals the pharmaceutical giant is betting on AI to reshape its R&D strategy and drug discovery process.

   Schadt's stealth startup is focused on precision medicine, built on the principle of starting with individual patient data and using AI to guide treatment decisions. At Pfizer, he will work with teams to shape AI strategy and build proprietary models.

3. 3

   Eli Lilly partners with Insilico Medicine for $2.8B AI drug discovery

   Eli Lilly has signed an agreement with Hong Kong-based Insilico Medicine valued at $2.8B to expand its AI-driven drug development efforts. The deal signals that major pharmaceutical companies are increasingly betting on AI to accelerate and reduce costs in drug discovery—a traditionally slow, expensive process that historically takes years and billions to bring a single drug to market.

   The partnership will test whether AI can meaningfully shorten timelines and improve success rates in identifying drug candidates, a critical benchmark for the pharmaceutical industry's broader embrace of machine learning in research.

4. 4

   WIO 2026 conference to feature AI, AMD, physician talks

   The World Ophthalmology Congress (WIO) 2026 has announced its agenda, which will include sessions on artificial intelligence, age-related macular degeneration (AMD), and physician leadership topics. Ophthalmology conferences shape clinical practice and research priorities. A congress agenda that covers AI reflects the specialty's interest in how machine learning may influence eye care, while AMD and leadership education address pressing clinical and professional needs in the field.

   WIO 2026 will bring together ophthalmologists globally to discuss emerging tools and practice challenges, though the body does not specify dates, location, or registration details.

5. 5

   AI discovers drugs, but only humans can claim the patent

   Insilico Medicine used AI to design a drug for pulmonary fibrosis and publicly credited its AI platform as the discoverer—but when filing the patent, listed five humans, including CEO Alex Zhavoronkov, as inventors. A 2022 US appeals court ruled that because statutes define an inventor as an "individual" (a human being), machines cannot legally be named inventors, even if they played the central role in the discovery. The patent system exists to encourage innovation by granting exclusive rights to inventors. As AI systems generate drug designs with less human input, the question of who qualifies as an inventor becomes legally and commercially critical. If a patent lists the wrong inventors—or if AI work is excluded from protection altogether—it could weaken patent claims or deter companies from pursuing AI-driven drug development, according to patent attorney Ryan Abbott, who brought the test case.

   The US Patent and Trademark Office currently treats AI as a tool (like a calculator) and does not require applicants to disclose its use. However, Abbott warns that future legal challenges could invalidate patents for incorrect inventor lists. Patent attorney Sarah Korman (chief business officer of Isomorphic Labs, an Alphabet spinout) stated at MIT Technology Review's EmTech event that "there is no doubt our laws will need to evolve to keep pace with AI."

6. 6

   HP Korea partners with Upstage for AI in healthcare, finance

   HP Korea has formed a partnership with Upstage, a large language model (LLM) startup, to advance its localized AI strategy. The company is already moving into AI transformation (AX) services targeting document-heavy industries such as healthcare and finance. Healthcare and finance sectors handle vast amounts of documents and require AI systems that can process and understand text in their specific context. By partnering with a local LLM startup rather than relying solely on global AI providers, HP Korea is tailoring its approach to regional needs and market demand.

   The partnership signals HP Korea's commitment to positioning itself in the growing market for enterprise AI services in document-intensive industries, where competitors are also targeting similar sectors.