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Healthcare AI startups in Robinhood Ventures Fund II

Robinhood Ventures Fund II backs healthcare AI startups spanning simulated drug discovery, accessible imaging, and clinical operations.

MedSpark Staff
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msadmin
MedSpark Staff
Bymsadmin
Medical, Healthcare, & Biotech/Pharma AI News
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Published: August 11, 2026
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Robinhood Ventures Fund II, the brokerage’s venture vehicle for early-stage startups, holds more than 80 companies, and healthcare AI runs through a striking number of them. The fund issues $250K SAFEs to founders, sources heavily from the Y Combinator network, and is set to begin trading on the NYSE under the ticker RVII on August 13. For a vehicle built around retail investors, the fund’s exposure to AI in medicine is one of its most distinctive threads, and the concentration is no accident of deal flow.

Contents
Simulated biology and drug discoveryImaging and diagnosticsClinical operations and care deliveryWhy healthcare AI fits the fund

The healthcare AI cohort inside Fund II spans drug discovery, diagnostics, clinical operations, and direct patient care. Its companies range from startups that simulate human biology in software to teams building accessible imaging hardware and AI assistants that work beside clinicians in real time. Most are young, YC-adjacent teams applying foundation models and agentic systems to problems that have historically resisted software, and several are already generating revenue from clinics and health systems rather than waiting on pilot programs.

Simulated biology and drug discovery

Drug discovery remains one of the slowest and most expensive phases in medicine, and two Fund II companies attack it from different angles. CellType is building agentic drug discovery on simulated human biology, letting AI agents test hypotheses against computational models of cells, tissues, and disease pathways before anything touches a lab bench. The approach treats biology as a software substrate: agents can run thousands of in silico experiments in a night, narrowing candidate lists that would otherwise take months in wet labs. BioStack Platforms works the data side, creating real-world training environments where healthcare AI can practice on realistic clinical scenarios before deployment. Together the two cover the pipeline: one generates biological hypotheses, the other gives AI systems a safe place to learn.

Imaging and diagnostics

Advanced imaging has a distribution problem. MRI and ultrasound are concentrated in large hospitals, leaving rural and community patients with fewer options and later diagnoses. Adialante is working to make MRI-based cancer screening broadly accessible, chipping away at the cost and footprint barriers that keep scanners out of community settings. Lumius Imaging is pursuing accessible 3D ultrasound with a similar mission, pushing sophisticated imaging toward the point of care rather than confining it to major hospital centers. Both companies are betting that the next decade of imaging innovation is about distribution as much as resolution, and that early detection should not depend on geography. For Adialante, that means rethinking the scanner itself; for Lumius, it means rethinking where ultrasound fits in routine visits.

Clinical operations and care delivery

A second cluster focuses on what happens after diagnosis. Opalite Health delivers real-time AI medical interpretation, helping clinicians and patients communicate across language barriers in the moment of care, a practical layer for hospitals serving diverse communities. Ruma builds the operations stack for biologic infusion clinics, handling the scheduling, logistics, and documentation that keep complex infusion therapies running. On the care delivery side, Prana AI is developing an AI primary care doctor, Plena is building an AI operating system for specialty medicine practices, and SharedGenes offers an AI health assistant aimed at people managing chronic illness day to day. Interpretation, infusion logistics, and AI-first care share a theme: software taking over work that scales poorly with headcount.

Why healthcare AI fits the fund

For a fund built around Y Combinator deal flow, the healthcare AI concentration makes sense. These are capital-efficient teams applying generative and agentic AI to some of the most expensive problems in medicine, and the $250K SAFE structure suits software-first companies that can show traction before raising larger rounds. That also explains why a brokerage’s venture arm, rather than a dedicated health fund, ended up with this mix: the thesis is software efficiency, with healthcare as the proving ground. The cohort reflects a broader shift as AI moves from back-office tools into clinical workflows, and Fund II’s healthcare names sit at that transition point.

With RVII set to list on August 13, retail investors get a window into more than 80 early bets, and the healthcare AI names are among the most visible in the portfolio. The full holdings list, including each company’s nature of business, is available in the fund’s SEC prospectus, and it shows a fund that is deliberately broad, with healthcare AI as one of its clearest through-lines.

TAGGED:AI biotechAI healthcareAI healthcare fundingbiotech startupshealthcare startupventure capital
SOURCES:RobinhoodSEC Prospectus
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