AI Is Catching Up to Preclinical Reality

A signal, not a surprise

Over the past year, large AI companies have made increasingly deliberate moves into healthcare and life sciences. Microsoft has expanded its healthcare AI infrastructure. OpenAI has formalized healthcare-specific applications. Anthropic has released tools positioned directly at biomedical research and clinical workflows. Public clinical trial data is increasingly treated as something models are expected to reason over, rather than a static archive.

To some observers, this looks like a pivot. From our vantage point at ModernVivo, it looks like confirmation.

For years, the promise of AI in biopharma has been framed around acceleration. Faster target identification. Faster molecule generation. Faster clinical trials. Speed matters, but speed alone does not address the structural issues that have constrained drug development for decades. What changes outcomes is decision quality, especially early in the pipeline when experimental assumptions are set and evidence begins to accumulate.

The recent behavior of large AI companies suggests that the industry is beginning to internalize this distinction.

What the current wave of healthcare AI gets right

A notable shift in recent healthcare AI announcements is how explicitly they acknowledge the constraints of medical and scientific work.

In January, Microsoft described its integration of Anthropic’s Claude models into Microsoft Foundry as an effort to “bridge the gap between AI and medicine,” emphasizing that healthcare AI must operate within clinical, regulatory, and data-governance boundaries rather than around them. The above post frames the core challenge not as raw model capability, but as alignment with how medical decisions are actually made.

OpenAI has taken a similar position. In outlining its healthcare initiatives, the company emphasizes clinical documentation, medical research synthesis, and support for regulated decision-making environments. The language is careful. Rather than promising autonomous discovery, OpenAI positions its models as tools that assist clinicians and researchers in navigating dense, high-stakes information environments.

Anthropic’s recent healthcare and life sciences announcements follow the same pattern. At JPM Healthcare, the company introduced a life sciences toolkit designed to support protocol development, literature analysis, and interaction with structured biomedical data. Anthropic has been explicit that its goal is not to replace scientific judgment, but to support reasoning in domains where errors carry real consequences.

This emphasis on reasoning and structure is not accidental. Healthcare data is unusually fragmented and constrained. Clinical trial records, regulatory submissions, and published studies are shaped by compliance requirements, ethical review, and uneven reporting standards. Treating this material as interchangeable with general web text produces brittle results, particularly in regulated settings.

There is also renewed attention to public data infrastructure. Platforms such as ClinicalTrials.gov and the AACT database maintained by the Clinical Trials Transformation Initiative are increasingly cited as foundational resources for understanding how trials are designed, which endpoints are chosen, and how interventions progress over time. As one researcher noted in response to Anthropic’s tooling, the ability to reason directly over ClinicalTrials.gov data meaningfully changes how questions about precedent and trial design can be approached.

Taken together, these moves point to a shared diagnosis: the bottleneck is not a lack of data, but a lack of usable structure.

Where the focus still falls short: in vivo study design

Despite this progress, one layer of the drug development process remains largely unaddressed by current AI systems: in vivo study design.

Animal studies continue to determine which programs advance, which therapeutic compounds are deprioritized, and how much confidence teams carry forward into the clinic. Yet the way these studies are planned often relies on partial precedent and informal reasoning rather than systematic comparison.

Published in vivo research frequently omits key methodological details. Animal models are described inconsistently. Dosing rationales are implied rather than stated. Negative results are rarely published. Even when protocols exist, comparing experimental designs across papers is difficult. As a result, teams planning a new preclinical study often assemble their rationale manually, searching for prior in vivo research that is “close enough” and inferring critical parameters from incomplete descriptions.

This has downstream consequences. A weakly justified model choice affects how efficacy signals are interpreted. Insufficient documentation creates friction during IACUC approval, where researchers must justify animal use and document consideration of alternatives. By the time a program reaches the clinic, these early decisions are embedded in the evidence base and difficult to untangle.

Most AI platforms avoid this layer. The reason is practical. In vivo studies are not standardized. They are context-dependent and shaped by biological nuance, ethical constraints, and lab-specific practices. Systems optimized for clean, well-labeled datasets struggle when faced with heterogeneous protocols and uneven reporting.

As a result, AI investment has concentrated downstream, where data is cleaner and metrics are clearer. Clinical trial optimization, medical documentation, and literature summarization are valuable. None of them resolve the uncertainty that enters the pipeline much earlier.

ModernVivo’s perspective: translatability starts upstream

ModernVivo was built around a different assumption. We start from the premise that many downstream failures are rooted in upstream opacity.

Translatability is often treated as a retrospective property of data. We treat it as a design constraint. The question is not whether an in vivo result might translate, but whether the study that produced it was grounded in a transparent understanding of prior evidence, methodological precedent, and biological context.

This requires treating literature review as a core component of in vivo experimental design. Not as a procedural formality, but as a structured analysis of how similar studies have been conducted, what parameters were chosen, and where approaches diverged. It also requires supporting the ethical and regulatory documentation that accompanies animal research, including clear justification for model selection and compliance with IACUC requirements.

Our work focuses on making this decision space legible. That means enabling comparison of in vivo protocols across studies, surfacing patterns in how models and dosing strategies have been used, and supporting explicit reasoning about experimental choices. The goal is not to prescribe a single correct design. It is to give researchers better visibility into what has already been tried before committing to new experiments.

AI is well suited to this role when used carefully. Large language models can structure unstructured scientific text and support comparisons that would be impractical to perform manually. Used responsibly, they reduce the cognitive burden of planning rigorous preclinical studies without replacing scientific judgment.

Convergence as validation, not competition

Seen in this context, the entry of Microsoft, OpenAI, and Anthropic into healthcare and life sciences is not a threat to ModernVivo’s work. It is validation of the broader direction.

These companies are investing heavily in secure infrastructure, compliance, and reasoning over biomedical data. Public clinical trial registries are being treated as first-class inputs. Ethical and regulatory constraints are being acknowledged explicitly. This reinforces the idea that decision quality, not just computational speed, is the limiting factor in drug development.

At the same time, these platforms are not designed to resolve the upstream challenges of in vivo study design. They do not answer questions like how a specific animal model has been used across indications, how dosing strategies have varied in prior in vivo research, or how methodological choices correlate with downstream success or failure.

In this sense, ModernVivo’s work sits slightly orthogonal to many recent AI advances in healthcare. While others focus on accelerating discovery or automating downstream processes, we are focused on strengthening the evidentiary foundation on which those processes depend. Better-designed in vivo studies do not guarantee clinical success, but poorly designed ones almost guarantee failure.

Complementarity matters. Downstream AI systems depend on the quality of the evidence they inherit. Strengthening that evidence base increases the value of everything that follows.

The unfinished work

AI will not remove biological uncertainty. It will not guarantee translation. It will not replace scientific judgment.

The recent convergence of AI efforts across healthcare suggests that the field is beginning to recognize this reality. Attention is shifting from isolated tasks to integrated workflows, from speed alone to decision quality, and from novelty to reliability. That shift creates space for more thoughtful engagement with the upstream processes that have historically been treated as opaque or purely artisanal.

ModernVivo exists to work within that space.

Our focus is not on predicting outcomes in the abstract, but on strengthening the foundations that make outcomes interpretable. By bringing structure to how in vivo research is planned, compared, and documented, we aim to support a drug development ecosystem that learns more effectively from its own history. This is not a claim that better tools will guarantee success, but it is a recognition that better-designed studies give teams a far stronger starting point.

As more organizations invest in AI to support healthcare and life sciences, the opportunity is not to race toward the same solutions, but to build complementary ones. Progress will come from alignment across the pipeline, not domination of any single stage.

The work is unfinished. But the direction is clear.