AI Integrations and a Shift in Experimental Models (Part 3 of 4)

This is the third post in a series reflecting on themes from FOG (Boston) and EACR (Budapest). This post covers two themes that were less flashy than multiomics and spatial biology but are potentially more consequential in the long run: the integration of AI across the field, and a structural shift in the experimental models researchers are using. Both are less about new science and more about the shifting infrastructure underneath. 

Click to read Part One and Part Two.

At FOG in particular, AI wasn’t confined to its own dedicated track. It was woven through virtually every session, across genomics, proteomics, drug discovery, pathology, and clinical data science. Assay automation, foundation models, agentic platforms, generative tools, and machine learning pipelines all featured across the full breadth of the programme. 

The signal isn’t that AI is coming to biology. The signal now is that AI is assumed. It’s becoming infrastructure rather than innovation. The more interesting questions are about what AI currently can’t do well, and where the field’s enthusiasm is running ahead of its validation. 

The hardest of those questions, and the one I kept hearing across both conferences: QC and standardisation of multi-modal data remains a genuine bottleneck. Sequencing data has well-established quality metrics (Q scores, coverage depth, error rates) that the field has spent two decades refining. High-content spatial imaging data doesn’t have equivalent consensus standards. Batch effects, optical artefacts, tissue quality variation, and registration errors all introduce noise that current QC frameworks handle inconsistently. 

This matters because AI tools are only as good as the data they’re trained and validated on. If the underlying data quality standards aren’t solved, AI amplifies the noise rather than cutting through it. Whoever solves the reproducibility and validation problem at scale for spatial and multimodal imaging data will capture disproportionate value as the field translates into clinical use. 

Less visible than AI and multiomics, but very recognisable especially at EACR is that research is moving away from animal models toward more human-relevant systems. Organoids, in vitro tissue models, and banked human tissue are gaining ground as primary experimental targets. 

This shift is being driven from multiple directions. Regulatory frameworks are increasingly permitting non-animal alternatives for preclinical testing, and the economics favour it. Animal studies are slow, expensive, and have well-documented translational failure rates. And the scientific argument is straightforward: if the goal is to understand human biology, starting with human material makes more sense than starting with a model organism and hoping the biology translates. 

The technology ecosystem around this shift is responding faster than I’d expected. Plenty of new platforms for 3D cell culture, tissue engineering, and high-throughput organoid screening were visible across both conferences, from established companies and newer entrants alike. The infrastructure to support human-relevant preclinical research is being built now, and the pace suggests this is a structural shift rather than a passing trend. 

These two themes (AI integration and the move to human-relevant models) might seem unrelated, but they share a common thread: both are about the foundations of how research gets done changing underneath the science itself. AI is reshaping the data infrastructure. Organoids and banked tissue are reshaping the experimental infrastructure. Together, they’re redefining what a well-equipped lab looks like.

Interested in how these infrastructure shifts connect to your lab’s workflows? Whether it’s spatial data analysis, 3D cell culture, or automation, we’re here to help you navigate what’s next.