Foundational Guide to using AI in Preclinical Research
To build on our foundations of AI, we will focus on the current, evidence-based roles of AI & ML in pre-clinical research. In this guide, we will focus on domains with substantive published evidence such as histopathology image analysis, automated behavioral scoring, high-content imaging, continuous monitoring, and pharmacometric augmentation.
Pre-clinical research generates high-dimensional and high-volume data. Typical datasets include whole-slide histology images, time-series behavior videos, high-content cellular images, and pharmacokinetic time courses. Conventional analysis approaches are often manual, labor intensive, or limited in sensitivity for subtle phenotypes. AI methods have shown the capacity to accelerate processing, increase throughput, and in some controlled settings improve signal detection. For pre-clinical teams, the relevant questions are not whether AI can work in principle but whether it can be validated and documented to the standard required for GLP and for downstream regulatory submissions.
Mature application areas and expected benefits
The literature and implementation reports identify several domains where methods and validation practice are most advanced.
Toxicologic pathology and digital histology: Convolutional neural networks and related methods can perform segmentation, cell detection, and lesion localization on digitized slides. Benefits include reduced manual annotation time and increased consistency of routine readouts when models are validated on institutionally representative data. Generalizability across scanners, stains, and sites remains a primary concern.
Automated behavioral analysis: Markerless pose estimation and downstream behavior classifiers enable objective, high-throughput scoring of selected ethograms. Benchmark studies report accuracy comparable to expert human scorers on defined tasks when camera geometry and annotation standards are consistent.
High-content imaging and phenotypic screening: Deep learning methods support robust cell segmentation and feature extraction, enabling automated triage and phenotype clustering at scale. Assay-specific calibration is required to ensure that features reflect biological signal rather than acquisition artifacts.
Continuous/home-cage monitoring: Sensorized systems with automated analytics can capture longitudinal signals that are difficult to collect manually. Early evidence suggests potential to detect subtle toxicity or behavioral change, but commercial and academic adoption is still limited and studies remain heterogeneous.
PK/PD and pharmacometrics augmentation: ML serves as a complementary tool to mechanistic models for forecasting and covariate discovery. Current best practice is to couple ML methods with established pharmacometric frameworks and to explicitly quantify uncertainty.
Principal limitations and risks
Data provenance and bias: Pre-clinical datasets are susceptible to site effects, batch effects, and annotation bias. Without careful provenance tracking and appropriate splits for validation, models risk learning confounding signals.
Domain shift: Models trained in one laboratory, scanner, or species often perform worse when applied to data from different conditions. Performance claims should be bounded to the validated domain.
Regulatory and GLP evidence requirements: For outputs that inform safety decisions, traceability, version control, and reproducible analysis pipelines are essential. Models intended to support study endpoints must be auditable and documented with validation plans commensurate with their risk.
Overinterpretation: Benchmark performance on curated datasets does not automatically translate to operational readiness. Human oversight must remain part of safety-critical workflows.
Our recommendations
Begin with use cases that have clear, narrow intended use and measurable downstream actions. Define the precise decision the model would support and the consequences of false positives and false negatives.
Assemble minimal viable datasets that reflect the target operational domain. Include diversity of scanners, stains, strains, or cages as appropriate. Track provenance and metadata.
Define a validation plan before model development. The plan should specify hold-out datasets, performance metrics with confidence intervals, and prospective verification steps.
Maintain pathologist or domain expert in the loop for model development, threshold setting, and periodic review. Use annotation consensus procedures to limit label noise.
Version control models and datasets. Capture configuration, training data summary, random seeds, and evaluation artifacts to meet audit requirements.
Pilot in a non-safety-critical context to build evidence. Document learning and adapt SOPs before deploying for regulatory endpoint support.
