EXPERT VALIDATION SURVEY-Sex-Specific AI Prediction in ALzheimer's Preclinical Research

Neurodegeneration / Neuroscience / Alzheimer's Research

Computational Biology / AI modelling / Bioinformatics / Pharmacokinetics

Gender & Health Equity / Women's Health / Sex-Inclusion Policy 

Which of the following best describes your area of expertise?

Studies are not designed or powered to account for hormonal variation

Male animal models are still the default in our field

Lack of validated sex-specific biomarkers for Alzheimer's

Limited tools for sex-stratified analysis

Translating sex-specific findings from mouse models to clinical outcomes is poorly understood

We include both sexes but do not stratify analysis

Sex-specific biomarker dynamics — how markers evolve differently in male vs female models

Dosing optimisation recommendations based on hormonal state and sex

Subgroup confidence scores — reliability of predictions across male and female groups

Study design recommendations tailored to sex-stratified protocols

Cross-species prediction (mouse to zebrafish or human)

Lack of published validation data for the AI model

Preference for traditional in vivo methods only

Uncertainty about how AI-generated data is viewed by journals or funders

Occasionally — some models include it but inconsistently

Moderately — about half of models I work with include it

Frequently — it is increasingly standard

Consistently — sex is always a modelling variable in my work

Limited publicly available sex-stratified mouse model datasets (e.g. GEO, ADNI)

Lack of hormonal state annotation in existing datasets

Inconsistent biomarker reporting across male and female stuies

No established benchmark datasets for sex-specific drug response validation

Insufficient sample sizes for female-only or sex-stratified sub-analyses

Machine learning or deep learning models (e.g. XGBoost, neural networks)

Pharmacokinetic / pharmacodynamic (PK/PD) modelling

Statistical or regression-based approaches

Natural language processing on literature or clinical notes

I primarily validate or review models rather than build them

Published benchmark comparisons against established preclinical datasets

Transparent model architecture and open-source code

Explainability features — the model shows feature importance and reasoning

Validated on independent datasets from different labs or species

Uncertainty quantification — the model reports confidence intervals per prediction