Tailored Biomarkers from H&E

Accelerate biomarker profiling to hours 

with BioAI’s custom H&E‑based biomarkers

AI Biomarker Development

BioAI supports and accelerates BioPharma clinical programs with rapid, AI‑driven analytics and tailored workflows built for the future of precision medicine. 
Our platform empowers our partners to discover new drug targets and enhance diagnostic precision through the development, validation, and global deployment of predictive digital biomarkers.

 

The H&E Advantage: A core specialty is the application of rapid H&E-based digital biomarkers to optimize clinical trial enrollment. By pre-screening for eligibility at the point of care, we expand patient access while drastically reducing screening costs and timelines.  

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Predictive AI Models from H&E

BioAI has a deep experience and a strong track record in developing H&E-based AI models that accurately predict known genetic mutations and proteomic marker expression across multiple cancer types.

A major use case is applying H&E-based digital biomarkers to screen or pre-screen patient’s eligibility for clinical trials in order to expand the access to patients suited and to significantly reduce overall testing costs

Special Capabilities: 

  • Developing AI models for broad bandwidth of cancer indications, such as lung, breast, melanoma, and more
  • Developing AI models to predict the status of genetic mutations, e.g. RET, EGFR, ROS1, ALK, NTRK, BRAF
  • Creating AI biomarker models to measure various types of protein expression, such us B7H4, and CD3.
  • Different measures and novel approach working towards high generalizability of AI algorithms
  • High performance of AI models; with sensitivity and specificity modeled according to the specific needs
See Our Work on Predictive AI Models

Novel Predictive AI Biomarkers

Customize treatments, increase success rates for clinical programs, and improve patient outcomes by predicting the patients’ response to therapy on retrospective clinical trial data.  

Special Capabilities: 

  • Determination of pathological and molecular factors that might play a role in patient response to therapy and drug resistance
  • Novel biomarker development in the form of AI models for outcome prediction: predict treatment outcomes, disease resistance factors, adverse events, and long-term patient responses
  • Identification of patient subgroups likely to respond to specific therapies, enabling more targeted treatment strategies