AI Biomarker Services
Accelerate biomarker discovery and development with BioAI’s custom biomarker services. Identify and understand novel drug targets, improve diagnostic accuracy, and experience the future of precision medicine.
AI Biomarker Development
Leading AI:
BioAI specializes in creating advanced workflows and analytics for Pharma clients’ clinical biomarker programs, offering services in developing, optimizing, validating, and deploying predictive and diagnostic digital biomarkers.
Data Sourcing:
We collaborate with data partners to augment or source complete patient data sets and are adept in handling projects in both regulated and non-regulated environments, open to global diagnostic development partnerships.
Predictive AI Models for Known Genetic Mutations
BioAI has a strong experience and track record in developing H&E-based AI models for known genetic mutations in various cancer indications.
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
- 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
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