Oncology is where healthcare AI has the strongest clinical evidence and the most demanding integration requirements. Treatment matching against the NCCN guidelines, biomarker-based therapy selection, clinical trial eligibility screening, and tumor board decision support all run on a stack that combines structured EHR data, unstructured pathology and radiology reports, genomic test results, and a regulatory pathway that often crosses the FDA SaMD threshold. Generic AI engineering does not get you there.
Taction Software’s oncology AI engineers have built treatment-matching engines, trial-eligibility screeners, and biomarker analytics pipelines integrated with Epic, Cerner, and other major EHRs. We work with NCCN guidelines, OncoKB, ClinicalTrials.gov data, and FHIR R4 oncology profiles. Engagements begin with a $45K Discovery Sprint.
Our experts are ready to understand your business goals.
We map your oncology AI use case (treatment matching, trial eligibility, biomarker analytics, tumor board support), select the data sources, identify FDA SaMD risk, and produce a technical architecture with eval harness skeleton and BAA-eligible model path.
We build the inference pipeline with PHI redaction, the NCCN or biomarker matching logic, FHIR integration, the first end-to-end use case, and audit logging.
We harden for clinical pilot, produce clinical evidence, prepare FDA SaMD documentation if applicable, and produce care-package handoff.
For productized sprint pages, see Discovery Sprint, MVP Sprint, and Pilot-Ready Sprint.
AI-powered treatment-pathway matching based on stage, biomarker, prior therapy, and comorbidity.
Automated trial-eligibility computation across ClinicalTrials.gov, tied to FHIR ResearchSubject and ResearchStudy.
Structured extraction from free-text pathology reports including tumor type, grade, margin status, lymph node involvement, and biomarker results.
RECIST measurement extraction, lesion tracking across studies, and tumor burden assessment.
Integration with OncoKB and ClinVar for biomarker-driven therapy matching.
Multidisciplinary case-presentation AI with treatment-option ranking and evidence summarization.
For background, read our healthcare AI use cases 2026 guide and the HIPAA-compliant AI engineers playbook.
Hire healthcare AI engineers, LLM engineers, or FDA SaMD engineers at $8K per engineer per month.
For estimates, use the healthcare AI cost calculator.
Discovery Sprint $45K, MVP Sprint $95K, Pilot-Ready Sprint $145K. Full pathway $285K over 24 weeks.
Frequently yes. Treatment-matching engines and biomarker-driven recommendations often qualify as Software-as-a-Medical-Device. We identify this during Discovery and pair the engagement with the FDA SaMD pathway add-on when applicable.
NCCN guidelines, OncoKB, ClinVar, COSMIC, ClinicalTrials.gov, SEER, AJCC staging, ICD-O-3, plus standard FHIR R4 resources.
Yes. Integration with OncoKB, ClinVar, and COSMIC for biomarker-driven therapy matching is standard.
Yes. NLP over unstructured oncology reports is core work, including RECIST measurement extraction and biomarker normalization.
Yes. Multidisciplinary decision-support patterns with treatment-option ranking and evidence summarization are within scope.
Yes. Every engagement begins with a BAA.
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We schedule a call to discuss your project, goals. and priorities, and provide preliminary feedback.
If you're satisfied, we finalize the agreement and start your project.