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Radiology Report AI
Finding and impression extraction, report classification, structured outputs, summarization and report-to-label mapping where suitable references exist.
Clinical NLPDatavendor · Clinical AI Validation
Evaluate healthcare AI with curated clinical data.
Datavendor is developing a project-scoped validation and benchmarking capability for Clinical NLP, medical LLMs and suitable imaging AI models — using clinical reports and DICOM cohorts with associated study reports where available.
Status: Validation projects are currently being scoped and developed case by case. Feasibility depends on model task, data coverage, DICOM/report linkage, reference labels and required clinical review.
Evaluation areas
Report-based and suitable imaging AI workflows
Evaluation methods are selected to fit the actual model task and available reference evidence rather than applying one score across every use case.
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Imaging AI — DICOM Cohorts
Potential evaluation of suitable imaging tasks where eligible DICOM studies, required metadata, associated reports and fit-for-purpose reference labels are available.
Cohort-dependent🔗
Image + Report Evaluation
Explore multimodal task evaluation on selected cohorts with valid study/report linkage and an agreed protocol.
Linked studies🔬
Pathology Report AI
Pathology finding extraction, report structuring and diagnosis classification for curated subsets with suitable references.
Pathology🧪
Laboratory & Clinical NLP
Extraction and structuring of lab results, clinical entities, values, units and other report-based information where available.
Structured output🤖
Medical LLMs
Task correctness, consistency, unsupported statements, omissions and hallucination-related failure patterns against agreed references.
LLM evaluationProcess
From test cohort to validation report
A transparent workflow designed around data quality, reference quality and the intended model use.
01
Define task
Agree the AI task, inputs/outputs and evaluation criteria.
02
Choose cohort
Review reports, DICOM, linkage and metadata coverage.
03
Set references
Establish reference answers/labels and clinical-review needs.
04
Run evaluation
Assess the permitted model/API output against references.
05
Analyse failures
Summarize metrics, error patterns and limitations.
Evaluation measures
Measure what matters for the task
Metrics and review depth depend on the intended use, class balance, reference quality and available labels.
Task performance
Accuracy, precision, recall/sensitivity, specificity, F1 and class-level errors where appropriate.
Extraction and structure
Field-level correctness, missing/extra entities, terminology mapping and output schema compliance.
Robustness
Performance variation across defined report styles, cohort subsets and metadata-supported scenarios.
Hallucination-focused review
Check whether generated statements are supported by the agreed source or reference.
False positives and negatives
Identify error categories and recurring failure patterns, with clinical review where needed.
Evidence-led reporting
Document data scope, methodology, reference protocol, results and limitations.
Important: an existing report is not automatically a gold-standard label. Image/report linkage, reference labels and clinical adjudication requirements need to be assessed for the specific task. This is not a claim of regulatory certification or autonomous diagnostic safety.
Start a conversation
Request a Discussion →Planning to evaluate a healthcare AI model?
Tell us about your model, target task, data requirements and expected outputs. Datavendor can review the fit of available report and DICOM cohorts and discuss an appropriate evaluation design.