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What is Mendel.ai?

Mendel.ai is an AI tool designed to extract structured data from clinical documents and match patients to relevant studies or treatment programmes. It reads unstructured clinical notes, test results, and medical records, then organises the information into usable data formats. The tool is built for healthcare providers, research institutions, and clinical trial coordinators who need to quickly identify eligible patients or gather consistent data across patient populations. The service handles the repetitive work of reviewing medical documents manually. Instead of staff reading through dozens of patient files to find candidates for a trial or research project, Mendel.ai can process documents in bulk and flag matching patients based on criteria you define. This is particularly useful in settings where clinical data is scattered across different systems or stored as free-text notes rather than structured fields.

Key Features

Clinical document parsing

extracts relevant information from notes, discharge summaries, and test results

Patient matching

identifies patients who meet specific clinical or demographic criteria

Data standardisation

converts unstructured text into consistent, queryable data formats

Bulk processing

handles multiple documents and patient records in one operation

Integration-ready

works with existing healthcare systems via API connections

Pros & Cons

Advantages

  • Free to use, making it accessible to smaller clinics and research teams without budget constraints
  • Reduces manual review time when screening patients for trials or specific conditions
  • Works with messy real-world clinical data rather than requiring perfectly formatted input
  • Handles sensitive medical information with appropriate data handling

Limitations

  • Accuracy depends on the quality and clarity of source documents; handwritten or poorly scanned notes may cause extraction errors
  • Requires careful setup of matching criteria to avoid missing eligible patients or creating false matches
  • Limited transparency on how the AI makes specific extraction or matching decisions

Use Cases

Clinical trial recruitment: quickly identify patients who meet enrollment criteria from a large patient database

Research cohort assembly: gather patient data for retrospective studies without manual chart review

Patient stratification: organise patients by clinical characteristics for treatment planning or resource allocation

Data migration: extract and standardise data when moving between electronic health record systems