Healthcare
The Challenge
Imaging backlogs are growing faster than radiology teams can read them, and clinicians struggle to find trusted information quickly at the point of care. Every AI output in a clinical setting also has to be reviewed, explained and traced back to its source.
How NueralSoft Is Solving It
We build computer-vision models that flag findings in medical images for clinician review, and generative AI assistants that answer from approved medical sources with citations. Every clinical decision stays with a qualified person.
Solutions
Working reference implementations across agentic AI, generative AI and machine learning, each available for a private demonstration.
Health Assistant with Human Review
An AI health assistant that provides guided medical information, with human-in-the-loop review before any answer is relied on.
Medical RAG Assistant
Answers clinical questions from an indexed medical reference, citing the page each answer comes from so clinicians can verify it.
Hospital Fracture Identification
Deep-learning model that identifies fractures on X-ray images to support emergency-room review and triage. Trained on about 20,000 X-ray images, with recall (sensitivity) as the release gate.
Medical Imaging Reference Models
Detection models built on public imaging and clinical datasets, each returning a confidence score for clinician review:
Built for Clinical Environments
- GroundedAnswers cite the medical source they come from.
- Clinician in controlAI flags and suggests; qualified people decide.
- Measured qualityRAGAS, DeepEval and LLM-as-a-Judge evaluation.
- Your controlsYour security, privacy and data controls in production.
See it working on your data
Every solution on this page is available for a private demonstration.
Request DemoSolutions shown are NueralSoft reference implementations, adapted to each client's data, systems and controls. Imaging models are built on public datasets and are not clinically validated or intended for diagnosis. Private demonstrations available on request.