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We are pioneering the future of healthcare through deep learning and domain specific reasoning, delivering insights that are early, fast, and highly interpretable.

What Sets Us Apart

01

Layered Clinical Reasoning

Our system mirrors the evaluation steps of a human specialist, with a deep pipeline that simulates differentials, condition hypothesis, and recommendations that bring instant clarity.

02

Recommendations

Produced results are tailored with a sophisticated clinical medicinal data, ensures cutting edge recommendation capablities.

03

Multimodal Imaging Fusion

We blend T1 axial MRI imaging, patient metadata, test results, and symptomatic reports to derive more precise predictions through structured fusion techniques, ensuring nothing essential is overlooked.

04

Explainable Outputs

From visual heatmaps to plain-language justifications, every result is explainable and designed to assist users with clarity and accountability.

05

Access Route

By recommending most relevant and the nearest dermatologist aligned towards solving your problem, we're creating monetary and moral value for both a dermatologist in a metropolitan city getting free, focused patient flow so they better focus on cases which matter.

06

Specialized Domains

Focused expertise in neurological and dermatological diagnostics, with plans to expand across domains

8
Plus datasets used in pretraining
3+
Plus Modalities Covered
2
Primary Diagnostic Domains




Imaging Architecture

Neurapex’s imaging architecture integrates modality aware vision language models for dermatology and radiology.


For dermatological analysis, the system leverages LLaVA Med and CLIP based encoders pretrained on dermoscopic, clinical, and histopathological imagery, enabling cross-modal alignment between visual features and medical language.


In radiology, the pipeline ingests DICOM formatted MRI and X-ray studies. Each slice is analyzed to generate structured semantic representations, which are subsequently contextualized across the study to enable cross slice reasoning.


This design prioritizes interpretability, modality compatibility, and scalable deployment within clinical workflows.

Meet the Founder

Jaiwardhan Tyagi, Founder. I'm an ML engineer, and I'm actively working to solve the uncertainty problem in health by creating an intelligence layer for primary healthcare.Know more

The Future

Built for the Curious. Designed for the Future. An AI specialist at your fingertips.