Privacy focused AI powered meeting intelligence using locally hosted Small Language Models. StenoAI Scribe for structured clinical notes available.

An open source openEHR server

Personal Cancer Genome Reporter (PCGR)

面向中文电子病历的命名实体识别

MNE-CPP: The C++ framework for real-time functional brain imaging.

Apache cTAKES is a Natural Language Processing (NLP) platform for clinical text.

Healthcare and biomedical datasets, for AI/ML

Official Codes for "Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes"

An electronic data capture platform for administering remote and in-person clinical instruments

A comprehensive cancer DNA/RNA analysis and reporting pipeline

Characterization of Germline variants

Clinically Adapted Model Enhanced from LLaMA

A collaborative learning framework for empowering biomedical research

OpenScribe is an open-source AI scribe that records patient encounters and generates structured clinical notes automatically. You keep full control over data, workflows, and patient privacy with no vendor lock-in.

Medkey Hospital Information System main repository: Practice Management for Practicioners & Hospitals, EHR, Patient Engagement

Mutation Identification Pipeline. Read the latest documentation:

[ACL 2024] This is the code for our paper ”RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records“.

Integrating AI to Clinical Workflow

A reported 96,480 people were diagnosed with melanoma in the United States in 2019, leading to 7230 reported deaths. Early-stage identification of suspicious pigmented lesions (SPLs) in primary care settings can lead to im- proved melanoma prognosis and a possible 20-fold reduction in treatment cost. Despite this clinical and economic value, efficient tools for SPL detection are mostly absent. To bridge this gap, we developed an SPL analysis system for wide-field images using deep convolutional neural networks (DCNNs) and applied it to a 38,283 dermatological dataset collected from 133 patients and publicly available images. These images were obtained from a variety of consumer-grade cameras (15,244 nondermoscopy) and classified by three board-certified dermatologists. Our system achieved more than 90.3% sensitivity (95% confidence interval, 90 to 90.6) and 89.9% specificity (89.6 to 90.2%) in distinguishing SPLs from nonsuspicious lesions, skin, and complex backgrounds, avoiding the need for cumbersome individual lesion imaging. We also present a new method to extract intrapatient lesion saliency (ugly duckling criteria) on the basis of DCNN features from detected lesions. This saliency ranking was validated against three board-certified dermatologists using a set of 135 individual wide-field images from 68 dermatolog- ical patients not included in the DCNN training set, exhibiting 82.96% (67.88 to 88.26%) agreement with at least one of the top three lesions in the dermatological consensus ranking. This method could allow for rapid and accurate assessments of pigmented lesion suspiciousness within a primary care visit and could enable improved patient triaging, utilization of resources, and earlier treatment of melanoma.

Official source for Spanish pretrained biomedical and clinical language models and resources made @ BSC-TEMU within the "Plan de las Tecnologías del Lenguaje" (Plan-TL).

No description provided.

Tool-set to support Bayesian evidence synthesis in R

Applied modelling in drug development: flexible Bayesian regression modelling in Stan via brms

This is a broad purpose web application for various kinds of clinical use cases. It has a Flask server, a React front end, SurrealDB for a multimodel database, LLM integration, MCP, RAG, OSCAR EMR integration, and much, much more.

Community-maintained list of resources that the CI4CC organization and the larger cancer informatics community have found useful or are developing.

Reconstruction and analysis of viral and host genomes at multi-organ level

A Java application designed to streamline the management of clinics offering dental and orthodontic services. This project, developed using Java Swing, provides essential tools for such clinics, making it ideal for single-doctor practices. It helps manage patients, appointments, records and more.

This project develops compact transformer models tailored for clinical text analysis, balancing efficiency and performance for healthcare NLP tasks.

Metagenomics/viromics pipeline that focuses on automation, user-friendliness and a clear audit trail. Jovian aims to empower classical biologists and wet-lab personnel to do metagenomics/viromics analyses themselves, without bioinformatics expertise.

We jailbreak your medical records and genomics data. For free.