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Awesome Medical Tools Awesome

An awesome list of clinical and medical tools built by practicing physicians (MD/DO).

Browse the catalog interactively: sinabarimd.github.io/awesome-medical-tools (filter by specialty, application type, training stage, stars).

For a long time, building software was a specialty of its own. Doctors who wrote production code were the exception. AI is changing that calculation. Tools like Claude Code, Copilot, and Cursor compress the technical lift enough that a working physician can ship a real clinical tool in a weekend.

The result: the people closest to the problems can now build the solutions. A clinical decision-support tool written by someone who has worked a night shift, an imaging pipeline written by a radiologist, or a patient-education app written by a doctor who has had the same conversation a thousand times brings design instincts that are hard to manufacture from outside the work. This list catalogs that emerging cohort. Every entry is authored by a verified physician or physician-in-training (MD or DO) who chose to ship their work in public.

Inclusion is based on self-declared credentials in the author's GitHub bio, profile README, or linked website. See CONTRIBUTING.md for the verification policy and removal procedure.

This list is maintained by Dr. Sina Bari, MD, a Stanford-trained surgeon and Healthcare AI VP, and updated monthly via an automated pipeline with manual review.

Contents

Cardiology

  • baljash-cheema/StageD_HF - Machine learning models to identify advanced heart failure (Stage C/D) using established risk factors and clinical data. By Dr. Josh Cheema, MD, MSCI, MSAI, Cardiology.
  • conlinm/ARB_project_code - Analyze association between ARB antihypertensive use and renal cell carcinoma incidence using OMOP data. By Dr. Michael Conlin.
  • Dai-Kusumoto/Deep-SeSMo - CNN-based quantitative scoring system for detecting cellular senescence from microscopic phase-contrast images for drug screening. By Dr. Dai Kusumoto, Cardiology.
  • david-shrk/ecgaxistrainer - Web app for training ECG axis deviation interpretation across multiple languages. By Dr. David Schaack, Cardiology (invasive electrophysiology).
  • drankush/LAViz - AI-powered cardiac MRI segmentation and 3D visualization tool for left atrium analysis with clinical interpretation. By Dr. Ankush, Radiology.
  • drankush/MIRACLE-API - API providing peer-reviewed reference values and z-scores for standardizing cardiovascular MRI measurement interpretation. By Dr. Ankush, Radiology.
  • drankush/MIRACLE-sdk - Python SDK for accessing standardized cardiovascular MRI reference values and z-score calculations for cardiac imaging research. By Dr. Ankush, Radiology.
  • drankush/MIRACLE-webapp - Web calculator for pediatric cardiac MRI z-scores and percentiles using MIRACLE reference database. By Dr. Ankush, Radiology.
  • ganeshbmc/DeepIVUS - Deep learning platform for segmentation and analysis of coronary artery intravascular ultrasound (IVUS) images. By Dr. Ganesh Paramasivam, Interventional Cardiology.
  • gcaptur/Paediatric-HCM-Proteomics - Analyze plasma proteomics data to identify diagnostic and prognostic biomarkers for pediatric hypertrophic cardiomyopathy using machine learning. By Dr. Gabriella Captur, Cardiology.
  • Gizmopath/Cardiac-Amyloid - Neural network for detecting and segmenting amyloid deposits in cardiac tissue from digital pathology slides. By Dr. Giorgio Cazzaniga, Pathology (Digital/Anatomic).
  • jgourassas/corsim - Simulate coronary angiographic views and fluoroscopic angles to train cardiology fellows on optimal imaging projections. By Dr. John Gourassas, Cardiology (Invasive).
  • jgourassas/zao_ecg_axis - Determine electrocardiographic QRS axis from frontal ECG leads using circular reference system. By Dr. John Gourassas, Cardiology (Invasive).
  • jphdotam/cwspy - Parse cardiac MR contours from CVI42 proprietary files for research workflows. By Dr. James, Cardiology (Cardiac MRI).
  • MargoSolo/PAHminer - Literature-mining pipeline discovering causal genes for pulmonary arterial hypertension beyond standard diagnostic panels. By Dr. Margarita, Pediatrics / Clinical Genetics.
  • mepepin/AVS_Epigenomics - Reproducible analysis pipeline for circulating T-cell epigenomics in aortic valve stenosis with methylation-based severity scoring. By Dr. Mark E. Pepin, Cardiovascular disease / Cardiology (based on repo focus on HCM, aortic valve stenosis, cardiovascular risk), Fellow.
  • mepepin/CVD_RISK - Shiny app computing 10 and 30-year cardiovascular risk scores using validated AHA PREVENT and ACC/AHA algorithms. By Dr. Mark E. Pepin, Cardiovascular disease / Cardiology (based on repo focus on HCM, aortic valve stenosis, cardiovascular risk), Fellow.
  • mepepin/HCM_CPX.Diastology - Analyze left ventricular diastolic dysfunction and cardiopulmonary fitness in hypertrophic cardiomyopathy using exercise testing and echocardiography data. By Dr. Mark E. Pepin, Cardiovascular disease / Cardiology (based on repo focus on HCM, aortic valve stenosis, cardiovascular risk), Fellow.
  • mepepin/NNT_HFpEF - RNA-sequencing analysis pipeline for NNT gene and heart failure with preserved ejection fraction (HFpEF) research. By Dr. Mark E. Pepin, Cardiovascular disease / Cardiology (based on repo focus on HCM, aortic valve stenosis, cardiovascular risk), Fellow.
  • mepepin/snRNA_ACTA2.p.Met49Thr - Single-nucleus RNA-seq analysis of ACTA2 mutation in thoracic aortic tissue for inherited aortic disease research. By Dr. Mark E. Pepin, Cardiovascular disease / Cardiology (based on repo focus on HCM, aortic valve stenosis, cardiovascular risk), Fellow.
  • Oristrell/CHADS - Calculate CHADS2-VASc and HAS-BLED stroke and bleeding risk scores simultaneously for atrial fibrillation patients. By Dr. Joaquim Oristrell, MD, PhD, Internal Medicine.
  • Oristrell/CHADS_ca - Calculate CHADS2-VASc and HAS-BLED stroke/bleeding risk scores simultaneously for atrial fibrillation patients. By Dr. Joaquim Oristrell, MD, PhD, Internal Medicine.
  • Oristrell/CHADS_es - Calculate CHADS2-VASc and HAS-BLED stroke/bleeding risk scores simultaneously for atrial fibrillation patients. By Dr. Joaquim Oristrell, MD, PhD, Internal Medicine.
  • Raffi-Hagopian/AI-CAC - Deep learning model for automated coronary artery calcium segmentation and scoring on routine chest CT scans. By Dr. Raffi Hagopian, Cardiology.
  • rguide/Automated-cell-capacity-analyser - Automated analysis of cardiomyocyte cell membrane capacitance from electrophysiology recordings. By Dr. Roman Kula, Anaesthesiology; ICU Medicine; Experimental Cardiology.
  • therlaup/DICOM2D-NeRF - Extract and align ECG data with rotational angiography DICOM frames for cardiac imaging analysis and 4D reconstruction. By Dr. Pascal Thériault-Lauzier, Cardiology.
  • uotw/m.mode.ify - Generate M-mode ultrasound images post hoc from 2D clips by drawing a line through anatomy of interest, enabling motion-mode analysis for cardiac and fetal measurements. By Dr. Ben Smith, Emergency Medicine.
  • Urban90/deepFlow - Extract aortic flow parameters from cardiac MRI using deep learning on DICOM images. By Dr. Bruna Gomes, Cardiology.
  • YukiSahashi/slowmotion_echocardiography - Generate slow-motion echocardiography video frames using AI interpolation to improve diagnostic accuracy in stress testing and high heart-rate patients. By Dr. Yuki Sahashi, Cardiology.

Critical Care / ICU

Cross-specialty

Dermatology

Emergency Medicine

Gastroenterology

Genomics

Neurology

Obstetrics / Gynecology

Oncology

Ophthalmology

Pathology

Pediatrics

Primary Care / Internal Medicine

Psychiatry / Mental Health

Public Health / Epidemiology

Radiology / Imaging

Rheumatology

Rheumatology / Autoimmune

Sports Medicine

Surgery

Urology

About

Curated catalog of medical tools built by verified physicians (MD/DO). Discover, verify, categorize, render pipeline. Browse: sinabarimd.github.io/awesome-medical-tools

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