The clinical expertise platform for safe, responsible healthcare AI. The trusted infrastructure to find, manage, and engage practicing specialty and subspecialty clinicians to build smarter, and more scalable AI.
Engage clinicians with real-world experience to train, stress-test, and validate AI that's built for real clinical environments and real patient care.
Ensure your AI learns from the right clinical signals. Clinicians identify relevant data, define clinical context, and identify incomplete or mislabeled inputs that can compromise model performance.
Use board-certified clinicians to label high-risk data, edge cases, and annotate diagnostics with the accuracy real-world care demands.
Train AI agents with real clinical judgment. Clinicians evaluate outputs for correctness, safety, and appropriateness, guiding systems to express uncertainty and escalate when needed.
Ensure your models stay clinically accurate. Clinicians assess reasoning, stress-test performance, and participate in ongoing QA to make sure your AI evolves alongside standards of care and clinical practice.
Bridge AI development and clinical practice by aligning model behavior with workflows, decision-making patterns, and real-world care delivery.
Support image labeling, edge-case validation, and clinical review for FDA-ready computer vision models.
Guide training data, annotation, and RLHF for ECG interpretation, arrhythmia detection, and heart failure prediction.
Label high-complexity biomarkers, refine precision medicine models, and review outputs for clinical-grade genomic interpretation.
Validate triage tools, remote monitoring agents, and symptom checkers that must perform safely under uncertainty and time pressure.
Assess inpatient decision-support tools, population health models, and care coordination agents across complex, multi-condition patients.
Ensure AI tools reflect real-world clinical reasoning for complex, nuanced conditions like epilepsy, stroke, and mental health disorders.
Contribute frontline perspectives on workflow integration, usability, and practical care delivery across real clinical environments.
As AI moves into frontline care, clinical oversight isn't optional, it's foundational.
MDisrupt's clinician network helps AI builders embed real-world medical insight at every stage of the model lifecycle, from training data and labeling to reinforcement learning and ongoing QA. Access subspecialists across 65+ domains, ready to guide, stress-test, and validate your model's clinical accuracy, safety, and real world relevance.
Access practicing, board-certified clinicians across a wide range of specialties and subspecialties, precisely matched to your use case so you get the right expertise at the right level of specificity.
Real-world practicing clinicians apply clinical judgment to identify unsafe behavior, flawed reasoning, and edge cases that directly improve model performance.
Find, engage, and deploy the right clinicians quickly. MDisrupt supports rapid onboarding and repeatable workflows so clinical input keeps pace with high-volume, iterative AI development.
From matching and scheduling to contracting, payment, and liability coverage, MDisrupt manages the operational complexity so your teams can stay focused on building and improving models.
Power your next model with expert insight that delivers clinical depth, speed, and real-world relevance.
