AI holds enormous potential for diagnostics, decision support, and patient care — but risks such as bias, lack of transparency, and regulatory challenges can compromise patient safety and stall adoption. SafeAI is the lifecycle platform that closes the gap.
Clinical AI is already at scale — and the failure modes that show up in the literature are not hypothetical. They map directly onto the tools being deployed today.
Clinicians over-trust algorithmic outputs even when they conflict with clinical judgment. Authority gradients make it worse.
Goddard et al., JAMIA 2012 →LLMs fabricate plausible-sounding dosages, contraindications, and citations — and agree with users' mistaken beliefs.
Ji 2022 · Sharma 2023 →Risk scores built on proxies that track access rather than need systematically under-treat the patients in greatest clinical need.
Obermeyer et al., Science 2019 →Training data under-represents demographic groups, so models fail to generalize to the patients they'll actually encounter.
Daneshjou et al., JAMA Derm 2022 →Models learn hospital- or scanner-specific artifacts rather than pathology. Internal performance looks strong; external collapses.
Zech et al., PLOS Medicine 2018 →New protocols, devices, and populations shift the relationships the model was trained on. Performance decays silently.
Gama et al., ACM Surveys 2014A single pooled model papers over real heterogeneity. Overall accuracy looks strong; specific subgroups are under-served.
Suresh & Guttag, ACM 2021 →Opaque models cannot be contested by clinicians or patients, and adverse events cannot be properly investigated.
Rudin, Nature MI 2019 →A lifecycle response to clinical AI risk — validation, monitoring, and integration support. Built on a working prototype.
Before a single patient is touched by the model, we benchmark it against local populations and workflows — so surprises surface in evaluation, not in the clinic.
A dashboard per clinic and per model — visible to clinical leadership, not buried in vendor logs. Incidents get routed, not hidden. Built on our working prototype.
Training, regulatory mapping, and an ongoing bench of advisors — all owned by you. Lessons from one deployment transfer to the next through a shared case library.
A multidisciplinary team from Harvard and MIT, combining global-health operating experience, clinical machine-learning research, and production engineering — advised by the people setting the standards the field will operate under.
Harvard doctorate on AI safety in healthcare. Ten years in global health across the World Health Organization and the Clinton Health Access Initiative (Regional Manager, AHD Global Team). Ex-BCG.
HarvardFormer founder and CEO of Minexx, a fair-trade mining technology platform. Launched Health Intelligence Centres leveraging AI and real-time insights to transform care across Rwanda, Botswana, and CAR.
OperatorPhD researcher at the University of Pisa and Harvard / MGH, specializing in Bayesian deep learning and uncertainty quantification for medical AI.
Harvard / MGHPhD at Harvard's Artificial Intelligence in Medicine lab, working on deep learning for pediatric brain tumors and Diffuse Midline Glioma.
HarvardMIT-trained software engineer and data scientist. 20+ years of full-stack engineering leadership, including CTO roles in financial services and electronic medical records for US military clinicians.
MITCo-founder and CEO of the Coalition for Health AI, the leading US non-profit setting consensus standards for responsible AI in healthcare across nearly 3,000 member organizations.
CHAIProfessor of Machine Learning and Healthcare at Harvard / MIT. Technical Director of MIMIC, the openly-accessible critical care database used by 4,000+ researchers.
Harvard / MITFounder and former CEO of ZocDoc, the online appointment platform that reached 40% of the US population across 1,900 cities and was valued at $1.8B, backed by Jeff Bezos and others.
FounderOur academic and innovation partners advancing clinical AI safety.
Backed and recognized by the leading academic innovation programs at Harvard and MIT.
These recognitions underscore SafeAI's mission to define the gold standard for trustworthy, compliant, and equitable AI in healthcare.
Join hospitals, startups, and regulators adopting SafeAI to ensure clinical AI is safe, fair, and compliant — before, during, and after deployment.