02/06/2026
AI in IVF: limited datasets = real risk for patients. ✅
Our systematic review found AI models for blastocyst assessment rely on small, non-representative, inconsistently annotated datasets—producing poor generalizability and overconfident predictions that fail across labs.
🔹 Risk of company-dependent black box AI:
- Fertility surgery: weak generalizability of surgical/outcome guidance
- IVF prognostication: overconfident success rates, suboptimal personalization
- Workflow/auditing: unreliable metrics from inconsistent labeling
- Embryo assessment: variable scores, disrupted workflows, unquantified risk
🔹 We steward Responsible AI by:
- Diverse, well-annotated local datasets
- Transparent multi-cohort validation
- Clinician-in-the-loop with explainability
- Continuous monitoring + retraining
- Consent & regulatory integrity
🎯 Why Building in-house AI Is Sustainable
(IVF chains, pharma companies, Innovator Ivf clinics ) We offer data governance, annotation standards, multi-center validation, clinician-in-the-loop workflows, and pilots for:
- Fertility surgery
- IVF outcome prognostication
- Workflow assessment & auditing
- Embryo assessment
At Cellsure Biotech, we're building indigenous AI for India—trained on representative local data, validated across clinics, with clinician oversight. ✅
Full review: https://lnkd.in/gSjPz9CG
Ready to collaborate on responsible AI? Let's connect. 💡
