Built for Social Security Disability Attorneys
MedEvidence AI extracts every functional limitation, maps it to RFC domains, flags Blue Book listing matches, and delivers page-level citations — so you walk into the ALJ hearing with every piece of evidence organized.
Built by a medical student·NOSSCR student member·HIPAA compliant
254 functional findings per case, organized by RFC domain — exertional, postural, manipulative, mental/cognitive, and more. Every finding links to the exact page it came from.
The system checks every diagnosis against SSA's Blue Book (Listing of Impairments) and flags potential matches with confidence scores. You see Listing 11.09 at 88% before the hearing, not after.
If there's no treating physician RFC opinion, no FCE on file, or documentation gaps that a DDS examiner will flag — we surface them before submission.
"Evidence gap identified: No treating physician RFC opinion on file. Consider requesting SSA-RFC-827 from the treating neurologist before the hearing."
Actual output from a 28-page neurology record processed in 4.7 minutes.
Who built this
Jay Ambat is a second-year osteopathic medical student at Rowan-Virtua School of Osteopathic Medicine and a NOSSCR student member. He built MedEvidence AI because the gap between what a medical record contains and what an attorney can extract manually is costing claimants their cases.
Medical training matters here. The system understands clinical language — EDSS scores, FCE findings, neuropsychological testing, treating source opinions — that generic summarizers miss. You don't need a medical degree to read your client's records. That's what this is for.
NOSSCR Student Member · Rowan-Virtua School of Osteopathic Medicine
| Manual Review | MedEvidence AI | |
|---|---|---|
| Turnaround | 3–5 days | ✓ ~5 minutes |
| Cost per case | $500–2,000 | ✓ $85 |
| RFC domain org | None | ✓ 18 domains |
| Blue Book mapping | None | ✓ Automated |
| Evidence gaps | Discovered late | ✓ Flagged pre-hearing |
| Citations | Narrative | ✓ Page-level, exact |