MEDICAL AI SAFETY / RED TEAMING / CLINICAL EVALUATION

A plausible answer can still be clinically unsafe.

Physician-led testing of model behavior, safety boundaries, and reproducible failures in clinical contexts.

THE EVALUATION QUESTION

What changes when the clinical context changes?

Accuracy is one part of an evaluation. The decision to help, refuse, escalate, or qualify an answer also needs to be tested.

01 / RESPONSE

Unsafe compliance

Does the answer cross a clinically meaningful safety boundary?

02 / CONTEXT

Role-sensitive safeguards

Does a claimed credential or a change in context alter the response?

03 / RELIABILITY

Reproducibility

Does the behavior recur under matched, documented conditions?

COMMISSIONED WORK

A defined question. A traceable deliverable.

EVALUATE

Clinical AI safety review

A scoped assessment of outputs, clinical context, and safety-relevant failure patterns.

CHARACTERIZE

Medical red teaming

Matched conditions and repeat testing to characterize when a boundary changes.

BUILD

Benchmarks and simulations

Physician-authored scenarios, scoring rubrics, and interactive educational pathways.

INTERACTIVE SYSTEMS

See the interaction, then discuss the application.

CRUCIBLE · PUBLIC DEMOS

Clinical decisions before feedback

Staged case challenges with diagnostic commitment and configurable confidence and timing layers.

CASCADE · SELECTED PUBLIC COMPONENTS

Advanced therapy education

Mechanism-rich learning journeys. Public components demonstrate the format; full modules are scoped by request.

QUINTUPLE-BOARD-CERTIFIED PHYSICIAN

Why Olga Goodman, MD

Physician-founder & Clinical AI Evaluation Architect. Five U.S. board certifications, active rheumatology practice, and original evaluation-system development bring clinical breadth and practical accountability to AI review.

CLINICAL BREADTH

Reason across specialties

Rheumatology, internal medicine, pediatrics, pain management, and regenerative/aesthetic medicine inform review of the interactions between diagnosis, comorbidity, treatment, and procedural risk.

DIRECT CLINICAL EXPERIENCE

Read the evidence behind the answer

Personal interpretation of radiographs and musculoskeletal ultrasound, alongside ultrasound-guided procedures, informs scrutiny of unsupported inferences and mismatches between clinical and imaging evidence.

EVALUATION ARCHITECTURE

Make clinical judgment testable

Physician-authored reference answers, safety rubrics, severity anchors, and repeatable scenarios translate clinical concerns into findings that developers can investigate and retest.

Selected recognition: ACR Annual Image Competition — Grand Prize of the year, 2014; InterACT Annual Interdisciplinary Autoimmune Summit — Grand Prize, 2022.

FEATURED WORK

Evidence with its limits visible.

SAFETY RESEARCH · POSTER PREVIEW

Clinical-role claims and procedural access

A two-stage evaluation of text masking and unverified clinical-role framing. The public preview describes the question; detailed methods and results remain withheld in the source.

METHODS · OPEN-ACCESS PREPRINTS

Governance and reproducible reporting

Related RheumaView work on deterministic radiographic reporting and audit-ready provenance. Methodological background with a distinct scope.

A physician-founded research ecosystem.

RheumaView AI Safety is part of the RheumaView™ platform, connecting original research with independent evaluation and educational development. RheumaView presents the imaging platform; RheumaView Research hosts extended methods, publications, and demonstrations.

Rheumatology in Perspective

By Olga Goodman, MD · A RheumaView publication. The physician-authored publication at dr.rheumaview.com is another part of the RheumaView™ platform.

Put a clinical safety question to the test.

Define the system, the intended user, and the behavior you need to understand.

Professional research and education. This site does not provide patient-specific medical care. Scope & disclaimer

RheumaView AI Safety

A note on the public view

This website presents a selected portion of our work. Most underlying evaluation materials, methods, and findings are not publicly displayed and remain confidential. We share additional information only when appropriate and subject to applicable confidentiality arrangements.