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Published case · AI-focused · Physician-collaborative

Help us find similar patients. Join a serious research effort.

CIMOS grew from firsthand experience navigating a complex medical journey.

Dr. Keith Choate

For patients and clinicians

Contact Dr. Keith Choate

If you have generalized idiopathic acanthosis nigricans—or treat a patient who does—contact Dr. Keith Choate, Chair of Dermatology and Professor of Genetics and Pathology at Yale School of Medicine.

Dr. Choate is a physician-scientist focused on the genetics of rare skin disease and senior author of the 2026 JAMA Dermatology report on de novo germline EGFR variants and generalized acanthosis nigricans.

Call Dr. Choate's office: +1 203-785-4092

What CIMOS is

CIMOS stands for Comfortable In My Own Skin. It helps patients with rare, poorly understood conditions use AI to organize medical records, photos, treatment history, wearable data, and physician input into a longitudinal research and treatment program.

Sensitive vault (planned)

Private sharing is planned work and will require patient-level approval plus recipient access controls before any sensitive records, photos, or treatment history are shared.

Physician-ready summaries

AI organizes scattered records into focused briefs, timelines, treatment tables, and research questions.

Rare-condition research networks

Verified physicians and researchers can help identify similar patients and build longitudinal cohorts.

Initial program: idiopathic acanthosis nigricans

The first CIMOS program focuses on apparent idiopathic acanthosis nigricans: thoroughly evaluated cases not explained by obesity, diabetes, or malignancy.

Find similar patients

Identify other patients with rare idiopathic presentations who may want to learn about physician-collaborative research.

Support genetic research

Help connect eligible patients with qualified academic genetics researchers and physician-led rare-condition research efforts.

Evaluate treatments

Track TCA peels, pain-management options, sedation-assisted care, and other treatment paths.

Track longitudinally

Measure disease progression, recovery, regrowth, recurrence, and outcomes over time.

AI treatment-analysis preview

The first physician-facing analysis will organize the clinical reasoning pathway behind this apparent idiopathic case: the diagnostics used to rule out typical causes, treatments previously tried and stopped, therapies currently being trialed, and options being considered but not yet attempted.

Use the left and right arrow keys to scroll the treatment analysis table.

Preliminary CIMOS analysis structure for converting medical records, procedural history, treatment notes, and more than 4,000 longitudinal before-and-after treatment images into a physician-reviewable summary.
Analysis areaWhat CIMOS is structuringCurrent status
Diagnostic exclusion pathwayPrior diagnostics used to support apparent idiopathic acanthosis nigricans and rule out typical causes such as obesity, diabetes, malignancy, medication effect, or other systemic drivers.Published case; repeated clinical evaluation; biopsy-supported AN diagnosis; physician-friendly summary in development.
Treatments tried and stoppedTopical, injectable, oral, systemic, laser, and procedural treatments that have been attempted, stopped, modified, or failed, including reasons for discontinuation and recurrence after response.Prior topical failures, systemic and biologic trials, isotretinoin history, laser procedures, chemical peels, and pain-limited procedural care to be organized into a structured treatment history.
Current and future treatment strategyActive treatment trials, pain-management protocols, longitudinal image tracking, and physician-reviewed candidate treatments not yet attempted.Current focus includes laser, TCA, phenol, dry ice, BLT, oral pain medications, lidocaine/tetracaine pain control, 4,000+ treatment images, and candidate therapies requiring physician review.

Access model

CIMOS currently uses request forms to identify relevant collaborators and similar-patient leads. The next version will add verified collaborator accounts for physicians and researchers.

Current View

Public summary, limited non-sensitive information, program goals, and request forms. This is the current public page.

Verified collaborator access

Approved physicians and researchers will be invited to sign in using Google, LinkedIn, or email magic link, then complete professional verification before accessing Level 1 materials.

Sensitive vault (planned)

Private sharing is planned work. It will require patient-level approval and recipient access controls before full records, sensitive photos, labs, genetics, wearable exports, or downloadable files can be shared.

Login confirms account access. Professional verification will be handled separately through physician, researcher, institutional, NPI, license, or public-profile review where applicable.

Join physicians and researchers working toward better treatments

CIMOS is seeking dermatologists, geneticists, researchers, VA clinicians, nurses, labs, and treatment-capable specialists who can help identify similar patients or evaluate the research approach.

Already approved?

Sign in to the private CIMOS evaluation workspace using your existing account.

Login to CIMOS

Join the phase II wait list

CIMOS.health is being built in phases. Join the wait list to be notified when verified collaborator access opens.

Join the wait list for access to phase II of Cimos.health

For physicians, researchers, nurses, labs, and clinical teams who want to be notified when the verified CIMOS workspace and phase II access become available.

Join the wait list

Privacy note: CIMOS uses submitted information to evaluate wait list requests and research/treatment relevance. Please do not submit identifiable patient information.

Evidence base

The initial CIMOS program builds on a patient/founder background that combines AI experience, military service, and firsthand urgency.

Patient / Founder background

CIMOS was created by a former MIT Assistant Professor, Army Ranger, and Army combat armor officer who served in Iraq. He previously coauthored research using novel data and machine learning to identify mental health risk among veterans, and is now applying that same data-driven approach to CIMOS.