Developing current procedural terminology codes that describe the work performed by machines
Open accessAbstract
The “Taxonomy of Artificial Intelligence for Medical Services and Procedures” became part of the Current Procedural Terminology (CPT®) code set effective January 1, 2022. It provides a framework for discrete and differentiable CPT codes which; are consistent with the features of the devices’ output, characterize interaction between the device and the physician or other qualified health care professional, and foster appropriate payment. Descriptors include “Assistive”, “Augmentative”, and “Autonomous”. As software increasingly augments the provision of medical services the taxonomy will foster consistent language in coding enabling patient, provider, and payer access to the benefits of innovation.
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The 2016 Abramoff et al study was limited by its retrospective nature and therefore could not demonstrate the system's clinical potential.
“However, this study remained limited given its retrospective nature and therefore could not demonstrate the system’s clinical potential.”
From Autonomous Artificial Intelligence in Diabetic Retinopathy Testing—Lessons Learned on Successful Health System Adoption (2026) · cites this paper as [14]
The Introduction discusses an autonomous diabetic retinal-exam product and its CPT code, but does not discuss a 2016 Abramoff study. Searches for the study’s retrospective design and its ability to demonstrate clinical potential found no passage addressing either point. (LLM, conf=0.76)
Experimental — accuracy not yet measuredIn a 2016 study by Abramoff et al, an earlier version of LumineticsCore reported a referable DR sensitivity of 96.8% and specificity of 87.0% against the Messidor-2 database.
“In a study by Abramoff et al in 2016, an earlier version of LumineticsCore reported a referable DR sensitivity of 96.8% and specificity of 87.0% against the Messidor-2 database.”
From Autonomous Artificial Intelligence in Diabetic Retinopathy Testing—Lessons Learned on Successful Health System Adoption (2026) · cites this paper as [14]
The Introduction discusses an autonomous diabetic retinal exam product, and Current context mentions the IDx-DR device, but neither reports a 2016 study or diagnostic performance. Searches for the stated sensitivity, specificity, Messidor-2, and the 2016 Abramoff study found no passage addressing those results. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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- Authors
- Frank, R. A., Jarrin, R., Pritzker, J., Abramoff, M. D., Repka, M. X., Baird, P. D., Marlene Grenon, S., Mahoney, M. R., Mattison, J. E., & Silva, E.
- Affiliations
- American Medical Association (US)
- Aetna (United States) (US)
- Johns Hopkins University (US)
- The University of Texas at San Antonio Health Science Center (US)
- Corresponding author
- Frank, Richard A.
- ORCID
- Venue
- npj Digital Medicine
- Published
- 2022-12-03
- Volume
- 5
- Issue
- 1
- Pages
- 177-177
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC9719561
- OpenAlex
- W4310781642
- Cited by
- 23
- References
- 3
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 10 · 2023: 3
- FWCI
- 3.37
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Topics
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Keywords
- Current Procedural Terminology, CPT codes, artificial intelligence in healthcare, assistive AI, autonomous AI systems
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