Generative Artificial Intelligence Considerations Beyond Natural Language Processing: Computer Vision Applications in Health Care

Generative artificial intelligence describes methodological processes wherein the output of interest for a given dataset is unlabeled (or unknown) and therefore is an original representation of learned data. Beyond natural language processing, computer vision embodies an important technique leveraging generative artificial intelligence methods and has enabled novel use cases relevant to health care. Namely, the introduction of autoencoders, generalized adversarial networks, and diffusion models have propelled the field of medical image reconstruction. Generative imaging reconstruction has implications including, but not limited to, improvements in image quality, surgical planning, and registry augmentation in a risk-averse manner as it pertains to patient privacy concerns. Overall, generative artificial intelligence in health care can enhance the efficiency and quality of medical services and research; however, responsible application requires governance and regulatory frameworks for responsible and ethical use. LEVEL OF EVIDENCE: Level V, expert opinion.

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Publication Details

Journal
Arthroscopy The Journal of Arthroscopic and Related Surgery
Published
2026-05-10
DOI
https://doi.org/10.1002/arj.70230
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

Generative Artificial Intelligence Considerations Beyond Natural Language Processing: Computer Vision Applications in Health Care

Mark P. Cote, Prem N Ramkumar, Kyle N. Kunze
Arthroscopy The Journal of Arthroscopic and Related Surgery
Artificial Intelligence in Healthcare and Education
article

Generative Artificial Intelligence Considerations Beyond Natural Language Processing: Computer Vision Applications in Health Care

Mark P. Cote, Prem N Ramkumar, Kyle N. Kunze
article en

Abstract

Generative artificial intelligence describes methodological processes wherein the output of interest for a given dataset is unlabeled (or unknown) and therefore is an original representation of learned data. Beyond natural language processing, computer vision embodies an important technique leveraging generative artificial intelligence methods and has enabled novel use cases relevant to health care. Namely, the introduction of autoencoders, generalized adversarial networks, and diffusion models have propelled the field of medical image reconstruction. Generative imaging reconstruction has implications including, but not limited to, improvements in image quality, surgical planning, and registry augmentation in a risk-averse manner as it pertains to patient privacy concerns. Overall, generative artificial intelligence in health care can enhance the efficiency and quality of medical services and research; however, responsible application requires governance and regulatory frameworks for responsible and ethical use. LEVEL OF EVIDENCE: Level V, expert opinion.

Arthroscopy The Journal of Arthroscopic and Related Surgery
Massachusetts General Hospital (US), Creative Commons (US), Rush University (US)
Quality Education
Openalex Percentile: Top 8%
Artificial Intelligence in Healthcare and Education
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Generative Artificial Intelligence Considerations Beyond Natural Language Processing: Computer Vision Applications in Health Care — Mark P. Cote, Prem N Ramkumar, et al. · Arthroscopy The Journal of Arthroscopic and Related Surgery (2026) | TGRS Research Map | TGRS