Artificial intelligence in surgery: promises and perils
Open accessAbstract
OBJECTIVE: The aim of this review was to summarize major topics in artificial intelligence (AI), including their applications and limitations in surgery. This paper reviews the key capabilities of AI to help surgeons understand and critically evaluate new AI applications and to contribute to new developments. SUMMARY BACKGROUND DATA: AI is composed of various subfields that each provide potential solutions to clinical problems. Each of the core subfields of AI reviewed in this piece has also been used in other industries such as the autonomous car, social networks, and deep learning computers. METHODS: A review of AI papers across computer science, statistics, and medical sources was conducted to identify key concepts and techniques within AI that are driving innovation across industries, including surgery. Limitations and challenges of working with AI were also reviewed. RESULTS: Four main subfields of AI were defined: (1) machine learning, (2) artificial neural networks, (3) natural language processing, and (4) computer vision. Their current and future applications to surgical practice were introduced, including big data analytics and clinical decision support systems. The implications of AI for surgeons and the role of surgeons in advancing the technology to optimize clinical effectiveness were discussed. CONCLUSIONS: Surgeons are well positioned to help integrate AI into modern practice. Surgeons should partner with data scientists to capture data across phases of care and to provide clinical context, for AI has the potential to revolutionize the way surgery is taught and practiced with the promise of a future optimized for the highest quality patient care.
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Video analysis of a laparoscopic procedure in real time has resulted in 92.8% accuracy in identification of all steps of the procedure and detection of missing or unexpected steps.
“As an example, a video analysis of a laparoscopic procedure in real time has resulted in 92.8% accuracy in identification of all the steps of the procedure and surprisingly, the detection of missing or unexpected steps [26].”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [26]
NLP is being used to analyze data from EMRs and gather large-scale information on the late-stage complications of a certain medical condition.
“NLP is being used to analyze data from EMRs and gather large-scale information on the late-stage complications of a certain medical condition [26].”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [26]
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- Authors
- Hashimoto, D. A., Rosman, G., Rus, D., & Meireles, O. R.
- Affiliations
- Massachusetts General Hospital (US)
- Massachusetts Institute of Technology (US)
- Corresponding author
- Hashimoto, Daniel A.
- Meireles, Ozanan R.
- Venue
- Annals of Surgery
- Published
- 2018-07
- Volume
- 268
- Issue
- 1
- Pages
- 70-76
- ISSN
- 0003-4932 (ISSN-L)
- 1528-1140
- PMC
- PMC5995666
- OpenAlex
- W2790209545
- Cited by
- 1,468
- References
- 60
- Citations by year
- 2026: 282 · 2025: 268 · 2024: 250 · 2023: 209 · 2022: 172 · 2021: 121 · 2020: 108 · 2019: 43 · 2018: 12
- FWCI
- 1.59
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Annals of Surgery (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Funders
- Massachusetts General Hospital
- National Institutes of Health
- National Institute of Diabetes and Digestive and Kidney Diseases (T32DK007754)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Surgery › Surgical Simulation and Training
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence in surgery, machine learning, artificial neural networks, natural language processing, computer vision, clinical decision support systems, big data analytics, surgical training, AI limitations
- MeSH terms
- Artificial Intelligence, Humans, Physician's Role, Surgical Procedures, Operative, Surgeons
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