Artificial Intelligence in Surgery: Promises and Perils

By Daniel A. Hashimoto, Guy Rosman, Daniela Rus, Ozanan R. Meireles. Annals of Surgery, 2018

1,077 citations recorded by the source corpus.

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.

Subfields: Health Informatics, Radiology, Nuclear Medicine and Imaging, Surgery

View Paper: https://www.semanticscholar.org/paper/d92c390fcb0ea1cb9da8ee4a2e85bebdfc65c96f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Health Informatics

1. Avishek Choudhury
2. Onur Asan
3. Blake Murdoch
4. Kaveh Memarzadeh
5. Adam Bohr

In Radiology, Nuclear Medicine and Imaging

1. Avishek Choudhury
2. Luís Pinto-Coelho
3. Onur Asan
4. Blake Murdoch
5. Kaveh Memarzadeh
6. Adam Bohr

In Surgery

1. Kaveh Memarzadeh
2. Adam Bohr

Citations in the corpus

• Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review (2020, background): “Machine learning enables computers to utilize labeled (supervised learning) or unlabeled (unsupervised learning) data to identify latent information or make predictions about the data without explicit programming [9].”
• Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare (2022, methodology): “…of wall motion abnormalities on echocardiograms, and segmenting the left ventricle to continuously measure ejection fraction in cardiac/thoracic surgeries [111]. al. discussed that ML-based methods like artificial neural networks and convolutional neural networks had been used in studies such as…”
• The rise of artificial intelligence in healthcare applications (2020, methodology): “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].”
• Artificial Intelligence and Surgical Decision-Making (2019, background): “60,62,63 Deep models have successfully performed patient phenotyping, disease prediction, and mortality prediction tasks.”
• Privacy and artificial intelligence: challenges for protecting health information in a new era (2021, background): “Radiation oncology, organ allocation, robotic surgery and several other healthcare domains also stand to be significantly impacted by AI technologies in the short to medium term [6–10].”
• The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review. (2019, neutral): “Inclusion criteria for the studies were as follows (1) Original articles published in English; (2) Radiology-/imaging-based clinical studies using AI models for automatic diagnosis of disease, detection of abnormalities/pathologies, measurement of pathological area/volume or identification of anatom”
• How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications (2023, background): “These tools enhance surgical precision, reduce procedural risks, and enable minimally invasive techniques, ultimately improving patient safety and surgical outcomes [13].”
• Artificial Intelligence in Health Care: Bibliometric Analysis (2020, background): “From 2015 to 2018, the appearance of autonomous robots, voice recognition, neural networks, and machine learning provided unprecedented opportunities for the prediction, diagnosis, and treatment or management of diseases [18,19].”