Artificial Intelligence in Surgery: Promises and Perils

Abstract

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.

Figure 1

In supervised learning, human labeled data are fed to a machine learning algorithm to teach the computer a function, such as recognizing a gallbladder in an image or detecting a complication in a large claims database. In unsupervised learning, unlabeled data are fed to a machine learning algorithm, which then attempts to find a hidden structure to the data, such as identifying bright red (e.g. bleeding) as different from non-bleeding tissue.

Figure 2

Artificial neural networks are composed of many computational units known as “neurons” (dotted red circle) that receive data inputs (similar to dendrites in biological neurons), perform calculations, and transmit output (similar to axons) to the next neuron. Input level neurons receive data while hidden layer neurons (many different hidden layers can be used) conduct the calculations necessary to analyze the complex relationships in the data. Hidden layer neurons then send the data to an output layer that provides the final version of the analysis for interpretation.

Figure 3

Computer vision utilizes mathematical techniques to analyze visual images or video streams as quantifiable features such as color, texture, and position that can then be used within a dataset to identify statistically meaningful events such as bleeding.

Figure 4

Integration of multimodal data with AI can augment surgical decision-making across all phases of care both at the individual patient and at the population level. An integrated AI serving as a “collective surgical consciousness” serves as the conduit to add individual patient data to a population dataset while drawing from population data to provide clinical decision support during individual cases. CV: computer vision, ANN: artificial neural network, NLP: natural language processing, SP: signal processing.