Research Topic

Clinical Applications of Artificial Intelligence in Surgery and Neurosurgery

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About this Research Topic

Artificial intelligence (AI) is a branch of computer science specifically concerning algorithms that permit machines to approximate human cognitive functions in domains including problem solving, decision making, object recognition and classification. The growth of AI has been exponential over the past 40 years, and AI techniques are increasingly finding their way into numerous aspects of our daily lives, evidenced by the rise of “smart” technology. Despite the growing sophistication of AI techniques like machine learning (ML) and deep learning (DL), its applications in healthcare have lagged behind other sectors.

Two factors make healthcare data ideal for AI application: Firstly, modern healthcare systems primarily utilize digital data recording and storage methods (e.g. electronic health records, PACS etc.), and secondly, high-resolution diagnostic modalities like advanced imaging and molecular analysis have created an exponential increase in the volume of data. Machine-based techniques are inherently well-suited to quantitative analysis of this digital “big data.” For example, a single magnetic resonance scan consists of dozens of slices, each containing hundreds-to-thousands of individual voxels. Though humans can read the scan and assess it for large-scale features like presence of a tumor or bleeding, it would be impractical or outright impossible for a human to objectively analyze each voxel of each slice. By comparison, not only can machines quantitatively analyze and compare each data point within a single scan relatively quickly, they can also compare this micro-scale data across many subjects, for diagnostic, decision making or prognostication purposes. Moreover, nascent applications of ML and DL to clinical data analysis have yielded performance advantages approximating or exceeding human ability.

Another potential application for AI in surgery may be in surgical adjuncts or robots. Surgical adjuncts, such as microscopes and image guidance among others, provide surgeons with a mechanical or physical advantage and permit quicker and more accurate procedures with fewer errors and ultimately improved outcomes. Recently, a number of AI-assisted surgical adjuncts have become commercially available, which may further enhance the technical aspects of surgery. Looking further ahead, AI may also increasingly find its way into the control algorithms for surgical robots, enabling realization of devices able to perform a range of soft or rigid-tissue procedures quickly and safely.

The aim of this Research Topic is to explore the potential applications of advanced AI techniques to the practice of surgery and neurosurgery. This may include basic translational research involving the application of AI methods to pathology, radiology or other clinical data for diagnostic, prognostic or decision-making purposes. It may also include work regarding nascent AI-enabled surgical adjuncts or robots. We also welcome any articles discussing the economic, social or political consequences that increasing use of AI may have upon the field of surgery and medicine.


Keywords: Artificial Intelligence, Surgery, Machine Learning, Neurosurgery, Deep Learning


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Artificial intelligence (AI) is a branch of computer science specifically concerning algorithms that permit machines to approximate human cognitive functions in domains including problem solving, decision making, object recognition and classification. The growth of AI has been exponential over the past 40 years, and AI techniques are increasingly finding their way into numerous aspects of our daily lives, evidenced by the rise of “smart” technology. Despite the growing sophistication of AI techniques like machine learning (ML) and deep learning (DL), its applications in healthcare have lagged behind other sectors.

Two factors make healthcare data ideal for AI application: Firstly, modern healthcare systems primarily utilize digital data recording and storage methods (e.g. electronic health records, PACS etc.), and secondly, high-resolution diagnostic modalities like advanced imaging and molecular analysis have created an exponential increase in the volume of data. Machine-based techniques are inherently well-suited to quantitative analysis of this digital “big data.” For example, a single magnetic resonance scan consists of dozens of slices, each containing hundreds-to-thousands of individual voxels. Though humans can read the scan and assess it for large-scale features like presence of a tumor or bleeding, it would be impractical or outright impossible for a human to objectively analyze each voxel of each slice. By comparison, not only can machines quantitatively analyze and compare each data point within a single scan relatively quickly, they can also compare this micro-scale data across many subjects, for diagnostic, decision making or prognostication purposes. Moreover, nascent applications of ML and DL to clinical data analysis have yielded performance advantages approximating or exceeding human ability.

Another potential application for AI in surgery may be in surgical adjuncts or robots. Surgical adjuncts, such as microscopes and image guidance among others, provide surgeons with a mechanical or physical advantage and permit quicker and more accurate procedures with fewer errors and ultimately improved outcomes. Recently, a number of AI-assisted surgical adjuncts have become commercially available, which may further enhance the technical aspects of surgery. Looking further ahead, AI may also increasingly find its way into the control algorithms for surgical robots, enabling realization of devices able to perform a range of soft or rigid-tissue procedures quickly and safely.

The aim of this Research Topic is to explore the potential applications of advanced AI techniques to the practice of surgery and neurosurgery. This may include basic translational research involving the application of AI methods to pathology, radiology or other clinical data for diagnostic, prognostic or decision-making purposes. It may also include work regarding nascent AI-enabled surgical adjuncts or robots. We also welcome any articles discussing the economic, social or political consequences that increasing use of AI may have upon the field of surgery and medicine.


Keywords: Artificial Intelligence, Surgery, Machine Learning, Neurosurgery, Deep Learning


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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