The challenges of deep learning in artificial intelligence and autonomous actions in surgery: a literature review

Abstract

Aim: Artificial intelligence (AI) is rapidly evolving in healthcare worldwide, especially in surgery. This article reviews important terms used in machine learning and the challenges of deep learning in surgery.

Methods: A review of the English literature was carried out focused on the terms “challenges of deep learning” and “surgery” using Medline and PubMed between 2018 and 2022.

Results: In total, 54 articles discussed the challenges of deep learning in general. We include 25 articles from various surgical specialties discussing challenges corresponding to their respective specialties.

Conclusion: The increased utilization of AI in surgery is faced with a wide variety of technical, ethical, clinical, and business-related challenges. The best way to expedite its expansion in surgery in the safest and most cost-efficient manner is by ensuring that as many surgeons as possible have a clear understanding of basic AI concepts and how they can be applied to the preoperative, intraoperative, postoperative, and long-term follow-up phases of the surgical patient care.

Keywords

Challenges of deep learning / surgery / artificial intelligence / surgical data science / image annotation / data standardization

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