A Surgeon’s Guide to Artificial Intelligence-Driven Predictive Models

Abstract

Artificial intelligence (AI) focuses on processing and interpreting complex information as well as identifying relationships and patterns among complex data. Artificial intelligence- and machine learning (ML)-driven predictions have shown promising potential in influencing real-time decisions and improving surgical outcomes by facilitating screening, diagnosis, risk assessment, preoperative planning, and shared decision-making. Fundamental understanding of the algorithms, as well as their development and interpretation, is essential for the evolution of AI in surgery. In this article, we provide surgeons with a fundamental understanding of AI-driven predictive models through an overview of common ML and deep learning algorithms, model development, performance metrics and interpretation. This would serve as a basis for understanding ML-based research, while fostering new ideas and innovations for furthering the reach of this emerging discipline.

Exploring Artificial Intelligence in Anesthesia: A Primer on Ethics, and Clinical Applications

“…However, the primary advantage of AI lies in empowering anesthesiologists to adopt a proactive approach to address clinical issues. For example, AI has been used for presurgical evaluation [11][12][13], to predict patient responses to anesthesia [14], to automate drug dosing [15], to monitor patients during surgery [6], and other purposes [16][17][18][19][20][21][22][23][24][25][26][27] (Table 1). Pain Management AI can help adjust the dosage and administering of drugs to achieve optimal postoperative pain control.…”

“…[8] Research and Analysis AI can help analyze vast amounts of data from anesthesia records, patient charts, and other sources to identify patterns, trends, and insights that could help improve patient outcomes and safety. [26,27] Although with limitations due to classification difficulties, the applications of AI in anesthesia can be schematically summarized into Clinical Decision Support System (CDSS) applications and pharmacological and mechanical robotic systems. Furthermore, teleanesthesia includes strategies of telemedicine (preoperative assessment and post-operative monitoring), as well as device networking for improving logistics in the operating room and augmented reality approaches for training and assistance (Figure 1).…”

ChatGPT and large language models in orthopedics: from education and surgery to research

“…Hernigou and Scarlat suggested that ChatGPT could be a valuable asset in orthopedics. They explored various roles for ChatGPT, such as acting as author, reviewer, publisher, and editor, and its potential to address numerous questions related to the technology [49]. LLMs are used in ChatGPT, a general‐domain AI model that has attracted considerable interest.…”

“…6ChatGPT Assists orthopedic practitioners by providing rapid access to large amounts of medical information, research papers, and accurate treatment guidelines. Additionally, it supports clinicians in making correct diagnoses, choosing suitable treatment options, and developing personalized care plans for patientsResearch[49] 7 ChatGPT Provides support for queries related to total knee arthroplasty and total hip arthroplasty by question type and topic to provide credible information Surgery[50] 8LLMs and ChatGPT Used to investigate the orthopedic surgery board examination (American Board) by challenging performance and knowledge that are equivalent to that of a firstyear orthopedic surgery resident Education[51] …”

Predicting Patient-Reported Outcomes Following Surgery Using Machine Learning

“…9-13 A detailed overview of the algorithms, model development, performance metrics, and interpretation is previously published by our research group. 14…”