The Role of Artificial Intelligence in Surgery

New Horizons of Artificial Intelligence in Medicine and Surgery

Background: Ideas about Artificial intelligence appeared about half a century ago, but only now is it becoming an essential element of everyday life. The data provided are becoming a bigger pool and we need artificial intelligence that will help us with its superhuman powers. Its interaction with medicine is improving more and more, with medicine being a domain that continues to be perfected. Materials and Methods: The most important databases were used to perform this detailed search that addresses artificial intelligence in the medical and surgical fields. Discussion: Machine learning, deep learning, neural networks and computer vision are some of the mechanisms that are becoming a trend in healthcare worldwide. Developed countries such as Japan, France and Germany have already implemented artificial intelligence in their medical systems. The help it gives is in medical diagnosis, patient monitoring, personalized therapy and workflow optimization. Artificial intelligence will help surgeons to perfect their skills, to standardize techniques and to choose the best surgical techniques. Conclusions: The goal is to predict complications, reduce diagnostic times, diagnose complex pathologies, guide surgeons intraoperatively and reduce medical errors. We are at the beginning of this, and the potential is enormous, but we must not forget the impediments that may appear and slow down its implementation.

Personality characteristics of Dutch nurse anesthetists and surgical nurses when compared to the normative Dutch population, a quantitative survey study

BACKGROUND

Working in the perioperative context is complex and challenging. The continual evaluation in this environment underscores the need for adaptability to technological advancements, and requires substantial allocation of resources for training and education. This study aimed to explore personality characteristics of nurse anesthetists and surgical nurses that are instrumental for sustainable employability in technologically advanced environment.

METHODS

Exploratory, cross-sectional survey study including nurse anesthetists and surgical nurses, both certified and in training, and a sample of the normative Dutch population. Personality characteristics were identified with the Big Five Inventory, which consisted of 60 items answered on a five-point Likert scale (strongly disagree to strongly agree).

RESULTS

Specific personality traits were found for nurse anesthetists and surgical nurses when compared to the normative Dutch population. Traits of both nurse anesthetists and surgical nurses differed significantly on all domains of the Big Five Inventory, with the largest differences found within the dimension negative emotionally.

CONCLUSIONS

This study highlights the role of specific personality traits in maintaining employability within the rapidly evolving and technologically advanced landscape of healthcare. It emphasizes the relationship between individual traits and professional excellence, being crucial educational strategies for overall improvement in healthcare.

Ethical and legal issues regarding artificial intelligence (AI) and management of surgical data

The advent of AI in surgical practice is representing a major innovation. As its role expands and due to its several implications, strict compliance with ethical, legal and regulatory good practices is mandatory. Observance of ethical principles and legal rules will be a professional imperative for the application of AI in surgical practice, both clinically and scientifically.

Artificial intelligence in the diagnosis and treatment of acute appendicitis: a narrative review

Artificial intelligence is transforming healthcare. Artificial intelligence can improve patient care by analyzing large amounts of data to help make more informed decisions regarding treatments and enhance medical research through analyzing and interpreting data from clinical trials and research projects to identify subtle but meaningful trends beyond ordinary perception. Artificial intelligence refers to the simulation of human intelligence in computers, where systems of artificial intelligence can perform tasks that require human-like intelligence like speech recognition, visual perception, pattern-recognition, decision-making, and language processing. Artificial intelligence has several subdivisions, including machine learning, natural language processing, computer vision, and robotics. By automating specific routine tasks, artificial intelligence can improve healthcare efficiency. By leveraging machine learning algorithms, the systems of artificial intelligence can offer new opportunities for enhancing both the efficiency and effectiveness of surgical procedures, particularly regarding training of minimally invasive surgery. As artificial intelligence continues to advance, it is likely to play an increasingly significant role in the field of surgical learning. Physicians have assisted to a spreading role of artificial intelligence in the last decade. This involved different medical specialties such as ophthalmology, cardiology, urology, but also abdominal surgery. In addition to improvements in diagnosis, ascertainment of efficacy of treatment and autonomous actions, artificial intelligence has the potential to improve surgeons' ability to better decide if acute surgery is indicated or not. The role of artificial intelligence in the emergency departments has also been investigated. We considered one of the most common condition the emergency surgeons have to face, acute appendicitis, to assess the state of the art of artificial intelligence in this frequent acute disease. The role of artificial intelligence in diagnosis and treatment of acute appendicitis will be discussed in this narrative review.

DATA GOVERNANCE in digital surgery

Technological and computer advances have led to a "new era" of Surgery called Digital Surgery. In it, the management of information is the key. The development of Artificial Intelligence requires "Big Data" to create its algorithms. The use of digital technology for the systematic capture of data from the surgical process raises ethical issues of privacy, property, and consent. The use of these out-of-control data creates uncertainty and can be a source of mistrust and refusal by surgeons to allow its use, requiring a framework for the correct management of them. This paper exposes the current situation of Data Governance in Digital Surgery, the challenges posed and the lines of action necessary to resolve the areas of uncertainty that have arisen in the process, in which the surgeon must play a relevant role.

Stratified Evaluation of GPT's Question Answering in Surgery Reveals Artificial Intelligence (AI) Knowledge Gaps

Large language models (LLMs) have broad potential applications in medicine, such as aiding with education, providing reassurance to patients, and supporting clinical decision-making. However, there is a notable gap in understanding their applicability and performance in the surgical domain and how their performance varies across specialties. This paper aims to evaluate the performance of LLMs in answering surgical questions relevant to clinical practice and to assess how this performance varies across different surgical specialties. We used the MedMCQA dataset, a large-scale multi-choice question-answer (MCQA) dataset consisting of clinical questions across all areas of medicine. We extracted the relevant 23,035 surgical questions and submitted them to the popular LLMs Generative Pre-trained Transformers (GPT)-3.5 and GPT-4 (OpenAI OpCo, LLC, San Francisco, CA). Generative Pre-trained Transformer is a large language model that can generate human-like text by predicting subsequent words in a sentence based on the context of the words that come before it. It is pre-trained on a diverse range of texts and can perform a variety of tasks, such as answering questions, without needing task-specific training. The question-answering accuracy of GPT was calculated and compared between the two models and across surgical specialties. Both GPT-3.5 and GPT-4 achieved accuracies of 53.3% and 64.4%, respectively, on surgical questions, showing a statistically significant difference in performance. When compared to their performance on the full MedMCQA dataset, the two models performed differently: GPT-4 performed worse on surgical questions than on the dataset as a whole, while GPT-3.5 showed the opposite pattern. Significant variations in accuracy were also observed across different surgical specialties, with strong performances in anatomy, vascular, and paediatric surgery and worse performances in orthopaedics, ENT, and neurosurgery. Large language models exhibit promising capabilities in addressing surgical questions, although the variability in their performance between specialties cannot be ignored. The lower performance of the latest GPT-4 model on surgical questions relative to questions across all medicine highlights the need for targeted improvements and continuous updates to ensure relevance and accuracy in surgical applications. Further research and continuous monitoring of LLM performance in surgical domains are crucial to fully harnessing their potential and mitigating the risks of misinformation.

Technological advancements in surgical laparoscopy considering artificial intelligence: a survey among surgeons in Germany

PURPOSE

The integration of artificial intelligence (AI) into surgical laparoscopy has shown promising results in recent years. This survey aims to investigate the inconveniences of current conventional laparoscopy and to evaluate the attitudes and desires of surgeons in Germany towards new AI-based laparoscopic systems.

METHODS

A 12-item web-based questionnaire was distributed to 38 German university hospitals as well as to a Germany-wide voluntary hospital association (CLINOTEL) consisting of 66 hospitals between July and November 2022.

RESULTS

A total of 202 questionnaires were completed. The majority of respondents (88.1%) stated that they needed one assistant during laparoscopy and rated the assistants' skillfulness as "very important" (39.6%) or "important" (49.5%). The most uncomfortable aspects of conventional laparoscopy were inappropriate camera movement (73.8%) and lens condensation (73.3%). Selected features that should be included in a new laparoscopic system were simple and intuitive maneuverability (81.2%), automatic de-fogging (80.7%), and self-cleaning of camera (77.2%). Furthermore, AI-based features were improvement of camera positioning (71.3%), visualization of anatomical landmarks (67.3%), image stabilization (66.8%), and tissue damage protection (59.4%). The reason for purchasing an AI-based system was to improve patient safety (86.1%); the reasonable price was €50.000-100.000 (34.2%), and it was expected to replace the existing assistants' workflow up to 25% (41.6%).

CONCLUSION

Simple and intuitive maneuverability with improved and image-stabilized camera guidance in combination with a lens cleaning system as well as AI-based augmentation of anatomical landmarks and tissue damage protection seem to be significant requirements for the further development of laparoscopic systems.

Ethical, Legal, and Financial Considerations of Artificial Intelligence in Surgery

Machine learning systems have become integrated into some of the most vital decision-making aspects of humanity, including hiring decisions, loan applications, and automobile safety, to name just a few. As applications increase in both gravity and complexity, the data quality and algorithmic interpretability of the systems must rise to meet those challenges. This is especially vital for navigating the nuances of health care, particularly among the high stakes of surgical operations. In addition to inherent ethical challenges of enabling a "black box" system to influence decision-making in patient care, the creation of biased datasets leads to biased algorithms with the power to perpetuate discrimination and reinforce disparities. Transparency and responsibility are paramount to the implementation of artificial intelligence in surgical decision-making and autonomous robotic surgery. Machine learning has been permeating health care across diverse clinical and surgical contexts but continues to face sizable obstacles, including apprehension from patients and providers alike. To integrate the technology fully while upholding standard of care and patient-provider trust, one must acknowledge and address the ethical, financial, and legal implications of using artificial intelligence for patient care.

New Frontiers of Natural Language Processing in Surgery

The vast and ever-growing volume of electronic health records (EHR) have generated a wealth of information-rich data. Traditional, non-machine learning data extraction techniques are error-prone and laborious, hindering the analytical potential of these massive data sources. Equipped with natural language processing (NLP) tools, surgeons are better able to automate, and customize their review to investigate and implement surgical solutions. We identify current perioperative applications of NLP algorithms as well as research limitations and future avenues to outline the impact and potential of this technology for progressing surgical innovation.

Deep Learning Applications in Surgery: Current Uses and Future Directions

Deep learning (DL) is a subset of machine learning that is rapidly gaining traction in surgical fields. Its tremendous capacity for powerful data-driven problem-solving has generated computational breakthroughs in many realms, with the fields of medicine and surgery becoming increasingly prominent avenues. Through its multi-layer architecture of interconnected neural networks, DL enables feature extraction and pattern recognition of highly complex and large-volume data. Across various surgical specialties, DL is being applied to optimize both preoperative planning and intraoperative performance in new and innovative ways. Surgeons are now able to integrate deep learning tools into their practice to improve patient safety and outcomes. Through this review, we explore the applications of deep learning in surgery and related subspecialties with an aim to shed light on the practical utilization of this technology in the present and near future.

Breaking down the silos of artificial intelligence in surgery: glossary of terms

BACKGROUND

The literature on artificial intelligence (AI) in surgery has advanced rapidly during the past few years. However, the published studies on AI are mostly reported by computer scientists using their own jargon which is unfamiliar to surgeons.

METHODS

A literature search was conducted in using PubMed following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement. The primary outcome of this review is to provide a glossary with definitions of the commonly used AI terms in surgery to improve their understanding by surgeons.

RESULTS

One hundred ninety-five studies were included in this review, and 38 AI terms related to surgery were retrieved. Convolutional neural networks were the most frequently culled term by the search, accounting for 74 studies on AI in surgery, followed by classification task (n = 62), artificial neural networks (n = 53), and regression (n = 49). Then, the most frequent expressions were supervised learning (reported in 24 articles), support vector machine (SVM) in 21, and logistic regression in 16. The rest of the 38 terms was seldom mentioned.

CONCLUSIONS

The proposed glossary can be used by several stakeholders. First and foremost, by residents and attending consultant surgeons, both having to understand the fundamentals of AI when reading such articles. Secondly, junior researchers at the start of their career in Surgical Data Science and thirdly experts working in the regulatory sections of companies involved in the AI Business Software as a Medical Device (SaMD) preparing documents for submission to the Food and Drug Administration (FDA) or other agencies for approval.

Global Research Trends of Gender-Related Artificial Intelligence in Medicine Between 2001-2020: A Bibliometric Study

This study aimed to assess the research on medical Artificial intelligence (AI) related to sex/gender and explore global research trends over the past 20 years. We searched the Web of Science (WoS) for gender-related medical AI publications from 2001 to 2020. We extracted the bibliometric data and calculated the annual growth of publications, Specialization Index, and Category Normalized Citation Impact. We also analyzed the publication distributions by institution, author, WoS subject category, and journal. A total of 3,110 papers were included in the bibliometric analysis. The number of publications continuously increased over time, with a steep increase between 2016 and 2020. The United States of America and Harvard University were the country and institution that had the largest number of publications. Surgery and urology nephrology were the most common subject categories of WoS. The most occurred keywords were machine learning, classification, risk, outcomes, diagnosis, and surgery. Despite increased interest, gender-related research is still low in medical AI field and further research is needed.

Modern Machine Learning Practices in Colorectal Surgery: A Scoping Review

Objective: The use of machine learning (ML) has revolutionized every domain of medicine. Surgeons are now using ML models for disease detection and outcome prediction with high precision. ML-guided colorectal surgeries are more efficient than conventional surgical procedures. The primary aim of this paper is to provide an overview of the latest research on “ML in colorectal surgery”, with its viable applications. Methods: PubMed, Google Scholar, Medline, and Cochrane library were searched. Results: After screening, 27 articles out of 172 were eventually included. Among all of the reviewed articles, those found to fit the criteria for inclusion had exclusively focused on ML in colorectal surgery, with justified applications. We identified existing applications of ML in colorectal surgery. Additionally, we discuss the benefits, risks, and safety issues. Conclusions: A better, more sustainable, and more efficient method, with useful applications, for ML in surgery is possible if we and data scientists work together to address the drawbacks of the current approach. Potential problems related to patients’ perspectives also need to be resolved. The development of accurate technologies alone will not solve the problem of perceived unreliability from the patients’ end. Confidence can only be developed within society if more research with precise results is carried out.

The role of artificial intelligence in pancreatic surgery: a systematic review

Artificial intelligence (AI), including machine learning (ML), is being slowly incorporated in medical practice, to provide a more precise and personalized approach. Pancreatic surgery is an evolving field, which offers the only curative option for patients with pancreatic cancer. Increasing amounts of data are available in medicine: AI and ML can help incorporate large amounts of information in clinical practice. We conducted a systematic review, based on PRISMA criteria, of studies that explored the use of AI or ML algorithms in pancreatic surgery. To our knowledge, this is the first systematic review on this topic. Twenty-five eligible studies were included in this review; 12 studies with implications in the preoperative diagnosis, while 13 studies had implications in patient evolution. Preoperative diagnosis, such as predicting the malignancy of IPMNs, differential diagnosis between pancreatic cystic lesions, classification of different pancreatic tumours, and establishment of the correct management for each of these lesions, can be facilitated through different AI or ML algorithms. Postoperative evolution can also be predicted, and some studies reported prediction models for complications, including postoperative pancreatic fistula, while other studies have analysed the implications for prognosis evaluation (from predicting a textbook outcome, the risk of metastasis or relapse, or the mortality rate and survival). One study discussed the possibility of predicting an intraoperative complication-massive intraoperative bleeding. Artificial intelligence and machine learning models have promising applications in pancreatic surgery, in the preoperative period (high-accuracy diagnosis) and postoperative setting (prognosis evaluation and complication prediction), and the intraoperative applications have been less explored.

High-Powered Magnet Exposures in Children: A Multi-Center Cohort Study

BACKGROUND AND OBJECTIVES

High-powered magnets were effectively removed from the US market by the Consumer Product Safety Commission (CPSC) in 2012 but returned in 2016 after federal court decisions. The United States Court of Appeals for the 10th Circuit cited imprecise data among other reasons as justification for overturning CPSC protections. Since then, incidence of high-powered magnet exposure has increased markedly, but outcome data are limited. In this study, we aim to describe the epidemiology and outcomes in children seeking medical care for high-powered magnets after reintroduction to market.

METHODS

This is a multicenter, retrospective cohort study of patients aged 0 to 21 years with a confirmed high-powered magnet exposure (ie, ingestion or insertion) at 25 children's hospitals in the United States between 2017 and 2019.

RESULTS

Of 596 patients with high-powered magnet exposures identified, 362 (60.7%) were male and 566 (95%) were <14 years of age. Nearly all sought care for magnet ingestion (n = 574, 96.3%), whereas 17 patients (2.9%) presented for management of nasal or aural magnet foreign bodies, 4 (0.7%) for magnets in their genitourinary tract, and 1 patient (0.2%) had magnets in their respiratory tract. A total of 57 children (9.6%) had a life-threatening morbidity; 276 (46.3%) required an endoscopy, surgery, or both; and 332 (55.7%) required hospitalization. There was no reported mortality.

CONCLUSIONS

Despite being intended for use by those >14 years of age, high-powered magnets frequently cause morbidity and lead to high need for invasive intervention and hospitalization in children of all ages.

Surgical Process Modeling for Open Spinal Surgeries

Modern operating rooms are becoming increasingly advanced thanks to the emerging medical technologies and cutting-edge surgical techniques. Current surgeries are transitioning into complex processes that involve information and actions from multiple resources. When designing context-aware medical technologies for a given intervention, it is of utmost importance to have a deep understanding of the underlying surgical process. This is essential to develop technologies that can correctly address the clinical needs and can adapt to the existing workflow. Surgical Process Modeling (SPM) is a relatively recent discipline that focuses on achieving a profound understanding of the surgical workflow and providing a model that explains the elements of a given surgery as well as their sequence and hierarchy, both in quantitative and qualitative manner. To date, a significant body of work has been dedicated to the development of comprehensive SPMs for minimally invasive baroscopic and endoscopic surgeries, while such models are missing for open spinal surgeries. In this paper, we provide SPMs common open spinal interventions in orthopedics. Direct video observations of surgeries conducted in our institution were used to derive temporal and transitional information about the surgical activities. This information was later used to develop detailed SPMs that modeled different primary surgical steps and highlighted the frequency of transitions between the surgical activities made within each step. Given the recent emersion of advanced techniques that are tailored to open spinal surgeries (e.g., artificial intelligence methods for intraoperative guidance and navigation), we believe that the SPMs provided in this study can serve as the basis for further advancement of next-generation algorithms dedicated to open spinal interventions that require a profound understanding of the surgical workflow (e.g., automatic surgical activity recognition and surgical skill evaluation). Furthermore, the models provided in this study can potentially benefit the clinical community through standardization of the surgery, which is essential for surgical training.

Adopt or Abandon? Surgeon-Specific Trends in Robotic Bariatric Surgery Utilization Between 2010 and 2019

Background: It is unknown if surgeons are more likely to adopt or abandon robotic techniques given that bariatric procedures are already performed by surgeons with advanced laparoscopic skills. Methods: We used a statewide bariatric-specific data registry to evaluate surgeon-specific volumes of robotic bariatric cases between 2010 and 2019. Operative volume, procedure type, and patient characteristics were compared between the highest utilizers of robotic bariatric procedures (adopters) and surgeons who stopped performing robotic cases, despite demonstrating prior use (abandoners). Results: A total of 44 surgeons performed 3149 robotic bariatric procedures in Michigan between 2010 and 2019. Robotic utilization peaked in 2019, representing 7.24% of all bariatric cases. We identified 7 surgeons (16%) who performed 95% of the total number of robotic cases (adopters) and 12 surgeons (27%) who stopped performing bariatric cases during the study period (abandoners). Adopters performed a higher proportion of gastric bypass both robotically (22.9% versus 3.1%, P < .001) and laparoscopically (27.5% versus 15.1%, P < .001), when compared with abandoners. Surgeon experience (no. of years in practice), type of practice (teaching versus nonteaching hospital), and patient populations were similar between groups. Conclusions: Robotic bariatric utilization increased during the study period. The majority of robotic cases were performed by a small number of surgeons who were more likely to perform more complex cases such as gastric bypass in their own practice. Robotic adoption may be influenced by surgeon-specific preferences based upon procedure-specific volumes and may play a greater role in performing more complex surgical procedures in the future.

Artificial intelligence-based computer vision in surgery: Recent advances and future perspectives

Technology has advanced surgery, especially minimally invasive surgery (MIS), including laparoscopic surgery and robotic surgery. It has led to an increase in the number of technologies in the operating room. They can provide further information about a surgical procedure, e.g. instrument usage and trajectories. Among these surgery-related technologies, the amount of information extracted from a surgical video captured by an endoscope is especially great. Therefore, the automation of data analysis is essential in surgery to reduce the complexity of the data while maximizing its utility to enable new opportunities for research and development. Computer vision (CV) is the field of study that deals with how computers can understand digital images or videos and seeks to automate tasks that can be performed by the human visual system. Because this field deals with all the processes of real-world information acquisition by computers, the terminology "CV" is extensive, and ranges from hardware for image sensing to AI-based image recognition. AI-based image recognition for simple tasks, such as recognizing snapshots, has advanced and is comparable to humans in recent years. Although surgical video recognition is a more complex and challenging task, if we can effectively apply it to MIS, it leads to future surgical advancements, such as intraoperative decision-making support and image navigation surgery. Ultimately, automated surgery might be realized. In this article, we summarize the recent advances and future perspectives of AI-related research and development in the field of surgery.

Minimally invasive gastrointestinal surgery: From past to the future

The improvement of the science and art of surgery began over 150 years ago. Surgical core tasks, “cutting and sewing” with hand and direct contact with the organs, have remained the same. However, in the 21st century, there has been a shifting paradigm in the methodology of surgery. The joint union between innovators, engineers, industry, and patient demands resulted in minimally invasive surgery (MIS). This method has influenced the techniques in every aspect of abdominal surgery, such as surgeons are not required to direct contact or see the structures on which they operate. Advances in the endoscope, imaging, and improved instrumentations convert the essential open surgery into the endoscopic method. Furthermore, computers and robotics show a promising future to facilitate complex procedures, enhance accuracy in microscale operations, and develop a simulation to improve the ability to face sophisticated approaches. MIS has been replacing open surgery due to improved survival, fewer complications, and rapid recoveries in recent years. Minimally invasive surgery's further research in diagnostic and therapeutic modalities is under investigation to achieve genuinely “noninvasive” surgery. Thus, MIS has gained interest in recent days and has been improving with promising outcomes.

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Minimally invasive surgery has interfered with multiple aspects of the surgical approach.

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Advancement in the endoscope, imaging, and other instrumentations shifting the current methodological conventional surgery.

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The benefit over risk is the promising primary outcome to achieve an exceptional quality of life.

The Development of Electronic Health and Artificial Intelligence in Surgery after the SARS-CoV-2 Pandemic-A Scoping Review

BACKGROUND

SARS-CoV-2 has significantly transformed the healthcare environment, and it has triggered the development of electronic health and artificial intelligence mechanisms, for instance. In this overview, we concentrated on enhancing the two concepts in surgery after the pandemic, and we examined the factors on a global scale.

OBJECTIVE

The primary goal of this scoping review is to elaborate on how surgeons have used eHealth and AI before; during; and after the current global pandemic. More specifically, this review focuses on the empowerment of the concepts of electronic health and artificial intelligence after the pandemic; which mainly depend on the efforts of countries to advance the notions of surgery.

DESIGN

The use of an online search engine was the most applied method. The publication years of all the studies included in the study ranged from 2013 to 2021. Out of the reviewed studies; forty-four qualified for inclusion in the review.

DISCUSSION

We evaluated the prevalence of the concepts in different continents such as the United States; Europe; Asia; the Middle East; and Africa. Our research reveals that the success of eHealth and artificial intelligence adoption primarily depends on the efforts of countries to advance the notions in surgery.

CONCLUSIONS

The study's primary limitation is insufficient information on eHealth and artificial intelligence concepts; particularly in developing nations. Future research should focus on establishing methods of handling eHealth and AI challenges around confidentiality and data security.

Ethical implications of AI in robotic surgical training: A Delphi consensus statement

CONTEXT

As the role of AI in healthcare continues to expand there is increasing awareness of the potential pitfalls of AI and the need for guidance to avoid them.

OBJECTIVES

To provide ethical guidance on developing narrow AI applications for surgical training curricula. We define standardised approaches to developing AI driven applications in surgical training that address current recognised ethical implications of utilising AI on surgical data. We aim to describe an ethical approach based on the current evidence, understanding of AI and available technologies, by seeking consensus from an expert committee.

EVIDENCE ACQUISITION

The project was carried out in 3 phases: (1) A steering group was formed to review the literature and summarize current evidence. (2) A larger expert panel convened and discussed the ethical implications of AI application based on the current evidence. A survey was created, with input from panel members. (3) Thirdly, panel-based consensus findings were determined using an online Delphi process to formulate guidance. 30 experts in AI implementation and/or training including clinicians, academics and industry contributed. The Delphi process underwent 3 rounds. Additions to the second and third-round surveys were formulated based on the answers and comments from previous rounds. Consensus opinion was defined as ≥ 80% agreement.

EVIDENCE SYNTHESIS

There was 100% response from all 3 rounds. The resulting formulated guidance showed good internal consistency, with a Cronbach alpha of >0.8. There was 100% consensus that there is currently a lack of guidance on the utilisation of AI in the setting of robotic surgical training. Consensus was reached in multiple areas, including: 1. Data protection and privacy; 2. Reproducibility and transparency; 3. Predictive analytics; 4. Inherent biases; 5. Areas of training most likely to benefit from AI.

CONCLUSIONS

Using the Delphi methodology, we achieved international consensus among experts to develop and reach content validation for guidance on ethical implications of AI in surgical training. Providing an ethical foundation for launching narrow AI applications in surgical training. This guidance will require further validation.

PATIENT SUMMARY

As the role of AI in healthcare continues to expand there is increasing awareness of the potential pitfalls of AI and the need for guidance to avoid them.In this paper we provide guidance on ethical implications of AI in surgical training.

Robotic assistance for quick and accurate image-guided needle placement

Computed tomography (CT) image-guided procedures including biopsy, drug delivery, and ablation are gaining increasing application in medicine. Robotic technology holds the promise for allowing surgeons, and other proceduralists, access to such CT-guided procedures by potentially shortening training, improving accuracy, decreasing needle passes, and reducing radiation exposure. We evaluated surgeon learning and proficiency for image-guided needle placement with an FDA-cleared robotic arm. Five out of six surgeons had no prior CT-guided procedural experience, while one had prior experience with freehand CT-guided needle placement. All surgeons underwent a 60-min training with the MAXIO robot (Perfint Healthcare, Redmond, WA). The robot was used to place needles into three different pre-specified targets on a spine model. Performance time, procedural errors, and needle placement accuracy were recorded. All participants successfully placed needles into the targets using the robotic arm. The average time for needle placement was 3:44 ± 1:43 min. Time for needle placement decreased with subsequent attempts, with average third placement taking 2:29 ± 1:51 min less than the first attempt. The average vector distance from the target was 2.3 ± 1.2 mm. One error resulted in the need for reimaging by CT scan. No errant needle placement occurred. Surgeons (attending fellows and residents) without previous experience and minimal training could successfully place percutaneous needles under CT guidance quickly, accurately, and reproducibly using a robotic arm. This suggests that robotic technology may be used to facilitate surgeon adoption of CT image-guided needle-based procedures in the future.