Knowledge, attitude, and practice of artificial intelligence in emergency and trauma surgery, the ARIES project: an international web-based survey

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.

Knowledge, attitudes and practices of using Indocyanine Green (ICG) fluorescence in emergency surgery: an international web-based survey in the ARtificial Intelligence in Emergency and trauma Surgery (ARIES)-WSES project

Fluorescence imaging is a real-time intraoperative navigation modality to enhance surgical vision and it can guide emergency surgeons while performing difficult, high-risk surgical procedures. The aim of this study is to assess current knowledge, attitudes, and practices of emergency surgeons in the use of indocyanine green (ICG) in emergency settings. Between March 08, 2023 and April 10, 2023, a questionnaire composed of 27 multiple choice and open-ended questions was sent to 200 emergency surgeons who had previously joined the ARtificial Intelligence in Emergency and trauma Surgery (ARIES) project promoted by the WSES. The questionnaire was developed by an emergency surgeon with an interest in advanced technologies and artificial intelligence. The response rate was 96% (192/200). Responders affirmed that ICG fluorescence can support the performance of difficult surgical procedures in the emergency setting, particularly in the presence of severe inflammation and in evaluating bowel viability. Nevertheless, there were concerns regarding accessibility and availability of fluorescence imaging in emergency settings. Eighty-seven out of 192 (45.3%) respondents have a fluorescence imaging system of vision for both elective and emergency surgical procedures; 32.3% of respondents have this system solely for elective procedures; 21.4% of respondents do not have this system, 15% do not have experience with it, and 38% do not use this imaging in emergency surgery. Less than 1% (2/192) affirmed that ICG fluorescence changed always their intraoperative decision-making. Precision surgery effectively tailors surgical interventions to individual patient characteristics using advanced technology, data analysis and artificial intelligence. ICG fluorescence can serve as a valid and safe tool to guide emergency surgery in different scenarios, such as intestinal ischemia and severe acute cholecystitis. Due to the lack of high-level evidence within this field, a consensus of expert emergency surgeons is needed to encourage stakeholders to increase the availability of fluorescence imaging systems and to support emergency surgeons in implementing ICG fluorescence in their daily practice.

Implementation considerations for the adoption of artificial intelligence in the emergency department

OBJECTIVE

Artificial intelligence (AI) has emerged as a potentially transformative force, particularly in the realm of emergency medicine (EM). The implementation of AI in emergency departments (ED) has the potential to improve patient care through various modalities. However, the implementation of AI in the ED presents unique challenges that influence its clinical adoption. This scoping review summarizes the current literature exploring the barriers and facilitators of the clinical implementation of AI in the ED.

METHODS

We systematically searched Embase (Ovid), MEDLINE (Ovid), Web of Science, and Engineering Village. All articles were published in English through November 20th, 2023. Two reviewers screened the search results, with disagreements resolved through third-party adjudication.

RESULTS

A total of 8172 studies were included in the preliminary search, with 22 selected for the final data extraction. 10 studies were reviews and the remaining 12 were primary quantitative, qualitative, and mixed-methods studies. Out of the 22, 13 studies investigated a specific AI tool or application. Common barriers to implementation included a lack of model interpretability and explainability, encroachment on physician autonomy, and medicolegal considerations. Common facilitators to implementation included educating staff on the model, efficient integration into existing workflows, and sound external validation.

CONCLUSION

There is increasing literature on AI implementation in the ED. Our research suggests that the most common barrier facing AI implementation in the ED is model interpretability and explainability. More primary research investigating the implementation of specific AI tools should be undertaken to help facilitate their successful clinical adoption in the ED.

Torso hemorrhage: noncompressible? never say never

Since limb bleeding has been well managed by extremity tourniquets, the management of exsanguinating torso hemorrhage (TH) has become a hot issue both in military and civilian medicine. Conventional hemostatic techniques are ineffective for managing traumatic bleeding of organs and vessels within the torso due to the anatomical features. The designation of noncompressible torso hemorrhage (NCTH) marks a significant step in investigating the injury mechanisms and developing effective methods for bleeding control. Special tourniquets such as abdominal aortic and junctional tourniquet and SAM junctional tourniquet designed for NCTH have been approved by FDA for clinical use. Combat ready clamp and junctional emergency treatment tool also exhibit potential for external NCTH control. In addition, resuscitative endovascular balloon occlusion of the aorta (REBOA) further provides an endovascular solution to alleviate the challenges of NCTH treatment. Notably, NCTH cognitive surveys have revealed that medical staff have deficiencies in understanding relevant concepts and treatment abilities. The stereotypical interpretation of NCTH naming, particularly the term noncompressible, is the root cause of this issue. This review discusses the dynamic relationship between TH and NCTH by tracing the development of external NCTH control techniques. The authors propose to further subdivide the existing NCTH into compressible torso hemorrhage and NCTH' (noncompressible but REBOA controllable) based on whether hemostasis is available via external compression. Finally, due to the irreplaceability of special tourniquets during the prehospital stage, the authors emphasize the importance of a package program to improve the efficacy and safety of external NCTH control. This program includes the promotion of tourniquet redesign and hemostatic strategies, personnel reeducation, and complications prevention.

Cesena guidelines: WSES consensus statement on laparoscopic-first approach to general surgery emergencies and abdominal trauma

BACKGROUND

Laparoscopy is widely adopted across nearly all surgical subspecialties in the elective setting. Initially finding indication in minor abdominal emergencies, it has gradually become the standard approach in the majority of elective general surgery procedures. Despite many technological advances and increasing acceptance, the laparoscopic approach remains underutilized in emergency general surgery and in abdominal trauma. Emergency laparotomy continues to carry a high morbidity and mortality. In recent years, there has been a growing interest from emergency and trauma surgeons in adopting minimally invasive surgery approaches in the acute surgical setting. The present position paper, supported by the World Society of Emergency Surgery (WSES), aims to provide a review of the literature to reach a consensus on the indications and benefits of a laparoscopic-first approach in patients requiring emergency abdominal surgery for general surgery emergencies or abdominal trauma.

METHODS

This position paper was developed according to the WSES methodology. A steering committee performed the literature review and drafted the position paper. An international panel of 54 experts then critically revised the manuscript and discussed it in detail, to develop a consensus on a position statement.

RESULTS

A total of 323 studies (systematic review and meta-analysis, randomized clinical trial, retrospective comparative cohort studies, case series) have been selected from an initial pool of 7409 studies. Evidence demonstrates several benefits of the laparoscopic approach in stable patients undergoing emergency abdominal surgery for general surgical emergencies or abdominal trauma. The selection of a stable patient seems to be of paramount importance for a safe adoption of a laparoscopic approach. In hemodynamically stable patients, the laparoscopic approach was found to be safe, feasible and effective as a therapeutic tool or helpful to identify further management steps and needs, resulting in improved outcomes, regardless of conversion. Appropriate patient selection, surgeon experience and rigorous minimally invasive surgical training, remain crucial factors to increase the adoption of laparoscopy in emergency general surgery and abdominal trauma.

CONCLUSIONS

The WSES expert panel suggests laparoscopy as the first approach for stable patients undergoing emergency abdominal surgery for general surgery emergencies and abdominal trauma.

Application of artificial intelligence in trauma orthopedics: Limitation and prospects

The varieties and capabilities of artificial intelligence and machine learning in orthopedic surgery are extensively expanding. One promising method is neural networks, emphasizing big data and computer-based learning systems to develop a statistical fracture-detecting model. It derives patterns and rules from outstanding amounts of data to analyze the probabilities of different outcomes using new sets of similar data. The sensitivity and specificity of machine learning in detecting fractures vary from previous studies. AI may be most promising in the diagnosis of less-obvious fractures that are more commonly missed. Future studies are necessary to develop more accurate and effective detection models that can be used clinically.

Artificial intelligence in orthopedics: A qualitative exploration of the surgeon perspective

Artificial intelligence (AI) is currently integrated into many medical services. AI is utilized in many aspects of orthopedic surgery. The scope ranges from diagnosis to complex surgery. To evaluate the perceptions, attitudes, and interests of Sudanese orthopedic surgeons regarding the different applications of AI in orthopedic surgery. This qualitative questionnaire-based study was conducted through an anonymous electronic survey using Google Forms distributed among Sudanese orthopedic surgeons. The questionnaire entailed 4 sections. The first section included the participants' demographic data. The remaining 3 sections included questions for the assessment of the perception, attitude, and interest of surgeons toward (AI). The validity and reliability of the questionnaire were tested and piloted before the final dissemination. One hundred twenty-nine surgeons responded to the questionnaires. Most respondents needed to be more aware of the basic concepts of AI. However, most respondents were aware of its use in spinal and joint replacement surgeries. Most respondents had doubts regarding the safety of (AI). However, they were highly interested in utilizing (AI) in many orthopedic surgical aspects. Orthopedic surgery is a rapidly evolving branch of surgery that involves adoption of new technologies. Therefore, orthopedic surgeons should be encouraged to enroll in research activities to generate more studies and reviews to assess the usefulness and safety of emerging technologies.

Surgeons' perspectives on artificial intelligence to support clinical decision-making in trauma and emergency contexts: results from an international survey

BACKGROUND

Artificial intelligence (AI) is gaining traction in medicine and surgery. AI-based applications can offer tools to examine high-volume data to inform predictive analytics that supports complex decision-making processes. Time-sensitive trauma and emergency contexts are often challenging. The study aims to investigate trauma and emergency surgeons' knowledge and perception of using AI-based tools in clinical decision-making processes.

METHODS

An online survey grounded on literature regarding AI-enabled surgical decision-making aids was created by a multidisciplinary committee and endorsed by the World Society of Emergency Surgery (WSES). The survey was advertised to 917 WSES members through the society's website and Twitter profile.

RESULTS

650 surgeons from 71 countries in five continents participated in the survey. Results depict the presence of technology enthusiasts and skeptics and surgeons' preference toward more classical decision-making aids like clinical guidelines, traditional training, and the support of their multidisciplinary colleagues. A lack of knowledge about several AI-related aspects emerges and is associated with mistrust.

DISCUSSION

The trauma and emergency surgical community is divided into those who firmly believe in the potential of AI and those who do not understand or trust AI-enabled surgical decision-making aids. Academic societies and surgical training programs should promote a foundational, working knowledge of clinical AI.

Artificial intelligence for oral and dental healthcare: Core education curriculum

OBJECTIVES

Artificial intelligence (AI) is swiftly entering oral health services and dentistry, while most providers show limited knowledge and skills to appraise dental AI applications. We aimed to define a core curriculum for both undergraduate and postgraduate education, establishing a minimum set of outcomes learners should acquire when taught about oral and dental AI.

METHODS

Existing curricula and other documents focusing on literacy of medical professionals around AI were screened and relevant items extracted. Items were scoped and adapted using expert interviews with members of the IADR's e-oral health group, the ITU/WHO's Focus Group AI for Health and the Association for Dental Education in Europe. Learning outcome levels were defined and each item assigned to a level. Items were systematized into domains and a curricular structure defined. The resulting curriculum was consented using an online Delphi process.

RESULTS

Four domains of learning outcomes emerged, with most outcomes being on the "knowledge" level: (1) Basic definitions and terms, the reasoning behind AI and the principle of machine learning, the idea of training, validating and testing models, the definition of reference tests, the contrast between dynamic and static AI, and the problem of AI being a black box and requiring explainability should be known. (2) Use cases, the required types of AI to address them, and the typical setup of AI software for dental purposes should be taught. (3) Evaluation metrics, their interpretation, the relevant impact of AI on patient or societal health outcomes and associated examples should be considered. (4) Issues around generalizability and representativeness, explainability, autonomy and accountability and the need for governance should be highlighted.

CONCLUSION

Both educators and learners should consider this core curriculum during planning, conducting and evaluating oral and dental AI education.

CLINICAL SIGNIFICANCE

A core curriculum on oral and dental AI may help to increase oral and dental healthcare providers' literacy around AI, allowing them to critically appraise AI applications and to use them consciously and on an informed basis.

Artificial intelligence in surgery: the emergency surgeon's perspective (the ARIES project)

Artificial Intelligence (AI) has been developed and implemented in healthcare with the valuable potential to reduce health, social, and economic inequities, help actualize universal health coverage, and improve health outcomes on a global scale. The application of AI in emergency surgery settings could improve clinical practice and operating rooms management by promoting consistent, high-quality decision making while preserving the importance of bedside assessment and human intuition as well as respect for human rights and equitable surgical care, but ethical and legal issues are slowing down surgeons' enthusiasm. Emergency surgeons are aware that prioritizing education, increasing the availability of high AI technologies for emergency and trauma surgery, and funding to support research projects that use AI to provide decision support in the operating room are crucial to create an emergency "intelligent" surgery.

Facilitators and Barriers of Artificial Intelligence Applications in Rehabilitation: A Mixed-Method Approach

Artificial intelligence (AI) has been used in physical therapy diagnosis and management for various impairments. Physical therapists (PTs) need to be able to utilize the latest innovative treatment techniques to improve the quality of care. The study aimed to describe PTs' views on AI and investigate multiple factors as indicators of AI knowledge, attitude, and adoption among PTs. Moreover, the study aimed to identify the barriers to using AI in rehabilitation. Two hundred and thirty-six PTs participated voluntarily in the study. A concurrent mixed-method design was used to document PTs' opinions regarding AI deployment in rehabilitation. A self-administered survey consisting of several aspects, including demographic, knowledge, uses, advantages, impacts, and barriers limiting AI utilization in rehabilitation, was used. A total of 63.3% of PTs reported that they had not experienced any kind of AI applications at work. The major factors predicting a higher level of AI knowledge among PTs were being a non-academic worker (OR = 1.77 [95% CI; 1.01 to 3.12], p = 0.04), being a senior PT (OR = 2.44, [95%CI: 1.40 to 4.22], p = 0.002), and having a Master/Doctorate degree (OR = 1.97, [95%CI: 1.11 to 3.50], p = 0.02). However, the cost and resources of AI were the major reported barriers to adopting AI-based technologies. The study highlighted a remarkable dearth of AI knowledge among PTs. AI and advanced knowledge in technology need to be urgently transferred to PTs.