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

Smart Operating Room in Digestive Surgery: A Narrative Review

The introduction of new technologies in current digestive surgical practice is progressively reshaping the operating room, defining the fourth surgical revolution. The implementation of black boxes and control towers aims at streamlining workflow and reducing surgical error by early identification and analysis, while augmented reality and artificial intelligence augment surgeons' perceptual and technical skills by superimposing three-dimensional models to real-time surgical images. Moreover, the operating room architecture is transitioning toward an integrated digital environment to improve efficiency and, ultimately, patients' outcomes. This narrative review describes the most recent evidence regarding the role of these technologies in transforming the current digestive surgical practice, underlining their potential benefits and drawbacks in terms of efficiency and patients' outcomes, as an attempt to foresee the digestive surgical practice of tomorrow.

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.

Potential of Large Language Models in Health Care: Delphi Study

BACKGROUND

A large language model (LLM) is a machine learning model inferred from text data that captures subtle patterns of language use in context. Modern LLMs are based on neural network architectures that incorporate transformer methods. They allow the model to relate words together through attention to multiple words in a text sequence. LLMs have been shown to be highly effective for a range of tasks in natural language processing (NLP), including classification and information extraction tasks and generative applications.

OBJECTIVE

The aim of this adapted Delphi study was to collect researchers' opinions on how LLMs might influence health care and on the strengths, weaknesses, opportunities, and threats of LLM use in health care.

METHODS

We invited researchers in the fields of health informatics, nursing informatics, and medical NLP to share their opinions on LLM use in health care. We started the first round with open questions based on our strengths, weaknesses, opportunities, and threats framework. In the second and third round, the participants scored these items.

RESULTS

The first, second, and third rounds had 28, 23, and 21 participants, respectively. Almost all participants (26/28, 93% in round 1 and 20/21, 95% in round 3) were affiliated with academic institutions. Agreement was reached on 103 items related to use cases, benefits, risks, reliability, adoption aspects, and the future of LLMs in health care. Participants offered several use cases, including supporting clinical tasks, documentation tasks, and medical research and education, and agreed that LLM-based systems will act as health assistants for patient education. The agreed-upon benefits included increased efficiency in data handling and extraction, improved automation of processes, improved quality of health care services and overall health outcomes, provision of personalized care, accelerated diagnosis and treatment processes, and improved interaction between patients and health care professionals. In total, 5 risks to health care in general were identified: cybersecurity breaches, the potential for patient misinformation, ethical concerns, the likelihood of biased decision-making, and the risk associated with inaccurate communication. Overconfidence in LLM-based systems was recognized as a risk to the medical profession. The 6 agreed-upon privacy risks included the use of unregulated cloud services that compromise data security, exposure of sensitive patient data, breaches of confidentiality, fraudulent use of information, vulnerabilities in data storage and communication, and inappropriate access or use of patient data.

CONCLUSIONS

Future research related to LLMs should not only focus on testing their possibilities for NLP-related tasks but also consider the workflows the models could contribute to and the requirements regarding quality, integration, and regulations needed for successful implementation in practice.

What More Can We Ask of Robotics?

The future of robotics relies heavily on the ongoing synergy between robotic surgery and artificial intelligence. To unlock their full potential, we should address issues such as accessibility, education, data privacy, and ethics.

Clinical applications of artificial intelligence in robotic surgery

Artificial intelligence (AI) is revolutionizing nearly every aspect of modern life. In the medical field, robotic surgery is the sector with some of the most innovative and impactful advancements. In this narrative review, we outline recent contributions of AI to the field of robotic surgery with a particular focus on intraoperative enhancement. AI modeling is allowing surgeons to have advanced intraoperative metrics such as force and tactile measurements, enhanced detection of positive surgical margins, and even allowing for the complete automation of certain steps in surgical procedures. AI is also Query revolutionizing the field of surgical education. AI modeling applied to intraoperative surgical video feeds and instrument kinematics data is allowing for the generation of automated skills assessments. AI also shows promise for the generation and delivery of highly specialized intraoperative surgical feedback for training surgeons. Although the adoption and integration of AI show promise in robotic surgery, it raises important, complex ethical questions. Frameworks for thinking through ethical dilemmas raised by AI are outlined in this review. AI enhancements in robotic surgery is some of the most groundbreaking research happening today, and the studies outlined in this review represent some of the most exciting innovations in recent years.

Technical and ethical considerations in telesurgery

Telesurgery, a cutting-edge field at the intersection of medicine and technology, holds immense promise for enhancing surgical capabilities, extending medical care, and improving patient outcomes. In this scenario, this article explores the landscape of technical and ethical considerations that highlight the advancement and adoption of telesurgery. Network considerations are crucial for ensuring seamless and low-latency communication between remote surgeons and robotic systems, while technical challenges encompass system reliability, latency reduction, and the integration of emerging technologies like artificial intelligence and 5G networks. Therefore, this article also explores the critical role of network infrastructure, highlighting the necessity for low-latency, high-bandwidth, secure and private connections to ensure patient safety and surgical precision. Moreover, ethical considerations in telesurgery include patient consent, data security, and the potential for remote surgical interventions to distance surgeons from their patients. Legal and regulatory frameworks require refinement to accommodate the unique aspects of telesurgery, including liability, licensure, and reimbursement. Our article presents a comprehensive analysis of the current state of telesurgery technology and its potential while critically examining the challenges that must be navigated for its widespread adoption.

Evaluation of objective tools and artificial intelligence in robotic surgery technical skills assessment: a systematic review

BACKGROUND

There is a need to standardize training in robotic surgery, including objective assessment for accreditation. This systematic review aimed to identify objective tools for technical skills assessment, providing evaluation statuses to guide research and inform implementation into training curricula.

METHODS

A systematic literature search was conducted in accordance with the PRISMA guidelines. Ovid Embase/Medline, PubMed and Web of Science were searched. Inclusion criterion: robotic surgery technical skills tools. Exclusion criteria: non-technical, laparoscopy or open skills only. Manual tools and automated performance metrics (APMs) were analysed using Messick's concept of validity and the Oxford Centre of Evidence-Based Medicine (OCEBM) Levels of Evidence and Recommendation (LoR). A bespoke tool analysed artificial intelligence (AI) studies. The Modified Downs-Black checklist was used to assess risk of bias.

RESULTS

Two hundred and forty-seven studies were analysed, identifying: 8 global rating scales, 26 procedure-/task-specific tools, 3 main error-based methods, 10 simulators, 28 studies analysing APMs and 53 AI studies. Global Evaluative Assessment of Robotic Skills and the da Vinci Skills Simulator were the most evaluated tools at LoR 1 (OCEBM). Three procedure-specific tools, 3 error-based methods and 1 non-simulator APMs reached LoR 2. AI models estimated outcomes (skill or clinical), demonstrating superior accuracy rates in the laboratory with 60 per cent of methods reporting accuracies over 90 per cent, compared to real surgery ranging from 67 to 100 per cent.

CONCLUSIONS

Manual and automated assessment tools for robotic surgery are not well validated and require further evaluation before use in accreditation processes.PROSPERO: registration ID CRD42022304901.

Benchtop simulation of the retrosigmoid approach: Validation of a surgical simulator and development of a task-specific outcome measure score

Background

Neurosurgical training is changing globally. Reduced working hours and training opportunities, increased patient safety expectations, and the impact of COVID-19 have reduced operative exposure. Benchtop simulators enable trainees to develop surgical skills in a controlled environment. We aim to validate a high-fidelity simulator model (RetrosigmoidBox, UpSurgeOn) for the retrosigmoid approach to the cerebellopontine angle (CPA).

Methods

Novice and expert Neurosurgeons and Ear, Nose, and Throat surgeons performed a surgical task using the model - identification of the trigeminal nerve. Experts completed a post-task questionnaire examining face and content validity. Construct validity was assessed through scoring of operative videos employing Objective Structured Assessment of Technical Skills (OSATS) and a novel Task-Specific Outcome Measure score.

Results

Fifteen novice and five expert participants were recruited. Forty percent of experts agreed or strongly agreed that the brain tissue looked real. Experts unanimously agreed that the RetrosigmoidBox was appropriate for teaching. Statistically significant differences were noted in task performance between novices and experts, demonstrating construct validity. Median total OSATS score was 14/25 (IQR 10-19) for novices and 22/25 (IQR 20-22) for experts (p < 0.05). Median Task-Specific Outcome Measure score was 10/20 (IQR 7-17) for novices compared to 19/20 (IQR 18.5-19.5) for experts (p < 0.05).

Conclusion

The RetrosigmoidBox benchtop simulator has a high degree of content and construct validity and moderate face validity. The changing landscape of neurosurgical training mean that simulators are likely to become increasingly important in the delivery of high-quality education. We demonstrate the validity of a Task-Specific Outcome Measure score for performance assessment of a simulated approach to the CPA.

An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture

This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.

Artificial Intelligence in Surgical Training for Kidney Cancer: A Systematic Review of the Literature

The prevalence of renal cell carcinoma (RCC) is increasing due to advanced imaging techniques. Surgical resection is the standard treatment, involving complex radical and partial nephrectomy procedures that demand extensive training and planning. Furthermore, artificial intelligence (AI) can potentially aid the training process in the field of kidney cancer. This review explores how artificial intelligence (AI) can create a framework for kidney cancer surgery to address training difficulties. Following PRISMA 2020 criteria, an exhaustive search of PubMed and SCOPUS databases was conducted without any filters or restrictions. Inclusion criteria encompassed original English articles focusing on AI's role in kidney cancer surgical training. On the other hand, all non-original articles and articles published in any language other than English were excluded. Two independent reviewers assessed the articles, with a third party settling any disagreement. Study specifics, AI tools, methodologies, endpoints, and outcomes were extracted by the same authors. The Oxford Center for Evidence-Based Medicine's evidence levels were employed to assess the studies. Out of 468 identified records, 14 eligible studies were selected. Potential AI applications in kidney cancer surgical training include analyzing surgical workflow, annotating instruments, identifying tissues, and 3D reconstruction. AI is capable of appraising surgical skills, including the identification of procedural steps and instrument tracking. While AI and augmented reality (AR) enhance training, challenges persist in real-time tracking and registration. The utilization of AI-driven 3D reconstruction proves beneficial for intraoperative guidance and preoperative preparation. Artificial intelligence (AI) shows potential for advancing surgical training by providing unbiased evaluations, personalized feedback, and enhanced learning processes. Yet challenges such as consistent metric measurement, ethical concerns, and data privacy must be addressed. The integration of AI into kidney cancer surgical training offers solutions to training difficulties and a boost to surgical education. However, to fully harness its potential, additional studies are imperative.

Considerations for the application of artificial intelligence in vascular surgical education

The rapid adoption of artificial intelligence (AI) into everyday use has presented multiple issues for surgical educators to consider. In this article, the authors discuss some of the ethical aspects of academic integrity and the use of AI. These issues include the importance of understanding the current limits of AI and the inherent biases of the technology. The authors further discuss the ethical considerations of the use of AI in surgical training and in clinical use, with an emphasis on vascular surgery.

Utilising an accelerated Delphi process to develop consensus on the requirement and components of a pre-procedural core robotic surgery curriculum

Robot-assisted surgery (RAS) continues to grow globally. Despite this, in the UK and Ireland, it is estimated that over 70% of surgical trainees across all specialities have no access to robot-assisted surgical training (RAST). This study aimed to provide educational stakeholders guidance on a pre-procedural core robotic surgery curriculum (PPCRC) from the perspective of the end user; the surgical trainee. The study was conducted in four Phases: P1: a steering group was formed to review current literature and summarise the evidence, P2: Pan-Specialty Trainee Panel Virtual Classroom Discussion, P3: Accelerated Delphi Process and P4: Formulation of Recommendations. Forty-three surgeons in training representing all surgical specialties and training levels contributed to the three round Delphi process. 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. There was 100% response from all three rounds. The resulting formulated guidance showed good internal consistency, with a Cronbach alpha of > 0.8. There was 97.7% agreement that a standardised PPCRC would be advantageous to training and that, independent of speciality, there should be a common approach (95.5% agreement). Consensus was reached in multiple areas: 1. Experience and Exposure, 2. Access and context, 3. Curriculum Components, 4 Target Groups and Delivery, 5. Objective Metrics, Benchmarking and Assessment. Using the Delphi methodology, we achieved multispecialty consensus among trainees to develop and reach content validation for the requirements and components of a PPCRC. This guidance will benefit from further validation following implementation.

Robot-assisted pelvic floor reconstructive surgery: an international Delphi study of expert users

BACKGROUND

Robotic surgery has gained popularity for the reconstruction of pelvic floor defects. Nonetheless, there is no evidence that robot-assisted reconstructive surgery is either appropriate or superior to standard laparoscopy for the performance of pelvic floor reconstructive procedures or that it is sustainable. The aim of this project was to address the proper role of robotic pelvic floor reconstructive procedures using expert opinion.

METHODS

We set up an international, multidisciplinary group of 26 experts to participate in a Delphi process on robotics as applied to pelvic floor reconstructive surgery. The group comprised urogynecologists, urologists, and colorectal surgeons with long-term experience in the performance of pelvic floor reconstructive procedures and with the use of the robot, who were identified primarily based on peer-reviewed publications. Two rounds of the Delphi process were conducted. The first included 63 statements pertaining to surgeons' characteristics, general questions, indications, surgical technique, and future-oriented questions. A second round including 20 statements was used to reassess those statements where borderline agreement was obtained during the first round. The final step consisted of a face-to-face meeting with all participants to present and discuss the results of the analysis.

RESULTS

The 26 experts agreed that robotics is a suitable indication for pelvic floor reconstructive surgery because of the significant technical advantages that it confers relative to standard laparoscopy. Experts considered these advantages particularly important for the execution of complex reconstructive procedures, although the benefits can be found also during less challenging cases. The experts considered the robot safe and effective for pelvic floor reconstruction and generally thought that the additional costs are offset by the increased surgical efficacy.

CONCLUSION

Robotics is a suitable choice for pelvic reconstruction, but this Delphi initiative calls for more research to objectively assess the specific settings where robotic surgery would provide the most benefit.

Artificial intelligence and automation in endoscopy and surgery

Modern endoscopy relies on digital technology, from high-resolution imaging sensors and displays to electronics connecting configurable illumination and actuation systems for robotic articulation. In addition to enabling more effective diagnostic and therapeutic interventions, the digitization of the procedural toolset enables video data capture of the internal human anatomy at unprecedented levels. Interventional video data encapsulate functional and structural information about a patient's anatomy as well as events, activity and action logs about the surgical process. This detailed but difficult-to-interpret record from endoscopic procedures can be linked to preoperative and postoperative records or patient imaging information. Rapid advances in artificial intelligence, especially in supervised deep learning, can utilize data from endoscopic procedures to develop systems for assisting procedures leading to computer-assisted interventions that can enable better navigation during procedures, automation of image interpretation and robotically assisted tool manipulation. In this Perspective, we summarize state-of-the-art artificial intelligence for computer-assisted interventions in gastroenterology and surgery.

A Delphi consensus statement for digital surgery

The use of digital technology is increasing rapidly across surgical specialities, yet there is no consensus for the term ‘digital surgery’. This is critical as digital health technologies present technical, governance, and legal challenges which are unique to the surgeon and surgical patient. We aim to define the term digital surgery and the ethical issues surrounding its clinical application, and to identify barriers and research goals for future practice. 38 international experts, across the fields of surgery, AI, industry, law, ethics and policy, participated in a four-round Delphi exercise. Issues were generated by an expert panel and public panel through a scoping questionnaire around key themes identified from the literature and voted upon in two subsequent questionnaire rounds. Consensus was defined if >70% of the panel deemed the statement important and <30% unimportant. A final online meeting was held to discuss consensus statements. The definition of digital surgery as the use of technology for the enhancement of preoperative planning, surgical performance, therapeutic support, or training, to improve outcomes and reduce harm achieved 100% consensus agreement. We highlight key ethical issues concerning data, privacy, confidentiality and public trust, consent, law, litigation and liability, and commercial partnerships within digital surgery and identify barriers and research goals for future practice. Developers and users of digital surgery must not only have an awareness of the ethical issues surrounding digital applications in healthcare, but also the ethical considerations unique to digital surgery. Future research into these issues must involve all digital surgery stakeholders including patients.

Surgical data science - from concepts toward clinical translation

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.

A Call for Change. Can 3D Printing Replace Cadavers for Surgical Training?

While cadaveric dissection has stood the test of time because of its widely accepted educational value by experienced surgeons, the introduction advances in 3D printing and biomaterial technologies could potentially provide alternative tools for surgical training. This novel concept in simulation (physical reality) would encompass all the benefits of cadavers in terms of realism and clinical relevance without any of its ethical, infection, safety, and financial concerns.

Neurosurgery and artificial intelligence

Neurosurgeons receive extensive and lengthy training to equip themselves with various technical skills, and neurosurgery require a great deal of pre-, intra- and postoperative clinical data collection, decision making, care and recovery. The last decade has seen a significant increase in the importance of artificial intelligence (AI) in neurosurgery. AI can provide a great promise in neurosurgery by complementing neurosurgeons' skills to provide the best possible interventional and noninterventional care for patients by enhancing diagnostic and prognostic outcomes in clinical treatment and help neurosurgeons with decision making during surgical interventions to improve patient outcomes. Furthermore, AI is playing a pivotal role in the production, processing and storage of clinical and experimental data. AI usage in neurosurgery can also reduce the costs associated with surgical care and provide high-quality healthcare to a broader population. Additionally, AI and neurosurgery can build a symbiotic relationship where AI helps to push the boundaries of neurosurgery, and neurosurgery can help AI to develop better and more robust algorithms. This review explores the role of AI in interventional and noninterventional aspects of neurosurgery during pre-, intra- and postoperative care, such as diagnosis, clinical decision making, surgical operation, prognosis, data acquisition, and research within the neurosurgical arena.