Human visual explanations mitigate bias in AI-based assessment of surgeon skills

A Beginner's Guide to Artificial Intelligence for Ophthalmologists

The integration of artificial intelligence (AI) in ophthalmology has promoted the development of the discipline, offering opportunities for enhancing diagnostic accuracy, patient care, and treatment outcomes. This paper aims to provide a foundational understanding of AI applications in ophthalmology, with a focus on interpreting studies related to AI-driven diagnostics. The core of our discussion is to explore various AI methods, including deep learning (DL) frameworks for detecting and quantifying ophthalmic features in imaging data, as well as using transfer learning for effective model training in limited datasets. The paper highlights the importance of high-quality, diverse datasets for training AI models and the need for transparent reporting of methodologies to ensure reproducibility and reliability in AI studies. Furthermore, we address the clinical implications of AI diagnostics, emphasizing the balance between minimizing false negatives to avoid missed diagnoses and reducing false positives to prevent unnecessary interventions. The paper also discusses the ethical considerations and potential biases in AI models, underscoring the importance of continuous monitoring and improvement of AI systems in clinical settings. In conclusion, this paper serves as a primer for ophthalmologists seeking to understand the basics of AI in their field, guiding them through the critical aspects of interpreting AI studies and the practical considerations for integrating AI into clinical practice.

Generative artificial intelligence in surgery

Generative artificial intelligence is able to collect, extract, digest, and generate information in an understandable way for humans. As the first surgical applications of generative artificial intelligence are applied, this perspective paper aims to provide a comprehensive overview of current applications and future perspectives for the application of generative artificial intelligence in surgery, from preoperative planning to training. Generative artificial intelligence can be used before surgery for planning and decision support by extracting patient information and providing patients with information and simulation regarding the procedure. Intraoperatively, generative artificial intelligence can document data that is normally not captured as intraoperative adverse events or provide information to help decision-making. Postoperatively, GAIs can help with patient discharge and follow-up. The ability to provide real-time feedback and store it for later review is an important capability of GAIs. GAI applications are emerging as highly specialized, task-specific tools for tasks such as data extraction, synthesis, presentation, and communication within the realm of surgery. GAIs have the potential to play a pivotal role in facilitating interaction between surgeons and artificial intelligence.

Artificial intelligence in surgery

Artificial intelligence (AI) is rapidly emerging in healthcare, yet applications in surgery remain relatively nascent. Here we review the integration of AI in the field of surgery, centering our discussion on multifaceted improvements in surgical care in the preoperative, intraoperative and postoperative space. The emergence of foundation model architectures, wearable technologies and improving surgical data infrastructures is enabling rapid advances in AI interventions and utility. We discuss how maturing AI methods hold the potential to improve patient outcomes, facilitate surgical education and optimize surgical care. We review the current applications of deep learning approaches and outline a vision for future advances through multimodal foundation models.

A scoping review of artificial intelligence in medical education: BEME Guide No. 84

BACKGROUND

Artificial Intelligence (AI) is rapidly transforming healthcare, and there is a critical need for a nuanced understanding of how AI is reshaping teaching, learning, and educational practice in medical education. This review aimed to map the literature regarding AI applications in medical education, core areas of findings, potential candidates for formal systematic review and gaps for future research.

METHODS

This rapid scoping review, conducted over 16 weeks, employed Arksey and O'Malley's framework and adhered to STORIES and BEME guidelines. A systematic and comprehensive search across PubMed/MEDLINE, EMBASE, and MedEdPublish was conducted without date or language restrictions. Publications included in the review spanned undergraduate, graduate, and continuing medical education, encompassing both original studies and perspective pieces. Data were charted by multiple author pairs and synthesized into various thematic maps and charts, ensuring a broad and detailed representation of the current landscape.

RESULTS

The review synthesized 278 publications, with a majority (68%) from North American and European regions. The studies covered diverse AI applications in medical education, such as AI for admissions, teaching, assessment, and clinical reasoning. The review highlighted AI's varied roles, from augmenting traditional educational methods to introducing innovative practices, and underscores the urgent need for ethical guidelines in AI's application in medical education.

CONCLUSION

The current literature has been charted. The findings underscore the need for ongoing research to explore uncharted areas and address potential risks associated with AI use in medical education. This work serves as a foundational resource for educators, policymakers, and researchers in navigating AI's evolving role in medical education. A framework to support future high utility reporting is proposed, the FACETS framework.

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.

Competency in Robotic Surgery: Standard Setting for Robotic Suturing Using Objective Assessment and Expert Evaluation

OBJECTIVE

Surgical skill assessment tools such as the End-to-End Assessment of Suturing Expertise (EASE) can differentiate a surgeon's experience level. In this simulation-based study, we define a competency benchmark for intraoperative robotic suturing using EASE as a validated measure of performance.

DESIGN

Participants conducted a dry-lab vesicourethral anastomosis (VUA) exercise. Videos were each independently scored by 2 trained, blinded reviewers using EASE. Inter-rater reliability was measured with prevalence-adjusted bias-adjusted Kappa (PABAK) using 2 example videos. All videos were reviewed by an expert surgeon, who determined if the suturing skills exhibited were at a competency level expected for residency graduation (pass or fail). The Contrasting Group (CG) method was then used to set a pass/fail score at the intercept of the pass and fail cohorts' EASE score distributions.

SETTING

Keck School of Medicine, University of Southern California.

PARTICIPANTS

Twenty-six participants: 8 medical students, 8 junior residents (PGY 1-2), 7 senior residents (PGY 3-5) and 3 attending urologists.

RESULTS

After 1 round of consensus-building, average PABAK across EASE subskills was 0.90 (Range 0.67-1.0). The CG method produced a competency benchmark EASE score of >35/39, with a pass rate of 10/26 (38%); 27% were deemed competent by expert evaluation. False positives and negatives were defined as medical students who passed and attendings who failed the assessment, respectively. This pass/fail score produced no false positives or negatives, and fewer JR than SR were considered competent by both the expert and CG benchmark.

CONCLUSIONS

Using an absolute standard setting method, competency scores were set to identify trainees who could competently execute a standardized dry-lab robotic suturing exercise. This standard can be used for high stakes decisions regarding a trainee's technical readiness for independent practice. Future work includes validation of this standard in the clinical environment through correlation with clinical outcomes.

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.

Ironies of artificial intelligence

Bainbridge's Ironies of Automation was a prescient description of automation related challenges for human performance that have characterised much of the 40 years since its publication. Today a new wave of automation based on artificial intelligence (AI) is being introduced across a wide variety of domains and applications. Not only are Bainbridge's original warnings still pertinent for AI, but AI's very nature and focus on cognitive tasks has introduced many new challenges for people who interact with it. Five ironies of AI are presented including difficulties with understanding AI and forming adaptations, opaqueness in AI limitations and biases that can drive human decision biases, and difficulties in understanding the AI reliability, despite the fact that AI remains insufficiently intelligent for many of its intended applications. Future directions are provided to create more human-centered AI applications that can address these challenges.