Artificial intelligence in surgical education and training: opportunities, challenges, and ethical considerations – correspondence

In Brief Export BACKGROUND: Artificial intelligence (AI) methods and AI-enabled metrics hold tremendous potential to advance surgical education. Our objective was to generate consensus guidance on specific needs for AI methods and AI-enabled metrics for surgical education. STUDY DESIGN: The study included a systematic literature search, a virtual conference, and a 3-round Delphi survey of 40 representative multidisciplinary stakeholders with domain expertise selected through purposeful sampling. The accelerated Delphi process was completed within 10 days. The survey covered overall utility, anticipated future (10-year time horizon), and applications for surgical training, assessment, and feedback. Consensus was agreement among 80% or more respondents. We coded survey questions into 11 themes and descriptively analyzed the responses. RESULTS: The respondents included surgeons (40%), engineers (15%), affiliates of industry (27.5%), professional societies (7.5%), regulatory agencies (7.5%), and a lawyer (2.5%). The survey included 155 questions; consensus was achieved on 136 (87.7%). The panel listed 6 deliverables each for AI-enhanced learning curve analytics and surgical skill assessment. For feedback, the panel identified 10 priority deliverables spanning 2-year (n = 2), 5-year (n = 4), and 10-year (n = 4) timeframes. Within 2 years, the panel expects development of methods to recognize anatomy in images of the surgical field and to provide surgeons with performance feedback immediately after an operation. The panel also identified 5 essential that should be included in operative performance reports for surgeons. CONCLUSIONS: The Delphi panel consensus provides a specific, bold, and forward-looking roadmap for AI methods and AI-enabled metrics for surgical education. Surgical education can benefit immensely from artificial intelligence (AI) methods and AI-enabled metrics. There is limited guidance on how AI should be developed to advance surgical education. This Delphi survey of experts across multiple domains provides a bold, forward-looking roadmap for AI methods and AI-enabled metrics for surgical education. 

Artificial Intelligence Methods and Artificial Intelligence-Enabled Metrics for Surgical Education: A Multidisciplinary Consensus

The rise of high-definition imaging and robotic surgery has independently been associated with improved postoperative outcomes. However, steep learning curves and finite human cognitive ability limit the facility in imaging interpretation and interaction with the robotic surgery console interfaces. This review presents innovative ways in which artificial intelligence integrates preoperative imaging and surgery to help overcome these limitations and to further advance robotic operations.PubMed was queried for "artificial intelligence," "machine learning," and "robotic surgery." From the 182 publications in English, a further in-depth review of the cited literature was performed.Artificial intelligence boasts efficiency and proclivity for large amounts of unwieldy and unstructured data. Its wide adoption has significant practice-changing implications throughout the perioperative period. Assessment of preoperative imaging can augment preoperative surgeon knowledge by accessing pathology data that have been traditionally only available postoperatively through analysis of preoperative imaging. Intraoperatively, the interaction of artificial intelligence with augmented reality through the dynamic overlay of preoperative anatomical knowledge atop the robotic operative field can outline safe dissection planes, helping surgeons make critical real-time intraoperative decisions. Finally, semi-independent artificial intelligence-assisted robotic operations may one day be performed by artificial intelligence with limited human intervention.As artificial intelligence has allowed machines to think and problem-solve like humans, it promises further advancement of existing technologies and a revolution of individualized patient care. Further research and ethical precautions are necessary before the full implementation of artificial intelligence in robotic surgery. 

The integration of artificial intelligence in robotic surgery: A narrative review.

ChatGPT has gained popularity among Philippine educational institutions due to its versatility and usefulness in academic activities. However, educators have raised concerns about the ethical issues, academic dishonesty, and technology dependence associated with its use. This study aimed to develop and validate an instrument called KAP-CQ39 that assesses the knowledge, attitudes, and practices of preservice teachers regarding the use of ChatGPT. The study involved 4 experts, 12 independent evaluators, and 206 preservice teachers. The instrument underwent a series of assessments to establish quality, validity, and reliability, resulting in the final version of KAP-CQ39. Results showed that KAP-CQ39 has acceptable content validity (CVR and CVI values>0.78), face validity (IS>1.5), construct validity (chi-squared value of 897.564, df=577, p<0.001), and internal consistency (overall Cronbach coefficient=0.91). This proved that KAP-CQ39 can be a valuable tool for assessing the impact of ChatGPT on the Philippine educational system and can provide insights for educational policy makers regarding the use of artificial intelligence in education.

Development and Validation of a Survey Instrument on Knowledge, Attitude, and Practices (KAP) Regarding the Educational Use of ChatGPT among Preservice Teachers in the Philippines

Literature reviews are considered a core research approach in developing new theories and identifying trends and gaps in a given research topic. However, the transparency level of literature reviews might hinder the quality of the obtained findings, thus limiting their implications. As transparency is one of the core elements when implementing Artificial Intelligence (AI), this study assesses the transparency level of literature reviews on AI in education. Specifically, this study used a systematic review to collect and analyze information about reports of methodological decisions and research activities in 61 literature review papers. The obtained findings highlighted that 51.9% of the conducted reviews on AI in education are descriptive. Additionally, the transparency level of the conducted literature reviews was low; 40% of the reviews were in Q1 and 32% in Q2. Particularly, the quality assessment step had the lowest transparency level. The findings of this research can advance the educational technology field by underscoring the methodological gaps when conducting a literature review on AI in education and hence enhance the transparency and trustworthiness of the obtained findings. 

pdf/speaking-of-transparency-are-all-artificial-intelligence-ai-32rocnb2xp.pdf

Speaking of transparency: Are all Artificial Intelligence (AI) literature reviews in education transparent?

Background Technology-enhanced teaching and learning, including Artificial Intelligence (AI) applications, has started to evolve in surgical education. Hence, the purpose of this scoping review is to explore the current and future roles of AI in surgical education. Methods Nine bibliographic databases were searched from January 2010 to January 2021. Full-text articles were included if they focused on AI in surgical education. Results Out of 14,008 unique sources of evidence, 93 were included. Out of 93, 84 were conducted in the simulation setting, and 89 targeted technical skills. Fifty-six studies focused on skills assessment/classification, and 36 used multiple AI techniques. Also, increasing sample size, having balanced data, and using AI to provide feedback were major future directions mentioned by authors. Conclusions AI can help optimize the education of trainees and our results can help educators and researchers identify areas that need further investigation. 

Exploring the roles of artificial intelligence in surgical education: A scoping review

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Exploring the roles of artificial intelligence in surgical education: A scoping review