Investigating the Ethical and Data Governance Issues of Artificial Intelligence in Surgery: Protocol for a Delphi Study

Lam, Kyle; Iqbal, Fahad; Purkayastha, Sanjay

doi:10.2196/26552

Cited by 24 publications

(13 citation statements)

References 16 publications

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“…However, to date, most of the research literature on AI in health care deals with the development, application, and evaluation of advanced analytic techniques and models [ 10 - 12 ], primarily within computer science, engineering, and medical informatics. The literature on the implementation of AI to improve existing clinical workflows is more fragmented and mostly based on nonempirical data from proof-of-concept studies [ 1 , 13 ] across multiple subject areas, such as data governance [ 14 ], ethics [ 15 ], accountability [ 3 ], interpretability [ 16 ], and regulation [ 17 ]. This means that there are uncertainties around factors that influence the implementation of AI in real-world health care setups [ 10 ] and that health care professionals lack guidance on how to implement AI in their daily practices [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Implementation Frameworks for Artificial Intelligence Translation Into Health Care Practice: Scoping Review

Gama¹,

Tyskbo²,

Nygren³

et al. 2022

J Med Internet Res

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Background Significant efforts have been made to develop artificial intelligence (AI) solutions for health care improvement. Despite the enthusiasm, health care professionals still struggle to implement AI in their daily practice. Objective This paper aims to identify the implementation frameworks used to understand the application of AI in health care practice. Methods A scoping review was conducted using the Cochrane, Evidence Based Medicine Reviews, Embase, MEDLINE, and PsycINFO databases to identify publications that reported frameworks, models, and theories concerning AI implementation in health care. This review focused on studies published in English and investigating AI implementation in health care since 2000. A total of 2541 unique publications were retrieved from the databases and screened on titles and abstracts by 2 independent reviewers. Selected articles were thematically analyzed against the Nilsen taxonomy of implementation frameworks, and the Greenhalgh framework for the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) of health care technologies. Results In total, 7 articles met all eligibility criteria for inclusion in the review, and 2 articles included formal frameworks that directly addressed AI implementation, whereas the other articles provided limited descriptions of elements influencing implementation. Collectively, the 7 articles identified elements that aligned with all the NASSS domains, but no single article comprehensively considered the factors known to influence technology implementation. New domains were identified, including dependency on data input and existing processes, shared decision-making, the role of human oversight, and ethics of population impact and inequality, suggesting that existing frameworks do not fully consider the unique needs of AI implementation. Conclusions This literature review demonstrates that understanding how to implement AI in health care practice is still in its early stages of development. Our findings suggest that further research is needed to provide the knowledge necessary to develop implementation frameworks to guide the future implementation of AI in clinical practice and highlight the opportunity to draw on existing knowledge from the field of implementation science.

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Section: Introductionmentioning

confidence: 99%

Implementation Frameworks for Artificial Intelligence Translation Into Health Care Practice: Scoping Review

Gama¹,

Tyskbo²,

Nygren³

et al. 2022

J Med Internet Res

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show abstract

“…Ultimately, for ML applications in surgery to flourish, a paradigm shift in the operating room towards large-scale collection of surgical data is needed in order to facilitate these applications. However, implementing these systems are not without issue and the surgical data science community continues to grapple with both the technical and ethical hurdles to its adoption 13 , 31 .…”

Section: Discussionmentioning

confidence: 99%

“…This will solve not only issues associated with the use of ML in surgical performance assessment but also issues across the whole field of surgical data science and the wider application of ML to surgery. Encouragingly, efforts have been made by the surgical data science community in order to identify the challenges and research targets associated with widespread data acquisition in the operating room and data sharing 13 , 31 . It is only through this that datasets can be acquired and utilized at scale.…”

Section: Discussionmentioning

confidence: 99%

Machine learning for technical skill assessment in surgery: a systematic review

et al. 2022

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Accurate and objective performance assessment is essential for both trainees and certified surgeons. However, existing methods can be time consuming, labor intensive, and subject to bias. Machine learning (ML) has the potential to provide rapid, automated, and reproducible feedback without the need for expert reviewers. We aimed to systematically review the literature and determine the ML techniques used for technical surgical skill assessment and identify challenges and barriers in the field. A systematic literature search, in accordance with the PRISMA statement, was performed to identify studies detailing the use of ML for technical skill assessment in surgery. Of the 1896 studies that were retrieved, 66 studies were included. The most common ML methods used were Hidden Markov Models (HMM, 14/66), Support Vector Machines (SVM, 17/66), and Artificial Neural Networks (ANN, 17/66). 40/66 studies used kinematic data, 19/66 used video or image data, and 7/66 used both. Studies assessed the performance of benchtop tasks (48/66), simulator tasks (10/66), and real-life surgery (8/66). Accuracy rates of over 80% were achieved, although tasks and participants varied between studies. Barriers to progress in the field included a focus on basic tasks, lack of standardization between studies, and lack of datasets. ML has the potential to produce accurate and objective surgical skill assessment through the use of methods including HMM, SVM, and ANN. Future ML-based assessment tools should move beyond the assessment of basic tasks and towards real-life surgery and provide interpretable feedback with clinical value for the surgeon.PROSPERO: CRD42020226071

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“…This, in turn, would allow them to modulate such performance as a means to improve patient outcomes. Third, an element of explainability implies improved framework transparency and is viewed, by some, as contributing to the ethical deployment of AI models in a clinical setting 37 .…”

Section: Discussionmentioning

confidence: 99%

Quantification of Robotic Surgeries with Vision-Based Deep Learning

Kiyasseh¹,

Ma²,

Haque³

et al. 2022

Preprint

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Surgery is a high-stakes domain where surgeons must navigate critical anatomical structures and actively avoid potential complications while achieving the main task at hand. Such surgical activity has been shown to affect long-term patient outcomes. To better understand this relationship, whose mechanics remain unknown for the majority of surgical procedures, we hypothesize that the core elements of surgery must first be quantified in a reliable, objective, and scalable manner. We believe this is a prerequisite for the provision of surgical feedback and modulation of surgeon performance in pursuit of improved patient outcomes. To holistically quantify surgeries, we propose a unified deep learning framework, entitled Roboformer, which operates exclusively on videos recorded during surgery to independently achieve multiple tasks: surgical phase recognition (the what of surgery), gesture classification and skills assessment (the how of surgery). We validated our framework on four video-based datasets of two commonly-encountered types of steps (dissection and suturing) within minimally-invasive robotic surgeries. We demonstrated that our framework can generalize well to unseen videos, surgeons, medical centres, and surgical procedures. We also found that our framework, which naturally lends itself to explainable findings, identified relevant information when achieving a particular task. These findings are likely to instill surgeons with more confidence in our framework's behaviour, increasing the likelihood of clinical adoption, and thus paving the way for more targeted surgical feedback.

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Investigating the Ethical and Data Governance Issues of Artificial Intelligence in Surgery: Protocol for a Delphi Study

Cited by 24 publications

References 16 publications

Implementation Frameworks for Artificial Intelligence Translation Into Health Care Practice: Scoping Review

Implementation Frameworks for Artificial Intelligence Translation Into Health Care Practice: Scoping Review

Machine learning for technical skill assessment in surgery: a systematic review

Quantification of Robotic Surgeries with Vision-Based Deep Learning

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