Does Intraoperative Artificial Intelligence Decision Support Pose Ethical Issues?

Binkley, Charles E.; Green, Brian P.

doi:10.1001/jamasurg.2021.2055

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…Use of real-time data may present additional challenges such as delays in preprocessing data or incomplete data at a given time-point. Other considerations include establishing a decision pathway and legal framework ( 14 ). A silent trial also facilitates an assessment of bias to ensure social disparities are not accentuated by the model.…”

Section: Discussionmentioning

confidence: 99%

The silent trial - the bridge between bench-to-bedside clinical AI applications

Kwong

Erdman

Khondker

et al. 2022

Front. Digit. Health

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As more artificial intelligence (AI) applications are integrated into healthcare, there is an urgent need for standardization and quality-control measures to ensure a safe and successful transition of these novel tools into clinical practice. We describe the role of the silent trial, which evaluates an AI model on prospective patients in real-time, while the end-users (i.e., clinicians) are blinded to predictions such that they do not influence clinical decision-making. We present our experience in evaluating a previously developed AI model to predict obstructive hydronephrosis in infants using the silent trial. Although the initial model performed poorly on the silent trial dataset (AUC 0.90 to 0.50), the model was refined by exploring issues related to dataset drift, bias, feasibility, and stakeholder attitudes. Specifically, we found a shift in distribution of age, laterality of obstructed kidneys, and change in imaging format. After correction of these issues, model performance improved and remained robust across two independent silent trial datasets (AUC 0.85–0.91). Furthermore, a gap in patient knowledge on how the AI model would be used to augment their care was identified. These concerns helped inform the patient-centered design for the user-interface of the final AI model. Overall, the silent trial serves as an essential bridge between initial model development and clinical trials assessment to evaluate the safety, reliability, and feasibility of the AI model in a minimal risk environment. Future clinical AI applications should make efforts to incorporate this important step prior to embarking on a full-scale clinical trial.

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Section: Discussionmentioning

confidence: 99%

The silent trial - the bridge between bench-to-bedside clinical AI applications

Kwong

Erdman

Khondker

et al. 2022

Front. Digit. Health

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show abstract

“…Ethical considerations underscore the importance of maintaining and fostering trust between healthcare providers, patients, and the AI systems. Transparency in how AI algorithms operate and make decisions is crucial for building and sustaining this trust, ensuring that patients feel confident in the recommendations provided by AIenhanced medical decision support systems (Blease and Bernstein, 2018). Transparency is a key ethical principle when implementing AI in medical decision support.…”

Section: Importance Of Ethical Considerations In Ai-enhanced Medical ...mentioning

confidence: 99%

Ethical considerations in AI-enhanced medical decision support systems: A review

Tolulope Olorunsogo,

Adekunle Oyeyemi Adenyi,

Chioma Anthonia Okolo

et al. 2024

World J. Adv. Eng. Technol. Sci.

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As Artificial Intelligence (AI) continues to play an increasingly pivotal role in medical decision support systems, the ethical implications of its integration into healthcare practices demand comprehensive examination. This review delves into the ethical considerations surrounding AI-enhanced medical decision support systems, aiming to provide insights into the challenges, existing frameworks, exemplary practices, and emerging trends in this rapidly evolving field. The significance of ethical considerations is underscored by the patient-centric focus, emphasizing the impact of AI on patient outcomes and the delicate balance between technological advancements and patient welfare. Trust and transparency emerge as critical pillars, exploring the role of trust in medical decision-making and the imperative of ensuring transparency in AI algorithms to foster confidence among healthcare professionals and patients. Ethical challenges, including privacy and confidentiality concerns, biases in AI algorithms, and issues related to informed consent, are thoroughly examined. Strategies for safeguarding patient data, mitigating biases, and transparently communicating with patients are explored to address these challenges. The role of accountability and responsibility is delineated, defining the ethical responsibilities of both healthcare professionals and AI developers. The review surveys existing ethical frameworks in healthcare AI and evaluates their applicability and effectiveness. Additionally, it highlights recent proposals for ethical guidelines, emphasizing the need to integrate ethical considerations into the entire development life cycle of AI-enhanced medical decision support systems. Case studies and exemplary practices from healthcare institutions implementing ethical AI serve to illustrate real-world applications and offer insights into best practices. The evolving landscape of ethical AI research is explored, showcasing ongoing initiatives and potential innovations that hold promise for addressing ethical challenges in the future. This review underscores the paramount importance of ethical considerations in the integration of AI into medical decision support systems. It provides a comprehensive overview of current challenges, existing frameworks, exemplary practices, and emerging trends, emphasizing the ongoing need for vigilance and ethical governance to ensure the responsible and beneficial deployment of AI in healthcare.

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Improving the Selection of Patients for High-Risk Surgery: The Ethical Use of Artificial Intelligence

Martins,

Binkley

2024

Recent Strategies in High Risk Surgery

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Does Intraoperative Artificial Intelligence Decision Support Pose Ethical Issues?

Cited by 5 publications

References 7 publications

The silent trial - the bridge between bench-to-bedside clinical AI applications

The silent trial - the bridge between bench-to-bedside clinical AI applications

Ethical considerations in AI-enhanced medical decision support systems: A review

Improving the Selection of Patients for High-Risk Surgery: The Ethical Use of Artificial Intelligence

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