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J Thorac Dis
J Thorac Dis
JTD
Journal of Thoracic Disease
2072-1439
2077-6624
AME Publishing Company

jtd-16-08-5490
10.21037/jtd-24-990
Letter to the Editor
Safety regulation of machine learning in cardiac surgery
Wang Zhiwen
Wang Linfeng
School of Nursing, Peking University, Beijing, China
Correspondence to: Zhiwen Wang, PhD. School of Nursing, Peking University, The 38 Xueyuan Road, Haidian District, Beijing 100069, China. Email: hezuogongying60@163.com.
28 8 2024
31 8 2024
16 8 54905491
19 6 2024
16 8 2024
2024 Journal of Thoracic Disease. All rights reserved.
2024
Journal of Thoracic Disease.
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0.
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pmcWhen the machine learning techniques applied in the domain of cardiothoracic surgery, safety supervision must be considered. Miles (1) proved the benefits of machine learning technology in its research. But in-depth discussion of the potential risks and challenges has been relatively limited. The current period comes at a time of increasing concerns related to the application of machine learning. Different machine learning techniques have been used in cardiothoracic surgery (Table S1). The public, professionals and regulators are wary of the use of artificial intelligence (AI), especially when it comes to the sensitive healthcare sector. In this background, the attempt to introduce machine learning technology in the field of cardiothoracic surgery must adhere to the highest regulatory standards, combined with the 2024 edition of the Chinese Cardiothoracic Surgery Treatment Standards issued by the Chinese Medical Doctor Association to ensure the reliability and safety of AI-assisted decision-making. We need a safe monitoring frame-work for this. We emphasize the risk-based regulatory framework (2), with appropriate preventive measures to enable responsible innovation. At the same time, the application of machine learning needs to be coordinated with the existing supervision of cardiothoracic surgery to ensure that the two can be effectively combined to avoid duplication of supervision or regulatory gaps. Moreover, in the field of safety and process supervision in the field of machine learning cardiothoracic treatment, the results of validation measures should be focused on (3). Although it is the responsibility of regulators to ensure the safety of the process, the main driver of “white box” access is to improve the interpretability of the process. Safety verification can rely on “black box” evaluation (4), that is, no deep knowledge of the algorithm or dataset is required (5), only methodological transparency, to ensure the safe use of machine learning in the field of cardiothoracic surgical treatment, while also protecting the incentive to innovate. To establish a coherent and risk-based regulatory framework for the full potential of machine learning in cardiothoracic surgery and to provide appropriate, targeted protections for patient safety (Table S2).

Supplementary

The article’s supplementary files as

10.21037/jtd-24-990 10.21037/jtd-24-990

Acknowledgments

Funding: None.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Provenance and Peer Review: This article was a standard submission to the journal. The article did not undergo external peer review.

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-990/coif). The authors have no conflicts of interest to declare.
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References

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