Why the study?
Heart surgeries pose a high risk of surgical errors due to complex anatomical structures, procedural complexity, and surgical team stress, prompting an exploration of artificial intelligence's potential to reduce these errors.
Does artificial intelligence reduce surgical errors and improve outcomes in patients undergoing heart surgeries?
Does artificial intelligence reduce surgical errors and improve outcomes in patients undergoing heart surgeries?
Artificial intelligence and large language models offer potential to reduce surgical errors and improve efficiency in cardiac surgery, but require further research to overcome limitations like hallucinations and high costs.
Answers are grounded in this paper and cite their sources.
Should not yet change cardiac surgical practice; leaves open the need for prospective trials to address barriers.
Of all surgeries, heart surgeries are considered to pose a higher risk of surgical errors due to the intricacies of the heart's anatomical structures, surgical complexities, the critical nature of procedures, and stress on the surgical team.In this paper, considering the significant impact of artificial intelligence (AI) on enhancing patient outcomes in healthcare, we explore AI's potential in reducing errors in heart surgeries.To that end, we reviewed recent papers on the intersection of artificial intelligence and heart surgeries.We analyzed their efficiency in reducing surgical errors and their promise to mitigate the risks involved in cardiac surgeries.Large language-based surgical assistants, such as CARES Copilot 1.0 and ChatGPT, are cost-effective solutions for underprivileged areas with high demand for cardiac surgeries because they enhance surgical outcomes, shorten ICU and hospital stays, and thus give opportunities to other patients awaiting surgery.At the same time, despite reducing surgical errors and improving patient outcomes, AI has some limitations, including hallucinations, biases in training data, threats of job displacement, and high deployment costs, especially for robotic surgeries and fine-tuning large language models, all of which remain significant barriers to AI's adoption, requiring further research.
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Yonas Bekele (2025) studied this question.
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