人工智能与外科手术

IF 2.9 4区 医学 Q2 GASTROENTEROLOGY & HEPATOLOGY
Masashi Takeuchi, Yuko Kitagawa
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By analyzing large amounts of medical data, including patient records, imaging findings (e.g., CT and endoscopy), and previous history, AI can help surgeons plan more effective and personalized surgical strategies. For instance, it can predict potential perioperative complications, suggesting optimal surgical approaches, and even simulate surgical outcomes. Furthermore, AI can analyze CT scans to create 3D models of patient anatomy, such as blood vessels, allowing surgeons to plan surgeries with a level of detail that was previously unattainable.</p><p>Robotic surgery, one of the most notable applications of AI in surgery, has been a game changer. Compared with the conventional approach, robotic systems such as the da Vinci Surgical System enable surgeons to perform fine and complex procedures with more precision, flexibility, and control. 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Such precision in care planning addresses patient-specific requirements and improves the overall recovery experience, thereby potentially accelerating the healing process.</p><p>AI is also revolutionizing surgical training for trainees. Implementing AI in training includes the use of innovative tools such as virtual reality and augmented reality simulations, fundamentally changing the learning experience of surgeons. With AI able to tailor the training experience to each trainee's skill level, these simulations can provide a dynamic and adaptive learning platform that goes beyond traditional methods; thus, the modules can be individualized according to the trainee's needs and proficiency level.</p><p>In this way, young surgeons can immerse themselves in real scenarios and gain hands-on experience without the risks associated with actual surgery. 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引用次数: 0

摘要

人工智能(AI)对医疗保健领域产生了重大影响,尤其是成像和视频分析。它可以为临床决策提供重要支持,包括在内窥镜检查中自动诊断胃肠道癌症,以及在计算机断层扫描(CT)中自动检测肺部病变。将人工智能融入外科手术可能会重新定义外科手术程序,开创个性化和数据驱动型医疗保健的新时代。通过分析大量医疗数据,包括患者记录、成像结果(如 CT 和内窥镜检查)以及既往病史,人工智能可以帮助外科医生制定更有效、更个性化的手术策略。例如,它可以预测潜在的围手术期并发症,建议最佳手术方法,甚至模拟手术结果。此外,人工智能还可以分析 CT 扫描,创建病人解剖结构(如血管)的 3D 模型,让外科医生能够以以前无法达到的详细程度规划手术。与传统方法相比,机器人系统(如达芬奇手术系统)使外科医生能够更精确、更灵活、更可控地进行精细复杂的手术。人工智能增强了这些系统的功能,可提供实时分析和精确运动,并从每次手术中学习,从而长期改善手术效果。3, 4 此外,人工智能理解和分析手术过程的能力有助于手术团队无缝共享信息,有助于及早发现并发症,确保快速干预。这些由人工智能算法控制的机器人系统能以无与伦比的精度完成复杂的手术。它们通常是为适应每个外科医生的独特技能而量身定制的,从而提高了手术的准确性,同时最大限度地减少了出错的可能性。人类外科医生与人工智能引导的机器人系统之间的协同合作,代表着一个合作式高水平外科手术的新时代。通过使用传感器和数据分析,人工智能可以在出现严重问题之前预见潜在的并发症和再入院情况,使外科医生能够提供及时和有针对性的干预,为整体术后护理做出重大贡献。人工智能的参与不仅仅是监测,而是根据每位患者的独特数据资料,通过高度个性化的方式为其量身定制术后护理计划。这种个性化方法考虑了个人健康参数、病史和特定的恢复需求。这种精确的护理计划可满足患者的特定需求,改善整体恢复体验,从而有可能加快愈合过程。在培训中实施人工智能包括使用虚拟现实和增强现实模拟等创新工具,从根本上改变外科医生的学习体验。由于人工智能能够根据每个学员的技能水平定制培训体验,这些模拟可以提供一个超越传统方法的动态自适应学习平台;因此,这些模块可以根据学员的需求和熟练程度进行个性化定制。通过利用人工智能增强型互动模拟,可以透彻地理解外科技术,为成功的外科手术奠定必要的实用技能基础。对人工智能和机器人系统的依赖引发了手术失误时的责任和问责问题。鉴于这些系统需要访问患者的个人数据,数据隐私和安全问题也随之出现。培训外科医生有效使用这些先进技术也是一项挑战,因为不同地域和社会经济群体需要公平地获得这些技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial intelligence and surgery

Artificial intelligence (AI) has a significant impact on the field of health care, particularly imaging and video analyses. It can considerably support clinical decision-making, including the automatic diagnosis of gastrointestinal cancer during endoscopy and automated detection of pulmonary lesions on computed tomography (CT).1, 2 In the future, AI may provide innovative solutions that improve surgical efficiency and patient outcomes in the field of surgical procedures. Integrating AI into surgery can potentially redefine the surgical procedures, ushering in a new era of personalized and data-driven healthcare.

The role of AI in preoperative planning is substantial. By analyzing large amounts of medical data, including patient records, imaging findings (e.g., CT and endoscopy), and previous history, AI can help surgeons plan more effective and personalized surgical strategies. For instance, it can predict potential perioperative complications, suggesting optimal surgical approaches, and even simulate surgical outcomes. Furthermore, AI can analyze CT scans to create 3D models of patient anatomy, such as blood vessels, allowing surgeons to plan surgeries with a level of detail that was previously unattainable.

Robotic surgery, one of the most notable applications of AI in surgery, has been a game changer. Compared with the conventional approach, robotic systems such as the da Vinci Surgical System enable surgeons to perform fine and complex procedures with more precision, flexibility, and control. AI enhances these systems by providing real-time analysis and precision in movement and learning from each surgery performed, thereby improving the outcomes over time. In particular, advanced image recognition powered by AI algorithms aids in quickly identifying critical anatomical structures, navigating complicated anatomy more easily, and reducing the risk of developing surgical complications.3, 4 In addition, AI's ability to understand and analyze surgical process facilitates seamless information sharing across the surgical team, contributing to the early detection of complications and ensuring rapid intervention.5 Moreover, incorporating AI into surgical robots represents a giant leap forward. These AI algorithm-controlled robotic systems perform complex procedures with unparalleled precision. They are often tailored to adapt to the unique skills of individual surgeons, thereby improving surgical accuracy while minimizing the potential for error. The synergistic cooperation between human surgeons and AI-guided robotic systems represents a new era of cooperative, high-leveled surgery.

AI also plays a pivotal role in overseeing and optimizing patient recovery after surgery. Through the use of sensors and data analysis, AI can foresee potential complications and readmissions before serious problems develop, enabling surgeons to provide timely and targeted interventions and contributing significantly to overall postoperative care. AI involvement goes beyond mere monitoring to tailoring postoperative care plans through a high degree of personalization based on each patient's unique data profile. This personalized approach considers individual health parameters, medical history, and specific recovery needs. Such precision in care planning addresses patient-specific requirements and improves the overall recovery experience, thereby potentially accelerating the healing process.

AI is also revolutionizing surgical training for trainees. Implementing AI in training includes the use of innovative tools such as virtual reality and augmented reality simulations, fundamentally changing the learning experience of surgeons. With AI able to tailor the training experience to each trainee's skill level, these simulations can provide a dynamic and adaptive learning platform that goes beyond traditional methods; thus, the modules can be individualized according to the trainee's needs and proficiency level.

In this way, young surgeons can immerse themselves in real scenarios and gain hands-on experience without the risks associated with actual surgery. By utilizing AI-enhanced interactive simulations, surgical techniques can be thoroughly understood, laying the foundation of practical skills essential for successful surgery.

While AI demonstrates significant benefits in surgery, it still has ethical and practical challenges. Reliance on AI and robotic systems raises questions about liability and accountability in the event of surgical errors. Given that these systems require access to patients' personal data, data privacy and security issues also emerge. Training surgeons to effectively use these advanced technologies is also a challenge because of the need for equitable access to these technologies among different geographic and socioeconomic groups.

In conclusion, the integration of AI into surgery represents a major leap forward in medical technology. It can potentially increase surgical accuracy, improve patient outcomes, and revolutionize surgical training. However, the ethical and practical challenges associated with AI must be addressed. With careful consideration and continued development, AI in surgery will continue to evolve and benefit patients and healthcare professionals.

Yuko Kitagawa received lecture fees from Chugai Pharmaceutical Co., Ltd., Taiho Pharmaceutical Co., Ltd, Asahi Kasei Pharma Corporation, Otsuka Pharmaceutical Factory Inc., Shionogi & Co., Ltd., Nippon Covidien Inc., Ono Pharmaceutical Co., Ltd., Bristol-Myers Squibb K. K. Author Y. K was supported by grants from Chugai Pharmaceutical Co., Ltd., Taiho Pharmaceutical Co., Ltd, Yakult Honsha Co. Ltd., Asahikasei Co., Ltd., Otsuka Pharmaceutical Co., Ltd., Takeda Pharmaceutical Co., Ltd., Ono Pharmaceutical Co., Ltd., Tsumura & Co., Kyouwa Hakkou Kirin Co., Ltd., Dainippon Sumitomo Pharma Co., Ltd., EA Pharma Co., Ltd., Astellas Pharma Inc., Toyama Chemical Co., Ltd., Medicon Inc., Kaken Pharmaceutical Co., Ltd., Eisai Co., Ltd., Otsuka Pharmaceutical Factory Inc., Teijin Pharma Ltd., Nihon Pharmaceutical Co., Ltd., and Nippon Covidien Inc. Author Y. K held an endowed chair provided by Chugai Pharmaceutical Co., Ltd., and Taiho Pharmaceutical Co., Ltd, outside the submitted work. Masashi Takeuchi has stocks from Direava inc. outside the submitted work. Author Yuko Kitagawa is a current chief editor of Annals of Gastroenterological Surgery.

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来源期刊
Annals of Gastroenterological Surgery
Annals of Gastroenterological Surgery GASTROENTEROLOGY & HEPATOLOGY-
CiteScore
5.30
自引率
11.10%
发文量
98
审稿时长
11 weeks
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