Associate Professor, Oncology and Palliative Care, Cyprus University of Technology, Osijek, Croatia.
Adjunct Professor, University of Turku, Turku, Finland; Teaching Assistant, Faculty of Dental Medicine and Health, Department of Nursing and Palliative Care, Osijek, Croatia; Unit Manager, Department for Oncology, Clinical Hospital Center, Osijek, Croatia.
Semin Oncol Nurs. 2023 Jun;39(3):151429. doi: 10.1016/j.soncn.2023.151429. Epub 2023 Apr 20.
The rapid advances in artificial intelligence (AI), big data, and machine learning (ML) technologies hold promise for personalized, equitable cancer care and improved health outcomes within the context of cancer and beyond. Furthermore, integrating these technologies into cancer research has been effective in addressing many of the challenges for cancer control and cure. This can be achieved through the insights generated from massive amounts of data, in ways that can help inform decisions, interventions, and precision cancer care. AI, big data, and ML technologies offer, either in isolation or in combination, unconventional pathways that facilitate the better understanding and management of cancer and its impact on the person. The value of AI, big data, and ML technologies has been acknowledged and integrated within the Cancer Moonshot program in the U.S. and the EU Beating Cancer Plan in Europe.
Relevant studies on the topic have formed the basis for this article.
In a shifting health care environment where cancer care is becoming more complex and demanding, big data and AI technologies can act as a vehicle to facilitating the care continuum. An increasing body of literature demonstrates their impactful contributions in areas such as treatment and diagnosis. These technologies, however, create additional requirements from health care professionals in terms of capacity and preparedness to integrate them effectively and efficiently in clinical practice. Therefore, there is an increasing need for investment and training in oncology to combat and overcome some of the challenges posed by cancer control.
AI, big data, and ML are increasingly integrated in various aspects of health care. As a result, health care professionals, including nurses, will need to adjust in an ever-changing practice environment where these technologies have potential applications in clinical settings to improve risk stratification, early detection, and surveillance management of cancer patients.
人工智能（AI）、大数据和机器学习（ML）技术的快速发展有望在癌症及其他领域实现个性化、公平的癌症护理和改善健康结果。此外，将这些技术纳入癌症研究对于解决癌症控制和治疗的许多挑战非常有效。通过从大量数据中生成的见解，可以实现这一目标，这些见解可以帮助为决策、干预和精准癌症护理提供信息。AI、大数据和 ML 技术提供了非传统的途径，可以帮助更好地理解和管理癌症及其对个人的影响。AI、大数据和 ML 技术的价值已经得到美国癌症登月计划和欧盟抗癌计划的认可和整合。
关于该主题的相关研究构成了本文的基础。
在癌症护理变得更加复杂和苛刻的医疗环境中，大数据和 AI 技术可以作为促进护理连续性的工具。越来越多的文献证明了它们在治疗和诊断等领域的有影响力的贡献。然而，这些技术对医疗保健专业人员提出了额外的要求，需要他们具备能力并为有效和高效地将其整合到临床实践中做好准备。因此，需要在肿瘤学领域进行投资和培训，以应对和克服癌症控制带来的一些挑战。
AI、大数据和 ML 越来越多地整合到医疗保健的各个方面。因此，医疗保健专业人员，包括护士，将需要在不断变化的实践环境中进行调整，在这个环境中，这些技术有可能在临床环境中应用，以改善癌症患者的风险分层、早期发现和监测管理。
