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非洲在临床放射治疗中应用人工智能的准备情况:医学物理师引领前行之路。

Africa's readiness for artificial intelligence in clinical radiotherapy delivery: Medical physicists to lead the way.

作者信息

Manson Eric Naab, Hasford Francis, Trauernicht Chris, Ige Taofeeq Abdallah, Inkoom Stephen, Inyang Samuel, Samba Odette, Khelassi-Toutaoui Nadia, Lazarus Graeme, Sosu Edem Kwabla, Pokoo-Aikins Mark, Stoeva Magdalena

机构信息

University for Development Studies, Tamale, Ghana.

Ghana Atomic Energy Commission, Accra, Ghana.

出版信息

Phys Med. 2023 Sep;113:102653. doi: 10.1016/j.ejmp.2023.102653. Epub 2023 Aug 14.

Abstract

BACKGROUND

There have been several proposals by researchers for the introduction of Artificial Intelligence (AI) technology due to its promising role in radiotherapy practice. However, prior to the introduction of the technology, there are certain general recommendations that must be achieved. Also, the current challenges of AI must be addressed. In this review, we assess how Africa is prepared for the integration of AI technology into radiotherapy service delivery.

METHODS

To assess the readiness of Africa for integration of AI in radiotherapy services delivery, a narrative review of the available literature from PubMed, Science Direct, Google Scholar, and Scopus was conducted in the English language using search terms such as Artificial Intelligence, Radiotherapy in Africa, Machine Learning, Deep Learning, and Quality Assurance.

RESULTS

We identified a number of issues that could limit the successful integration of AI technology into radiotherapy practice. The major issues include insufficient data for training and validation of AI models, lack of educational curriculum for AI radiotherapy-related courses, no/limited AI teaching professionals, funding, and lack of AI technology and resources. Solutions identified to facilitate smooth implementation of the technology into radiotherapy practices within the region include: creating an accessible national data bank, integrating AI radiotherapy training programs into Africa's educational curriculum, investing in AI technology and resources such as electronic health records and cloud storage, and creation of legal laws and policies to support the use of the technology. These identified solutions need to be implemented on the background of creating awareness among health workers within the radiotherapy space.

CONCLUSION

The challenges identified in this review are common among all the geographical regions in the African continent. Therefore, all institutions offering radiotherapy education and training programs, management of the medical centers for radiotherapy and oncology, national and regional professional bodies for medical physics, ministries of health, governments, and relevant stakeholders must take keen interest and work together to achieve this goal.

摘要

背景

由于人工智能(AI)技术在放射治疗实践中具有广阔前景,研究人员提出了多项引入该技术的建议。然而,在引入该技术之前,必须达成某些一般性建议。此外,还必须应对人工智能当前面临的挑战。在本综述中,我们评估了非洲在将人工智能技术整合到放射治疗服务提供方面的准备情况。

方法

为评估非洲在放射治疗服务提供中整合人工智能的准备情况,我们使用“人工智能”“非洲的放射治疗”“机器学习”“深度学习”和“质量保证”等搜索词,对来自PubMed、Science Direct、谷歌学术和Scopus的可用英文文献进行了叙述性综述。

结果

我们确定了一些可能限制人工智能技术成功整合到放射治疗实践中的问题。主要问题包括用于人工智能模型训练和验证的数据不足、缺乏与人工智能放射治疗相关课程的教育课程、没有/有限的人工智能教学专业人员、资金以及缺乏人工智能技术和资源。为促进该技术在该地区放射治疗实践中的顺利实施而确定的解决方案包括:创建一个可访问的国家数据库、将人工智能放射治疗培训计划纳入非洲的教育课程、投资于人工智能技术和资源(如电子健康记录和云存储)以及制定支持该技术使用的法律法规和政策。这些已确定的解决方案需要在提高放射治疗领域卫生工作者认识的背景下实施。

结论

本综述中确定的挑战在非洲大陆的所有地理区域都很常见。因此,所有提供放射治疗教育和培训计划的机构、放射治疗和肿瘤医学中心的管理部门、国家和地区医学物理专业机构、卫生部、政府及相关利益攸关方必须密切关注并共同努力实现这一目标。

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