Department of Pharmaceutical Chemistry/Analysis, Delhi Pharmaceutical Sciences & Research University, Pushp Vihar, Sector 3, New Delhi, 110017, India.
Department of Pharmaceutical Chemistry/Analysis, Delhi Pharmaceutical Sciences & Research University, Pushp Vihar, Sector 3, New Delhi, 110017, India.
Comput Biol Med. 2024 Sep;179:108917. doi: 10.1016/j.compbiomed.2024.108917. Epub 2024 Jul 25.
Since the past decade, the interest towards more precise and efficient healthcare techniques with special emphasis on diagnostic techniques has increased. Artificial Intelligence has proved to be instrumental in development of various such techniques. The various types of AI like ML, NLP, RPA etc. are being used, which have streamlined and organised the Electronic Health Records (EHR) along with aiding the healthcare provider with decision making and sample and data analysis. This article also deals with the 3 major categories of diagnostic techniques - Imaging based, Pathology based and Preventive diagnostic techniques and what all changes and modifications were brought upon them, due to use of AI. Due to such a high demand, the investment in AI based healthcare techniques has increased substantially, with predicted market size of almost 188 billon USD by 2030. In India itself, AI in healthcare is expected to raise the GDP by 25 billion USD by 2028. But there are also several challenges associated with this like unavailability of quality data, black box issue etc. One of the major challenges is the ethical considerations and issues during use of medical records as it is a very sensitive document. Due to this, there is several trust issues associated with adoption of AI by many organizations. These challenges have also been discussed in this article. Need for further development in the AI based diagnostic techniques is also done in the article. Alongside, the production of such techniques and devices which are easy to use and simple to incorporate into the daily workflows have immense scope in the upcoming times. The increasing scope of Clinical Decision Support System, Telemedicine etc. make AI a promising field in the healthcare and diagnostics arena. Concluding the article, it can be said that despite the presence of various challenges to the implementation and usage, the future prospects for AI in healthcare is immense and work needs to be done in order to ensure the availability of resources for same so that high level of accuracy can be achieved and better health outcomes can be provided to patients. Ethical concerns need to be addressed for smooth implementation and to reduce the burden of the developers, which has been discussed in this narrative review article.
在过去的十年中，人们对更精确和高效的医疗技术产生了浓厚的兴趣，尤其侧重于诊断技术。人工智能已被证明是开发各种此类技术的有力工具。各种类型的人工智能，如机器学习、自然语言处理、机器人流程自动化等，正在被使用，它们简化和组织了电子健康记录（EHR），并帮助医疗保健提供者进行决策以及对样本和数据分析。本文还涉及诊断技术的 3 个主要类别——基于成像的、基于病理学的和预防性诊断技术，以及由于使用人工智能，它们发生了哪些变化和修改。由于这种高需求，基于人工智能的医疗技术的投资大幅增加，预计到 2030 年市场规模将达到近 1880 亿美元。仅在印度，到 2028 年，人工智能在医疗保健领域预计将使 GDP 增长 250 亿美元。但随之而来的也有一些挑战，例如缺乏高质量的数据、黑箱问题等。其中一个主要挑战是在使用医疗记录时的道德考虑和问题，因为它是一份非常敏感的文件。因此，许多组织在采用人工智能时都存在一些信任问题。本文也讨论了这些挑战。本文还探讨了进一步开发基于人工智能的诊断技术的必要性。此外，在未来的一段时间内，易于使用和简单融入日常工作流程的此类技术和设备的生产将具有巨大的发展潜力。临床决策支持系统、远程医疗等的不断扩大的范围使得人工智能在医疗保健和诊断领域成为一个有前途的领域。总的来说，尽管在实施和使用方面存在各种挑战，但人工智能在医疗保健领域的未来前景是广阔的，需要开展工作，以确保有足够的资源来实现高水平的准确性，并为患者提供更好的健康结果。本文讨论了需要解决道德问题，以实现平稳实施并减轻开发人员的负担。
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