Khanna Narendra N, Maindarkar Mahesh A, Viswanathan Vijay, Fernandes Jose Fernandes E, Paul Sudip, Bhagawati Mrinalini, Ahluwalia Puneet, Ruzsa Zoltan, Sharma Aditya, Kolluri Raghu, Singh Inder M, Laird John R, Fatemi Mostafa, Alizad Azra, Saba Luca, Agarwal Vikas, Sharma Aman, Teji Jagjit S, Al-Maini Mustafa, Rathore Vijay, Naidu Subbaram, Liblik Kiera, Johri Amer M, Turk Monika, Mohanty Lopamudra, Sobel David W, Miner Martin, Viskovic Klaudija, Tsoulfas George, Protogerou Athanasios D, Kitas George D, Fouda Mostafa M, Chaturvedi Seemant, Kalra Mannudeep K, Suri Jasjit S
Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110001, India.
Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA.
Healthcare (Basel). 2022 Dec 9;10(12):2493. doi: 10.3390/healthcare10122493.
: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals.
由于以下原因，医疗费用持续上涨：（i）人口增长；（ii）人类老龄化；（iii）疾病流行率；（iv）使用医疗保健服务的患者频率增加；以及（v）价格上涨。人工智能（AI）在各种医疗保健应用中的优越性已广为人知，包括图像中病变的分割、语音识别、智能手机个人助手、导航、拼车应用等等。我们的研究基于两个假设：（i）与传统方法相比，人工智能提供更经济的解决方案；（ii）与人工智能诊断相比，人工智能治疗具有更强的经济性。这项新颖的研究旨在评估医疗保健成本背景下的人工智能技术，即在诊断和治疗领域，然后将其与传统的或非基于人工智能的方法进行比较。采用PRISMA方法选择了医疗保健领域中关于人工智能的200项最佳研究，主要关注成本降低，特别是在诊断和治疗方面。我们定义了诊断和治疗架构，研究了它们的特征，并对人工智能在诊断和治疗范式中所起的作用进行了分类。我们通过整合人工智能并将其与传统成本进行比较，对不同假设的各种组合进行了实验。最后，我们详细探讨了人工智能的三个强大的未来概念，即修剪、偏差、可解释性以及人工智能系统的监管批准。该模型显示，在诊断和治疗中使用人工智能工具可大幅节省成本。通过纳入修剪、减少人工智能偏差、可解释性和监管批准，可以提高人工智能的经济性。
