Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

Narendra N. Khanna, Mahesh A. Maindarkar, Vijay Viswanathan, Jose Fernandes E. Fernandes, Sudip Paul, Mrinalini Bhagawati, Puneet Ahluwalia, Zoltan Ruzsa, Aditya Sharma, Raghu Kolluri, Inder M. Singh, John R. Laird, Mostafa Fatemi, Azra Alizad, Luca Saba, Vikas Agarwal, Aman Sharma, Jagjit S. Teji, Mustafa Al-Maini, Vijay RathoreSubbaram Naidu, Kiera Liblik, Amer M. Johri, Monika Turk, Lopamudra Mohanty, David W. Sobel, Martin Miner, Klaudija Viskovic, George Tsoulfas, Athanasios D. Protogerou, George D. Kitas, Mostafa M. Fouda, Seemant Chaturvedi, Mannudeep K. Kalra, Jasjit S. Suri

Research output: Contribution to journalArticlepeer-review


Motivation: 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. Objective: 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. Methodology: 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. Conclusions: 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.

Original languageEnglish (US)
Article number2493
JournalHealthcare (Switzerland)
Issue number12
StatePublished - Dec 2022


  • AI bias
  • AI explainability
  • AI pruning
  • artificial intelligence
  • cost-effectiveness
  • deep learning
  • diagnosis
  • health economics
  • machine learning
  • recommendations
  • treatment

ASJC Scopus subject areas

  • Leadership and Management
  • Health Policy
  • Health Informatics
  • Health Information Management


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