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. 2022 Dec 9;10(12):2493.
doi: 10.3390/healthcare10122493.

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

Narendra N Khanna  1 , Mahesh A Maindarkar  2   3 , Vijay Viswanathan  4 , Jose Fernandes E Fernandes  5 , Sudip Paul  3 , Mrinalini Bhagawati  3 , Puneet Ahluwalia  6 , Zoltan Ruzsa  7 , Aditya Sharma  8 , Raghu Kolluri  9 , Inder M Singh  2 , John R Laird  10 , Mostafa Fatemi  11 , Azra Alizad  12 , Luca Saba  13 , Vikas Agarwal  14 , Aman Sharma  14 , Jagjit S Teji  15 , Mustafa Al-Maini  16 , Vijay Rathore  17 , Subbaram Naidu  18 , Kiera Liblik  19 , Amer M Johri  19 , Monika Turk  20 , Lopamudra Mohanty  21 , David W Sobel  22 , Martin Miner  23 , Klaudija Viskovic  24 , George Tsoulfas  25 , Athanasios D Protogerou  26 , George D Kitas  27   28 , Mostafa M Fouda  29 , Seemant Chaturvedi  30 , Mannudeep K Kalra  31 , Jasjit S Suri  2
Affiliations

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

Narendra N Khanna et al. Healthcare (Basel). .

Abstract

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.

Keywords: AI bias; AI explainability; AI pruning; artificial intelligence; cost-effectiveness; deep learning; diagnosis; health economics; machine learning; recommendations; treatment.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
PRISMA model for selection of studies.
Figure 2
Figure 2
Statistical distribution of various diseases.
Figure 3
Figure 3
The generalized architecture of the ML-based system.
Figure 4
Figure 4
Comparing the ML-based CVD risk assessment using AtheroEdge™ 3.0ML with (A) 13 types of CCVRC and (B) the standard-of-care ASCVD calculator [131].
Figure 5
Figure 5
Applications of AI in healthcare diagnosis. AI: artificial intelligence, ML: machine learning, DL: deep learning, TL: transfer learning, CVD: cardiovascular diseases.
Figure 6
Figure 6
The graphical user interface of AtheropointTM (3.0) AI-based CVD Risk Stratification system to predict a person’s 10-year CVD risk. (A) Trained Model Selection Process and (B) Risk Stratification Predication Process [117]. (Courtesy of AtheroPoint, Roseville, CA, USA permission granted).
Figure 7
Figure 7
CNN-based medical image analysis architecture. (Courtesy of AtheroPoint, Roseville, CA, USA permission granted).
Figure 8
Figure 8
Role of AI in improving the pipeline of radiology, from clinical protocol selection to treatment prognosis [12].
Figure 9
Figure 9
The AI economical model for the diagnosis and treatment against the conventional model.
Figure 10
Figure 10
Patients per day per hospital for diagnosis.
Figure 11
Figure 11
Time-saving for AI-based diagnosis model (green). Conventional model (red) vs. AI (blue) showing year vs. time (in hours).
Figure 12
Figure 12
Cost saving (green) in diagnosis: conventional (red) vs. AI (blue).
Figure 13
Figure 13
Patients per day per Hospital for treatment (red), number of hospitals (green).
Figure 14
Figure 14
Treatment time-saving (green). Conventional time (red) and AI time (blue).
Figure 15
Figure 15
Cost saving in treatment (green) shows a non-linear curve. Conventional treatment cost (red) vs. AI treatment cost (blue).
Figure 16
Figure 16
Cost saving in USD using AI-based system, AI-Diagnosis (blue), AI-Treatment (red).
Figure 17
Figure 17
Eight systems were created using four pruning approaches (DE, GA, PSO, and WO): FCN-DE, FCN-GA, FCN-PSO, FCN-WO and SegNet-DE, SegNet-GA, SegNet-PSO, and SegNet-WO [35].
Figure 18
Figure 18
Eight aspects of Explainable AI [165].
See this image and copyright information in PMC

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