Healthcare (Dec 2022)
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 Rathore,
- Subbaram 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
Affiliations
- Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110001, India
- Mahesh A. Maindarkar
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Vijay Viswanathan
- MV Diabetes Centre, Royapuram, Chennai 600013, India
- Jose Fernandes E Fernandes
- Department of Vascular Surgery, University of Lisbon, 1649-004 Lisbon, Portugal
- Sudip Paul
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
- Mrinalini Bhagawati
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
- Puneet Ahluwalia
- Max Institute of Cancer Care, Max Super Specialty Hospital, New Delhi 110017, India
- Zoltan Ruzsa
- Invasive Cardiology Division, Faculty of Medicine, University of Szeged, 6720 Szeged, Hungary
- Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22904, USA
- Raghu Kolluri
- Ohio Health Heart and Vascular, Columbus, OH 43214, USA
- Inder M. Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA
- Mostafa Fatemi
- Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
- Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
- Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, 40138 Cagliari, Italy
- Vikas Agarwal
- Department of Immunology, SGPGIMS, Lucknow 226014, India
- Aman Sharma
- Department of Immunology, SGPGIMS, Lucknow 226014, India
- Jagjit S. Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
- Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, Canada
- Vijay Rathore
- AtheroPoint LLC, Roseville, CA 95661, USA
- Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA
- Kiera Liblik
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada
- Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada
- Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany
- Lopamudra Mohanty
- Department of Computer Science, ABES Engineering College, Ghaziabad 201009, India
- David W. Sobel
- Rheumatology Unit, National Kapodistrian University of Athens, 15772 Athens, Greece
- Martin Miner
- Men’s Health Centre, Miriam Hospital Providence, Providence, RI 02906, USA
- Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia
- George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece
- Athanasios D. Protogerou
- Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece
- George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK
- Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
- Seemant Chaturvedi
- Department of Neurology & Stroke Program, University of Maryland School of Medicine, Baltimore, MD 21201, USA
- Mannudeep K. Kalra
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- DOI
- https://doi.org/10.3390/healthcare10122493
- Journal volume & issue
-
Vol. 10,
no. 12
p. 2493
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