Author(s) Name | Publication Year | Study Location | Study Objective | AI Technology | Healthcare Area or Disease Studied | Type of Evaluation Employed | Key Findings | |
Pifer, R. | 2023 | United States | To estimate potential cost savings from implementing AI in healthcare | Not Specified | Healthcare system | Cost analysis | Implementation of AI could save: The US healthcare system $200 - 360 billion annually. Private payers between $80 - 110 billion annually. Physician groups between $20 - 60 billion annually. | |
Spatharou et al. | 2020 | Europe | To explore how AI can support improvements in care outcomes, patient experience and access to healthcare services. | Various AI applications | Healthcare system | Cost analysis | AI can Minimize inefficiencies, create cost-effective healthcare ecosystem, and maximize return on investment. | |
Sahni et al. | 2023 | United States | To estimate potential impact of wider AI adoption on US healthcare spending | Various AI applications | Healthcare system | Cost analysis | Wider AI implementation could result in 5% - 10% savings in US healthcare spending annually. | |
Ramessur et al. | 2021 | Not specified | To examine cost impact of integrating AI and telemedicine in ophthalmology | AI and telemedicine | Ophthalmology | Literature Review | Integration led to 15% reduction in urgent transfers and 24% reduction in outreach consultations, saving $1.1 million | |
Ruamvibo- onsuk et al. | 2021 | Not specified | To discuss economic evaluations of AI in ophthalmology | AI software | Ophthalmology (retinopathy screening) | Literature review | AI could enhance quality and reduce cost | |
Leeuwenthe et al. | 2022 | Not specified | To discuss how AI improves efficiency and outcomes in radiology | Various AI applications relevant to radiology | Radiology | Literature review | AI shows promise in streamlining workflow, speeding interpretation, and improving accuracy and personalization. | |
Rossi et al. | 2022 | Not specified | To analyze cost-effectiveness of AI applications in medicine | Various AI applications | Healthcare system | Literature review | Results are diverse. Cost-effectiveness depends on use cases and assumed effects of diagnosis. | |
Khanna et al. | 2022 | Not specified | To evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment | Various AI applications | Diagnosis & treatment | Literature review | AI can save time for diagnosis & treatment which in turn results in cost-saving. | |
Rossi et al. | 2022 | Not specified | To analyze cost-effectiveness of AI as decision-support system for disease detection | AI models | dermatology, dentistry, and ophthalmology | Cost- effectiveness analysis using Markov models | AI can effectively detect and assess melanoma, dental caries, and diabetic retinopathy at a lower cost compared to standard care | |
De Vos et al. | 2022 | Not specified | To investigate cost-effectiveness of machine learning tool for ICU patients | Prediction model (Pacmed Critical) | Intensive care units | Literature Review | Pacmed Critical for ICE was a cost-effective strategy and continued to be cost-effective when compared to standard care |