The rising cost of medical treatment has become a growing concern, driven by factors such as an increasing population, an aging society, and the prevalence of diseases. However, the integration of Artificial Intelligence (AI) in healthcare offers a promising solution to address these challenges. AI has already proven its superiority in various healthcare applications, including image segmentation, speech recognition, and personal assistants. This study aims to evaluate the economic impact of AI in healthcare, specifically in the areas of diagnosis and treatment, and compare it to traditional methods.
AI’s Economic Advantage
The study is based on two hypotheses: firstly, that AI offers more economic solutions compared to conventional methods, and secondly, that AI treatment offers stronger economics compared to AI diagnosis. To investigate these hypotheses, the researchers utilized the PRISMA methodology to select the best 200 studies on AI in healthcare, with a primary focus on cost reduction in diagnosis and treatment. The researchers defined the architecture of AI-based diagnosis and treatment systems and examined their characteristics. They then categorized the roles that AI plays in the diagnostic and therapeutic paradigms. By integrating AI into these processes and comparing the costs against conventional methods, the researchers were able to assess the economic impact of AI in healthcare.
Figure 1: The generalized architecture of the ML-based system.
Cost Savings and Future Concepts
The results of the study revealed significant cost savings when utilizing AI tools in diagnosis and treatment. The economic benefits of AI can be further enhanced by incorporating pruning techniques to optimize AI algorithms, reducing AI bias, ensuring explainability of AI systems, and obtaining regulatory approvals. Pruning involves removing unnecessary components from AI algorithms, resulting in more efficient and cost-effective systems. By addressing AI bias, healthcare providers can ensure fair and accurate diagnoses and treatments. Explainability of AI systems is crucial for building trust and understanding among healthcare professionals and patients. Regulatory approvals are necessary to ensure the safety and effectiveness of AI technologies in healthcare.
Implications for the Healthcare Industry
The findings of this study have significant implications for the healthcare industry. The integration of AI in diagnosis and treatment can lead to substantial cost savings, making healthcare more affordable and accessible for patients. Additionally, AI can enhance the accuracy and efficiency of medical procedures, improving patient outcomes and reducing the burden on healthcare professionals.
Conclusion
Furthermore, the future concepts of pruning, bias reduction, explainability, and regulatory approvals will play a crucial role in maximizing the economic benefits of AI in healthcare. These concepts will ensure that AI technologies are well placed within HEOR methodologies and evaluation.
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