The 24 Big Challenges of Artificial Inteligence Adoption in Healthcare
Review Article
DOI:
https://doi.org/10.5281/zenodo.8340188Keywords:
Artificial Intelligence, Radiology, Learning Curve, Deep Learning, Health CentersAbstract
Introduction: The integration of Artificial Intelligence (AI) into medical disciplines has shown significant potential. However, while an abundance of literature is enthusiastic about the potential and promises of AI, particularly in medical diagnostics, there is a distinct of discussion concerning the multitude of challenges associated with its widespread adoption in practical medical settings.
Objective: This study aims to thoroughly analyze the challenges associated with adopting artificial intelligence technologies in medical practice. It provides a realistic perspective on the progress of this technology, countering the often overly idealized viewpoints that primarily showcase advancements in prototypes and technological demonstrators within controlled laboratory conditions.
Method: The research design of this study is grounded in the method of document analysis/review. In this context, numerous scientific works were explored through platforms such as Google Scholar, PubMed, BioMed Central, Cochrane, and various scietific databases. Access to articles was obtained, followed by meticulous data analysis and assessments. Search criteria were adjusted based for each of the challenges under examination.
Results: A total of 24 significant challenges have been identified, intricately interconnected, and dissected using examples that illustrate the maturity level of AI-based developments within the medical domain. These challenges have been categorized into three main categories based on their nature. Each section has been wriiten in a way that can be independently comprehended. The future holds great promise, as underscored by numerous articles showcasing the remarkable advancements arising from the synergy between medicine and artificial intelligence. Hence, there is a need to develop critical thinking to discern the benefits, current limitations, and new paths to overcome them.
Conclusion: None of the challenges holds greater importance than the others. The evolution of artificial intelligence in medicine entails collectively overcoming these challenges, using strategies to maximize benefits for both patients and medical experts.
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