Artificial Intelligence In Radiology: Opportunities And Challenges For Clinical Implementations

Abstract

Artificial intelligence (AI) has the potential to revolutionize medical imaging and transform radiology practice.

AI has the potential to improve accuracy, consistency, and productivity in radiology.   However, regulatory and validation hurdles have slowed clinical integration of many AI applications. This paper aims to explore opportunities for AI in radiology, requirements for clinical use, and strategies to address challenges in implementation.

However, several challenges currently hinder the widespread adoption of AI in clinical radiology. Lack of large, diverse datasets labeled by expert radiologists limits the ability to train models on rare diseases and patient subgroups. Several studies have demonstrated AI's ability to detect subtle findings that may be missed by radiologists alone.

By assisting in detection of subtle abnormalities, AI has the potential to improve clinical decision making and patient outcomes. Improved workflow efficiency. AI can help prioritize exams by highlighting those requiring urgent or emergent reads.

Quantitative image analysis with AI enables extraction of numerous biomarkers and features from medical images that are not easily discerned by the human eye. This allows for objective characterization of diseases and monitoring of treatment  responses.

AI has considerable potential to augment human capabilities in radiology and improve key areas like diagnostic accuracy, workflow, and research - ultimately enabling higher quality and more efficient patient care. Of course, responsible development and evaluation remain important to realize this potential safely and effectively in clinical practice.

A literature search was conducted in PubMed, Web of Science, and IEEE Xplore Digital Library for articles published between 2015-2022 using the search terms "artificial intelligence", "deep learning", "machine learning", "radiology", "clinical", and "implementation". Reference lists of relevant articles were also reviewed to identify additional sources. Only peer-reviewed articles from reputed journals and conferences were included.

The literature revealed several opportunities for AI to enhance radiology such as detecting subtle findings to improve diagnostic accuracy, reducing workload by prioritizing exams, standardizing diagnoses through consistency and enabling personalized medicine through quantitative image analysis. However, clinical adoption faces challenges in regulatory approval, validation studies, and physician adoption.

While AI shows promise, clinical implementation requires overcoming regulatory hurdles. The FDA has provided guidance but approval remains complex for many applications. Large multi-site studies are needed to validate AI for specific tasks and patient populations.

With further research and prudent oversight, AI has the potential to significantly improve radiology. Focusing on transparent validation studies, user-centered design, and collaborative human-AI partnerships may help realize AI's benefits for patients while navigating implementation challenges.