Overcoming diagnostic challenges of artificial intelligence in pathology and radiology: Innovative solutions and strategies

Abstract

The advent of artificial intelligence (AI) has brought about significant changes in the fields of pathology and radiology, particularly in the area of diagnostic accuracy. Although AI has enormous potential for enhancing the precision and effectiveness of diagnosis, it also presents an array of challenges. This review article examines the diagnostic challenges of AI in pathology and radiology. The article begins by giving a general review of AI and its potential applications in pathology and radiology. It then discusses the challenges posed by AI in the areas of data quality, generalization, interpretability, and hardware limitations. The article also explores the ethical and regulatory implications of AI in diagnostic settings, including issues of bias and transparency. Finally, the article offers potential solutions to address these challenges, such as standardization of AI algorithms, data sharing initiatives, saliency mapping, adversarial training of algorithms, cloud computing, edge computing, hybrid approaches, and increased collaboration between human experts and AI systems. Overall, this review highlights the critical importance of addressing the diagnostic challenges of AI in pathology and radiology to make sure AI is able to achieve its potential to enhance patient care.