Deep Learning for Medical Imaging: A Comprehensive Review of NLP Algorithms, Advancements and Challenges

Abstract

The present study examines the impact of Artificial Intelligence (AI) and Machine Learning (ML) on medical imaging, focusing on the demand for automated analysis of unstructured data in Electronic Medical Records (EMRs). It addresses the challenges in extracting knowledge from this data despite advancements in Natural Language Processing (NLP) and image processing. This review covers key principles and challenges of applying ML, including the use of algorithms like logistic regression, decision trees, and neural networks to classify and predict illnesses. It also explores various machine learning techniques such as supervised, unsupervised, and reinforcement learning, and emphasizes the importance of data preprocessing, feature selection, and model evaluation. The review also highlights various AI applications in medical imaging, including image segmentation, classification, registration, and reconstruction across modalities like X-ray, CT, MRI, and ultrasound. It also points out AI’s potential in enhancing robotic surgery through innovative techniques such as holography and attention models for early disease detection. While deep learning shows promise in disease diagnosis, the lack of large, annotated datasets remains a barrier. The authors note the progress in unsupervised and semi-supervised learning methods to tackle this issue. They stress the need for collaboration between healthcare professionals and AI experts to improve the interpretability of deep learning models. Ultimately, the review concludes that while AI has the potential to improve diagnostic accuracy and treatment strategies, challenges like data availability and ethical considerations must be addressed for successful implementation in healthcare.