AI for Detecting Rare Diseases: Utilizing Small Datasets in Medical Diagnostics

Abstract

The rise of artificial intelligence (AI) in healthcare has paved the way for innovative approaches to diagnosing rare diseases, which often suffer from the challenge of limited datasets. This paper presents a comprehensive study on leveraging AI techniques, particularly machine learning algorithms, to enhance the detection of rare diseases using small datasets. We explore various methodologies, including transfer learning, data augmentation, and synthetic data generation, to address the inherent limitations posed by the scarcity of clinical data. Our experiments demonstrate the effectiveness of these techniques in improving diagnostic accuracy and model robustness, thus contributing to the early detection and treatment of rare conditions. Additionally, we discuss the ethical implications and practical challenges of implementing AI solutions in clinical settings, emphasizing the importance of interdisciplinary collaboration between data scientists and healthcare professionals. The findings highlight the potential of AI not only to transform rare disease diagnostics but also to inspire future research in the domain of medical AI, ultimately improving patient outcomes and advancing personalized medicine.