Introduction to AI in Disease Detection — An Overview of the Use of AI in Detecting Diseases, Including the Benefits and Limitations of the Technology

Abstract

The convergence of artificial intelligence (AI) and disease detection signals a sea change in modern medicine. This chapter contributes to this overview by detailing the role of AI in revolutionizing disease diagnosis, focusing on innovations and imminent trends reshaping diagnostic practices. The chapter adds to the debate regarding the pros and cons of AI in disease detection through a critical review, underlining its transformative potential via deep learning algorithms that make the processing of vast amounts of medical data possible at speeds and precisions that were unimaginable before. Out of these developments, several challenges are emerging, such as the “black box” characteristics of AI models and data privacy and bias issues. It is in this regard that this study brings to the table cutting-edge innovations in state-of-the-art AI-driven diagnosis techniques: multimodal fusion that assimilates a wide variety of data sources, transfer learning for efficient adaptation of models, explainable AI methods that enhance interpretability and privacy-preserving federated learning for diagnostics. Innovation areas under investigation offer great hope for improved diagnostic accuracy, personalization of patient care, and mitigated healthcare disparities. By embracing the advancements and facilitating interdisciplinary collaboration, healthcare stakeholders can allow AI to have the full potential for disruptive change in disease detection toward improving patient outcomes and furthering the paradigm of precision medicine.

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