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The merger of disease detection and artificial intelligence (AI) predicts a paradigm shift in modern medicine that rebalances the playing field toward a new frontier in diagnostic practices and healthcare delivery. Quite recently, within the last decade, there has been manifold augmentation in computational power paralleled by a corresponding growth in healthcare data, both forerunners to unprecedented developments in AI-driven disease detection. This chapter gives an overview of the changing landscape of AI in disease detection. It reviews innovations and upcoming trends that are likely to transform clinical practice in the future. At the heart of this metamorphosis lies the power of deep learning algorithms, AI methods inspired by the arrangement and functionality of the neocortex. Deep learning (DL) is very good at deciphering complex patterns from large datasets. Hence, deep learning can allow fast and accurate analysis of medical imaging, genomic sequences, electronic health records (EHRs), and other health data modalities. Drawing on this potential, AI empowers clinicians with improved accuracy in diagnosis, thus allowing for very early detection of various diseases, such as cancer and cardiovascular disorders, or even neurological conditions. However, introducing AI into disease detection does not come easy. Probably, most of the concerns are focused on the intrinsic opacity of DL models, often referred to as the “black box” problem. Compared to the traditional diagnosis methods –where a clinician explains the reasoning behind the decisions –the AI algorithm is an esoteric mathematical construct, and its decision processes simply cannot be fathomed by human beings. Thus, incorporating AI-driven diagnostics will call for a massive paradigm shift in how clinicians view algorithmic recommendations and build trust in them. Moreover, the training of AI models on such large datasets further raises ethical, privacy, and security concerns. The aggregation of such sensitive patient data is connected with enormous challenges regarding privacy and protection against bias in algorithms. Addressing these concerns will be critical in enabling the acceptance and trust of AI-driven diagnostics among healthcare professionals and patients alike. It is against this backdrop that the current research sets out to explore innovations that aspire to surmount these challenges and, in a real sense, unleash the power of AI in disease detection. By integrating multimodal fusion techniques, researchers can draw from relatively disparate data sources and, hence, compose a rather detailed profile of a patient for improved diagnostic accuracy and personalization. Transfer learning has proven to be one of the very promising approaches, which can avoid all the limitations of data scarcity by effortlessly adapting pretrained deep models of learning for specific disease detection with minimal labeled data. Furthermore, on the horizon, techniques of explainable AI (XAI) bring the promise of enhanced interpretability for AI-driven diagnoses into reality; it would bridge the gap between algorithmic recommendations and clinical decision-making. XAI offers a transparent explanation of the AI-generated predictions, empowering a clinician’s understanding and trust in algorithmic outputs. This opens the possibility for collaboration between experts “acting human-like” with AI systems. Federated learning is another upcoming paradigm that can maintain privacy but collaboratively train machine learning (ML) models across decentralized healthcare ecosystems. Federated learning decentralizes data storage and model training to address concerns about data privacy and security while enabling AI models to learn from diversified patient populations. The trajectory for the responsible integration of AI in disease detection is to be identified through an authentic synthesis between these innovations and emerging trends, contributing to advancing the paradigm of precision medicine, and boosting healthcare outcomes for patients worldwide.