Artificial intelligence in disease diagnostics: a comprehensive narrative review of current advances, applications,...

Artificial intelligence (AI) is revolutionizing healthcare, particularly in disease diagnostics, by improving accuracy, efficiency, and personalization. Its applications span medical imaging, pathology, and personalized medicine, significantly enhancing patient outcomes. However, challenges such as ethical dilemmas, data privacy concerns, and algorithmic biases hinder its full integration into clinical practice. A critical gap in the literature is the lack of comprehensive frameworks for addressing these challenges, particularly in low-resource settings.

We aim to explore the current advancements, applications, and challenges of AI in disease diagnostics, emphasizing its transformative impact on healthcare systems.

A narrative review was conducted to explore the role of AI in disease diagnostics and healthcare.

AI has shown remarkable success in various domains such as medical imaging, pathology, and personalized medicine. Key technologies include machine learning, deep learning, and natural language processing, which have improved diagnostic accuracy and efficiency. Applications such as cancer detection, drug development, and wearable health monitoring devices have demonstrated a significant impact. However, challenges persist, including ethical dilemmas, algorithmic bias, regulatory gaps, and data security concerns. Innovative solutions like interdisciplinary collaboration, synthetic data generation, and robust legal frameworks are recommended to address these issues.

AI's integration into disease diagnostics has the potential to revolutionize healthcare by improving outcomes and efficiency. Nonetheless, overcoming ethical, technical, and societal challenges is critical for realizing its full potential. Continued advancements in AI, combined with responsible implementation, can transform healthcare systems and pave the way for more equitable and effective medical practices.