Saurabh Srivastava

Irfan Ahmad

Rishabha Malviya

Shivam Rajput

Mukesh Roy

The book Artificial Intelligence for Healthcare: Machine Learning and Diagnostics explores how AI and machine learning are reshaping healthcare, focusing on diagnosis, patient care, and treatment across various specialties. It covers AI applications in fields such as cardiovascular care, orthopedics, emergency medicine, ENT, and remote patient monitoring. Additionally, the book discusses AI’s transformative role in brain tumor surgery, dentistry, and elder care, showcasing its power to personalize therapies, predict outcomes, and enhance outpatient efficiency. Key chapters examine AI in pathology, specifically in image analysis and digital pathology, and its expanding influence in maternal and fetal health. This essential guide provides healthcare practitioners, researchers, clinical experts, and students with insights into AI's practical uses and future potential, serving as a valuable reference for AI-driven diagnostic advancements and healthcare solutions.

Machine Learning and Diagnostics

Artificial Intelligence for Healthcare

Balamurugan Balusamy

Rajesh Kumar Dhanaraj

Sonali Sundram

Gheorghita Ghinea

DEEP LEARNING FOR TREATMENTS The book provides the direction for future research in deep learning in terms of its role in targeted treatment, biological systems, site-specific drug delivery, risk assessment in therapy, etc. Deep Learning for Targeted Treatments describes the importance of the deep learning framework for patient care, disease imaging/detection, and health management. Since deep learning can and does play a major role in a patient’s healthcare management by controlling drug delivery to targeted tissues or organs, the main focus of the book is to leverage the various prospects of the DL framework for targeted therapy of various diseases. In terms of its industrial significance, this general-purpose automatic learning procedure is being widely implemented in pharmaceutical healthcare. Audience The book will be immensely interesting and useful to researchers and those working in the areas of clinical research, disease management, pharmaceuticals, R&D formulation, deep learning analytics, remote healthcare management, healthcare analytics, and deep learning in the healthcare industry.

Transformation in Healthcare

Deep Learning for Targeted Treatments

George Ghinea

Rishabha Malviya

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