Fundamentals of Cost-Efficient AI: In Healthcare and Biomedicine

Author:   Rohit Kumar (HCLTech, Noida, Uttar Pradesh, India; IIT Delhi, New Delhi, India; Shiv Nadar University, Chennai, Tamil Nadu, India; 500 Startups, San Francisco, CA, United States)
Publisher:   Elsevier Science Publishing Co Inc
ISBN:  

9780443333620


Pages:   434
Publication Date:   09 December 2025
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Fundamentals of Cost-Efficient AI: In Healthcare and Biomedicine


Overview

Fundamentals of Cost-Efficient AI: In Healthcare and Biomedicine provides a comprehensive yet accessible introduction to the principles of designing, training, and deploying efficient artificial intelligence systems. It explains the theory behind cost-aware machine learning and data mining and examines methods across deep learning, graph neural networks (GNNs), transformer architectures, diffusion models, reinforcement learning, and knowledge distillation. The book covers fine-tuning and compression techniques such as low-rank adaptation (LoRA), parameter-efficient fine-tuning (PEFT), adapter-based tuning, pruning, and quantization. It also explores inference acceleration through Flash Attention, prefill optimization, and speculative decoding, and explains how mixture-of-experts (MoE) architectures can scale models efficiently across GPUs and edge devices. To build a strong conceptual understanding, the text introduces fundamentals of GPU architecture, matrix multiplication, memory hierarchies, and parallelization strategies, helping readers develop an intuition for optimizing training and inference pipelines. While applicable across domains, the book places special emphasis on healthcare and biomedicine, where efficient AI can reduce costs and improve diagnostics, precision medicine, and clinical decision support. Real-world case studies and interviews with experts from organizations such as Google and Microsoft provide practical insights into building scalable healthcare AI systems. Aimed at graduate students, researchers, clinicians, biomedical engineers, data scientists, and AI practitioners, this book bridges algorithmic principles with applied implementation.

Full Product Details

Author:   Rohit Kumar (HCLTech, Noida, Uttar Pradesh, India; IIT Delhi, New Delhi, India; Shiv Nadar University, Chennai, Tamil Nadu, India; 500 Startups, San Francisco, CA, United States)
Publisher:   Elsevier Science Publishing Co Inc
Imprint:   Academic Press Inc
Weight:   0.450kg
ISBN:  

9780443333620


ISBN 10:   0443333629
Pages:   434
Publication Date:   09 December 2025
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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Author Information

Rohit Kumar studied at Stanford, IIT Delhi, and RPI, specializing in machine learning. He is the Global Head of AI & Analytics at HCLTech (Digital Business), a visiting faculty at Shiv Nadar University, and a PhD scholar at IIT researching AI hallucinations. With over 20 years of product development experience in Silicon Valley, he has served as the Head of R&D at the Ministry of IT (Government of India), Senior Director at WalmartLabs, and CEO of a blockchain startup. He holds multiple patents and publications on generative AI, data mining, and large-scale distributed systems.

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