Medical Minds and Machine Learning

Transforming Care with AI
1. Auflage 2026
ISBN: 978-1-041-21286-7
Verlag: Taylor & Francis Ltd

Buch, Englisch, 338 Seiten, Format (B × H): 156 mm x 234 mm

This volume reviews the convergence of AI and medicine. As healthcare systems face rising demands, clinician shortages, and the complexity of patient data, machine learning and deep learning are stepping in as transformative forces—to improve diagnosis, personalize treatments, and enhance patient outcomes. This book is a comprehensive guide to technologies, applications, and implications of AI in healthcare. It navigates through the foundational principles of machine learning, dives into real-world implementations such as radiology diagnostics, robotic surgery, and predictive analytics, and addresses key topics like natural language processing of EHRs and AI assisted drug discovery.

Key Features:

- Explores how generative AI is revolutionizing diagnostics in fields like radiology, oncology, and pathology, enhancing accuracy and efficiency in patient care.

- Addresses Ethical, Legal, and Regulatory Considerations.

- Bridges the Gap Between Technology and Clinical Practice.

- Highlights Future Trends and Innovations.

- Fosters Multidisciplinary Collaboration.

Zielgruppe

Academic, Postgraduate, Professional Practice & Development, Undergraduate Advanced, and Undergraduate Core

Autoren/Hrsg.

Mathivanan, Sandeep Kumar

Mallik, Saurav

Shivahare, Basu Dev

S.K.B., Sangeetha

Basheer, Shakila

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik

Inhaltsverzeichnis

Preface. 1. OptiCareNet: An Optimization-Driven AI Framework for Intelligent Clinical Decision Support. 2. Adaptive Multimodal Diagnostic Network: A Hybrid Cross-Attention Approach for Explainable Clinical AI. 3. CCRET: A Contextualized Clinical Representation Extraction Transformer for Advanced NLP in Electronic Health Records. 4. AI-Driven Early Disease Detection and Risk Stratification using Multi-Omics Data Integration. 5. AI-Enhanced Cardiac Risk Prediction and Early Diagnosis using Multi-Modal Imaging and Clinical Data. 6. PAI-MED: A Personalized AI-Driven Medicine Framework Integrating Multi-Sensor Wearables for Predictive Healthcare. 7. AI-Augmented Wearables and Remote Monitoring for Scalable Personalized Medicine: A Clinical and Economic Perspective. 8. GEN-DRUG: A Generative AI-Driven Framework for Accelerated De Novo Drug Discovery and Lead Optimization. 9. ETHIC-AID: A Framework for Ethical AI Deployment in Healthcare with Bias Mitigation, Transparency, and Accountability Controls. 10. Federated Multimodal NLP for Privacy-Preserving Symptom Monitoring and Care Guidance. 11. Auto-IntelliDx: A Self-Evolving Deep Learning Framework for Adaptive and Interpretable Clinical Diagnosis. 12. A Self-Supervised Representation Learning Framework for Scalable Edge AI Deployment in Clinical Settings. 13. AI-MedMind: A Federated Deep Learning Framework for Real-Time Glycemic Forecasting and Adaptive Insulin Dosing. 14. HEAL-Net: A Multimodal Deep Ensemble Framework for Enhanced Risk Prediction in Global Healthcare. 15. MedBotX: An AI-Integrated Conversational System for Preliminary Medical Assistance. 16. A Chatbot Service for Suicide Detection and Prevention. 17. Deep SkinNet++: A Novel Feature Enrichment Framework for Advanced Dermatological Disease Classification Using Multiscale Spatio-Spectral Embeddings. 18. AI-HyMedNet: A Hybrid Multimodal Deep Learning Network for Precision Medicine and Real-Time Disease Prediction. 19. Quantum-Enhanced AI for Genomic Medicine: Accelerating Drug Discovery and Personalized Treatment through Hybrid Quantum-Classical Models. 20. AI-Driven Digital Twin Framework for Predictive Healthcare: Integrating Real-Time EHR Data with Virtual Patient Simulations. 21. Integration of Multimodal NLP Systems for Real-Time Clinical Decision Support in Digital Healthcare. Index.

Über Autor(innen)

Dr. Sandeep Kumar Mathivanan has a M.S. in software engineering and the M.Tech. (By Research) degree from Vellore Institute of Technology, Vellore, India, in 2016 and 2020, respectively, and a Ph.D. from the School of Information Technology and Engineering, VIT, in 2023. He is currently an Assistant Professor with the School of Computing Science and Engineering, Galgotias University, Greater Noida, India. He has more than six years of research experience, and has authored several journals and conferences papers. He is a reviewer of many reputed Q1 and Q2 journals. His current research interests include machine learning, deep learning, remote sensing, and big data.

Dr. Saurav Mallik (google scholar h-index: 35, citations > 4250) is a Research Scientist in the Department of Pharmacology and Toxicology, The University of Arizona, USA. Previously, he worked as Postdoctoral Fellow in Harvard T.H. Chan School of Public Health, Boston, MA, USA for more than three years (2019–2022), the Center of Precision Health, Department of School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA for one and half year (2018–2019), and in the Division of Bio-statistics, Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, Florida, USA for more than one year (2017–2018). He obtained a Ph.D. from the Department of Computer Science and Engineering from Jadavpur University, Kolkata, India in 2017. His Ph.D. work was carried out in Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India as junior research fellow. He has obtained the award of Research Associateship from Council of Scientific and Industrial Research, MHRD, Government of India in 2017. Dr. Mallik has more than 260 research papers in different top high impact factor peer-reviewed journals, conferences and book chapters. He has published various Books and Patents. He is working as the active member of Institute of Electrical and Electronics Engineers (IEEE), USA, ACM and American Association for Cancer Research (AACR), USA and Bioclues, India. He has also worked with section editors and reviewers with several well-reputed high impact journals. His research interest includes computational biology, knowledge retrieval and data mining, bioinformatics, biostatistics, and ML/DL.

Dr. Basu Dev Shivahare is an Associate Professor in School of Computer Science & Engineering at Galgotias University, Greater Noida, India. He has a Ph.D. (Computer Science & Engineering) from Dr. APJ Abdul Kalam Technical University AKTU, Lucknow, Uttar Pradesh, India in 2023, M.Tech. (CS) from BIT MESRA, Ranchi, India in 2012 and B.Tech. (CSE) from Uttar Pradesh Technical University (UPTU), Lucknow, in 2006. He was an Assistant Professor in Department of Computer Science and Engineering, at Amity University, Uttar Pradesh, India for over 10 years. He has published more than 50 research papers in peer review SCIE/Scopus index international journals and conferences. His research area is Image Processing, Medical image analysis, metaheuristic optimization algorithms and machine learning. He is UGC-NET qualified. He has more than 19 years of teaching experience.

Dr. Sangeetha S. K. B. is an Associate Professor at the School of Computer Engineering, Manipal Institute of Technology, Bengaluru, Manipal Academy of Higher Education, India. With over 17 years of academic experience, Dr. Sangeetha has contributed significantly to fields such as data science, medical imaging, Artificial Intelligence (AI), and quantum computing for healthcare. She earned her Ph.D. in Computer Science and Engineering from Anna University, Chennai, and is currently pursuing a postdoctoral program at Lincoln University College, Malaysia.

Her research interests focus on AI, machine learning, and quantum computing, particularly in multimodal data fusion for healthcare applications. Dr. Sangeetha has authored/co-authored over 50 research articles, many of which have been published in SCI-indexed journals. Her contributions have earned her multiple accolades, including Best Paper Awards at international conferences such as IEEE and Springer, and recognition from prominent journals like PLoS ONE and Springer. Dr. Sangeetha has mentored 3 Ph.D. students, and over 100 B.Tech. students, fostering academic and professional growth. She has guided numerous students through research and industry projects, particularly in AI and data science. In addition to her teaching and research roles, Dr. Sangeetha has been a guest speaker at over 25 international conferences and workshops. She has also authored 4 books and served as a reviewer for several peer-reviewed journals. Her professional engagement extends to industry consulting, with over
5 consulting projects focused on AI and machine learning solutions.

Dr. Shakila Basheer is currently working as an Associate Professor in Department of Information Systems, College of Computer and Information Science, Princess Nourah bint Abdulrahman University in Riyadh, Saudi Arabia. She has more than 10 years of teaching experience and has published more technical papers in international journals/proceedings of international conferences/edited chapters of reputed publications. She has worked and contributed in the field of Data Mining, Image Processing and Fuzzy Logic. Her research also focuses on Data Mining algorithms using Fuzzy Logic. She is currently working on Data mining, Vehicular networks Machine Learning, Blockchain, Vehicular networks and IoT.