Transformers and Large Language Models in Biomedical Sciences

Foundations, Methods, and Clinical Applications
Erscheinungsjahr 2026
ISBN: 978-3-032-38922-0
Verlag: Springer

Buch, Englisch, 341 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: Bio-IT and AI

Transformers and Large Language Models in Biomedical Sciences: Foundations, Methods, and Clinical Applications is a comprehensive, practice-driven textbook that links the mathematics of attention and scaling laws with real biomedical problems across molecules, omics data, and clinical narratives. It guides readers through transformer and LLM architectures, biomedical NLP foundations, key corpora and knowledge bases (PubMed, MIMIC, BLURB, MedQA), privacy-preserving data curation and de-identification, pretraining and domain adaptation strategies, and detailed comparisons of models such as BioBERT, PubMedBERT, BioGPT, MedPaLM, GPT-4-class systems, and emerging LLaMA-based medical LLMs.

The book is applicable to biomedical informatics, bioinformatics, computational biology, health data science, clinical AI, digital health, pharmaceutical sciences, and translational medicine, and supports courses in machine learning in healthcare, biomedical NLP, clinical decision support, health information technology, and medical AI governance. It can be adopted in undergraduate AI/ML and bioinformatics electives, postgraduate programs in biomedical informatics, health data science, and computer science, as well as doctoral and postdoctoral research training where students must move from theory to deployable systems. Its novelty lies in unifying rigorous mathematical foundations, domain-specific NLP, regulatory and governance frameworks (HIPAA, GDPR, FDA/EMA SaMD, EU AI Act), and hands-on deployment guidance in one coherent volume, making it directly usable for real-world projects and capstones rather than only theory.

The best part of the book is its learning ecosystem: every chapter offers clear learning objectives, exam-style questions (Objective & Subjective), case studies, and progressively challenging coding projects that use real biomedical corpora to build de-identification pipelines, clinical summarizers, retrieval-augmented question-answering systems, and drug discovery assistants—helping students and researchers move from understanding transformers and LLMs to actually implementing, testing, and responsibly using them in authentic clinical and research settings.

Zielgruppe

Research

Autoren/Hrsg.

Goswami, Siddharth

Sharma, Sachin

Rodrigues, Joel J. P. C.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie

Inhaltsverzeichnis

.- Introduction to Transformers in Biomedicine: Evolution from Attention Mechanisms to Modern Large Language Model Architectures.
.- Mathematical and Computational Foundations of Transformers: From Self-Attention to Scaling Laws.
.- Natural Language Processing Foundations for Biomedical Applications: Tokenization, Ontologies, and Clinical Narratives.
.- Biomedical Corpora and Knowledge Bases: PubMed, MIMIC-III, Clinical Trial Databases, and Domain-Specific Resources.
.- Data Curation, Annotation, and De-identification for Clinical Text: Privacy-Preserving Pipelines in Healthcare.
.- Pretraining Strategies for Biomedical LLMs: Transfer Learning, Domain Adaptation, and Computational Requirements.
.- BioBERT, BioGPT, MedPaLM, and Domain-Specific Transformers: Architecture Comparisons and Performance Benchmarks.
.- Prompt Engineering, Instruction Tuning, and Retrieval-Augmented Generation for Biomedical Tasks: Designing Effective Human-in-the-Loop Systems.
.- Large Language Models for Clinical Knowledge Mining, Drug Discovery, and Low-Resource Medical AI.
.- Trustworthy, Regulated, and Agentic Biomedical LLMs – Evaluation, Safety, Governance, and Future Directions.

Über Autor(innen)

Siddharth Goswami is a research scholar in the Department of Biotechnology at Graphic Era (Deemed to be University), Dehradun, India. His work lies at the intersection of biomedical informatics, computational biology, and artificial intelligence in healthcare, with a strong focus on developing data-driven solutions for complex biomedical challenges. He has contributed to IEEE and Scopus-indexed publications in areas such as AI-based diagnostics, molecular imaging, and computational oncology. His research interests include machine learning, deep learning, molecular modeling, and precision medicine, particularly in applications related to cancer and neurodegenerative diseases. 

Prof. (Dr.) Sachin Sharma is Professor and Head of the Amity School of Engineering and Technology at Amity University Punjab, Mohali, India, and Co-founder & Chief Technology Officer of IntelliNexus LLC, Arkansas, USA, bringing over 15 years of combined industry and academic experience. He holds both a Ph.D. and an M.S. in Engineering Science and Systems (Systems Engineering) from the University of Arkansas at Little Rock, earned with a perfect 4.0 GPA, and previously served as a Senior Systems Engineer at Belkin International in Irvine, California, which grounds his research in real-world system design and optimization. His work spans next-generation wireless and 6G communication networks, IoT and cyber-physical systems, cloud and edge computing, artificial intelligence and machine learning, quantum optimization, cybersecurity, and intelligent transportation systems, reflected in hundreds of publications and numerous patents with leading publishers and patent offices worldwide. Ranked among the world’s top 2% scientists in Stanford University’s global impact listings, he is deeply committed to mentoring emerging engineers and researchers and building global academic–industry partnerships that translate advanced technologies into impactful solutions for society.

Joel J. P. C. Rodrigues [Fellow, IEEE, IARIA & AAIS] is with the Artificial Intelligence Research Center (AIRC), Ajman University, UAE, the Federal University of Piauí, Brazil, and Leader of the Center for Intelligence at Fecomércio/CE,Brazil. Prof. Rodrigues is an Highly Cited Researcher (Clarivate), N. 1 of the top scientists in computer science in Brazil (Research.com), the leader of the Next Generation Networks and Applications (NetGNA) research group (CNPq), the Chair of the IEEE Fellow Committee at the IEEE Region 9, Member of the IEEE Fellow Evaluation Committee,Member Representative of the IEEE Communications Society on the IEEE Biometrics Council, and the President of the scientific council at ParkUrbis – Covilhã Science and Technology Park. He was Member of IEEE ComSoc Board of Governors and Director for Conference Development, an IEEE Distinguished Lecturer, Technical Activities Committee Chair of the IEEE ComSoc Latin America Region Board, a Past-Chair of the IEEE ComSoc Technical Committee (TC) on eHealth and the TC on Communications Software, a Steering Committee member of the IEEE Life Sciences Technical Community and Publications co-Chair. He is the editor-in-chief of the International Journal of E-Health and Medical Communications and editorial board member of several high-reputed journals (mainly, from IEEE). He has been general chair and TPC Chair of many international conferences, including IEEE ICC, IEEE GLOBECOM, IEEE HEALTHCOM, and IEEE LatinCom.