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Biomedical Informatics and Next-Generation Image Analysis for Precision Healthcare

Abstract

Biomedical informatics and next-generation image analysis are rapidly transforming healthcare by enabling the translation of massive biomedical datasets into actionable clinical knowledge. The convergence of artificial intelligence (AI), machine learning (ML), bioinformatics, medical imaging, and multi-omics technologies is driving a new era of precision medicine, where disease diagnosis, prognosis, and therapeutic interventions are increasingly personalized and data-driven.

Recent advances in medical imaging technologies—including MRI, CT, PET, ultrasound, digital pathology, and microscopy—generate unprecedented volumes of high-resolution data. Efficient management, analysis, and interpretation of these datasets require advanced computational approaches capable of extracting clinically meaningful information while ensuring scalability, reproducibility, and interoperability.

This special session aims to provide a multidisciplinary platform for researchers, clinicians, engineers, and data scientists to present recent advances in biomedical informatics, AI-assisted image analysis, computational biology, and intelligent healthcare systems. The session will emphasize innovative algorithms, translational research, and real-world clinical applications that improve patient care and accelerate biomedical discoveries.

Motivation

The increasing availability of biomedical big data presents significant opportunities and challenges. Modern healthcare demands computational tools capable of integrating imaging, genomic, proteomic, metabolomic, and clinical datasets into comprehensive patient-specific models.

Key motivations include:

  • Managing and analyzing large-scale biomedical and medical imaging datasets.
  • Bridging the semantic gap between low-level image features and clinically interpretable information.
  • Discovering novel imaging biomarkers for cancer, neurological disorders, cardiovascular diseases, infectious diseases, and rare diseases.
  • Integrating medical imaging with multi-omics and electronic health records to enable precision medicine.
  • Developing explainable and trustworthy AI models for clinical decision support.
  • Automating image segmentation, classification, and disease prediction through deep learning.
  • Enhancing reproducibility, standardization, interoperability, and secure biomedical data sharing.
  • Accelerating drug discovery and translational research through computational approaches.

Expected Outcomes

The proposed special session will:

  • Showcase state-of-the-art research in biomedical informatics and AI-driven healthcare.
  • Foster interdisciplinary collaboration among computer scientists, biomedical engineers, clinicians, and life scientists.
  • Promote innovative computational methodologies for disease diagnosis, prognosis, and therapeutic discovery.
  • Encourage collaborations between academia, healthcare institutions, and industry.
  • Highlight emerging trends in precision medicine, intelligent imaging, and biomedical data science.

Target Audience

The session is intended for:

  • Researchers
  • Faculty Members
  • Medical Doctors and Clinicians
  • Biomedical Engineers
  • Computer Scientists
  • AI and Machine Learning Researchers
  • Bioinformaticians
  • Data Scientists
  • Graduate and Doctoral Students
  • Healthcare Industry Professionals

Session Chair

Dr. Perugu Shyam
Associate Professor
Department of Biotechnology
National Institute of Technology Warangal, India

Email: shyamperugu@nitw.ac.in
Contact: +91 9948561761
Web: https://erp.nitw.ac.in/ext/profile/bt-shyamperugu

Dr. Perugu Shyam has extensive research experience in Bioinformatics, Artificial Intelligence, Machine Learning, Computational Drug Discovery, Biomedical Data Analytics, and Precision Medicine. He has published numerous peer-reviewed research articles, book chapters, and patents, and previously served as a Session Chair at IHCI 2022 and IHCI 2023. His research focuses on developing AI-enabled computational approaches for biomedical applications, disease prediction, biomarker discovery, and drug discovery.

Specialized Topics

Prospective authors are invited to submit original research, methodological papers, and practical case studies. Topics of interest include, but are not limited to:

Topics of Interest

  • Biomedical Informatics
  • Medical Image Computing
  • Artificial Intelligence in Healthcare
  • Deep Learning for Medical Imaging
  • Explainable AI (XAI) in Clinical Applications
  • Computational Drug Discovery
  • Bioinformatics and Computational Biology
  • Digital Pathology
  • Radiomics and Radiogenomics
  • Medical Image Segmentation and Registration
  • Computer-Aided Diagnosis (CAD)
  • Image-Based Biomarker Discovery
  • Multi-omics Data Integration
  • Precision and Personalized Medicine
  • Clinical Decision Support Systems
  • Electronic Health Records Analytics
  • Biomedical Big Data Analytics
  • Federated Learning in Healthcare
  • Healthcare Data Privacy and Security
  • AI for Cancer Diagnosis and Prognosis
  • Neuroimaging Analytics
  • Cardiovascular Imaging Informatics
  • Biomedical Signal and Image Fusion
  • Digital Twin Technologies for Healthcare
  • Intelligent Healthcare Systems
  • Drug Repurposing Using AI and Bioinformatics
  • Medical Internet of Things (IoMT)

Chairs

  • Dr. Perugu Shyam, Associate Professor, Department of Biotechnology, National Institute of Technology Warangal, Hanamkonda, Telangana, India