Artificial Intelligence Technologies for Biomedicine and Healthcare Applications, 2nd Edition
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 May 2025 | Viewed by 1420
Special Issue Editors
Interests: bioinformatics; biosignal processing; decision support systems; activity recognition; telemedical technologies; digital image processing; technologies for assisted living; mHealth
Interests: signal transduction; mechanobiology; tumorigenesis
Special Issues, Collections and Topics in MDPI journals
Interests: AI-enabled algorithms for the optimization of communication networks; cognitive networks; intelligent transport systems; highly automated/autonomous driving
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This focus of this Special Issue on artificial intelligence (AI) technologies for biomedicine and healthcare applications is on cutting-edge AI methodologies and their transformative impact on medical and healthcare systems. Below are several potential areas of focus:
- AI in Medical Imaging and Diagnostics
- Deep learning for imspaging: the utilization of convolutional neural networks (CNNs) and generative adversarial networks (GANs) for medical imaging analysis, including radiology, pathology, and dermatology.
- Automated diagnostics: AI-driven tools for detecting and diagnosing diseases, such as cancer, heart disease, and neurological disorders, from medical images.
- Explainability in AI diagnostics: Enhancing transparency and trust in AI algorithms used for critical healthcare decision making.
- AI in Precision Medicine and Personalized Healthcare
- Genomic data analysis: AI applications in analyzing genomic data for drug discovery, disease predisposition, and personalized treatment plans.
- Predictive analytics for patient outcomes: machine learning models that predict patient responses to treatments based on electronic health records (EHRs) and other data.
- AI-driven drug discovery: leveraging AI for accelerating the discovery of new drugs and personalized therapeutic interventions.
- Natural Language Processing (NLP) in Healthcare
- Clinical documentation automation: AI-driven NLP for processing unstructured clinical notes and improving documentation efficiency in EHR systems.
- Speech recognition for medical transcription: AI systems to facilitate accurate real-time transcription for physicians and clinical personnel.
- AI-powered chatbots and virtual health assistants: Using NLP to provide patient support, answer medical queries, and assist in telemedicine.
- AI in Digital Health and Telemedicine
- Remote patient monitoring: AI-based platforms for analyzing data from wearable sensors, enabling continuous health monitoring and the early detection of health issues.
- AI in telemedicine triage: AI-driven systems to guide patient triage and improve the quality of remote healthcare services.
- Health behavior analytics: AI tools for monitoring patient behavior and promoting wellness using digital health apps and interventions.
- AI for Healthcare Management and Operations
- Predictive modeling for hospital resource management: AI algorithms to optimize hospital operations such as patient flow, bed management, and staffing.
- AI in scheduling and logistics: tools for streamlining appointment scheduling, surgical planning, and supply chain management.
- Fraud detection in healthcare billing: AI-driven fraud detection systems to prevent billing errors, overcharges, and insurance fraud.
- AI and Robotics in Surgery and Treatment Delivery
- Robotic-assisted surgery: AI advancements in improving precision and outcomes in minimally invasive and robotic surgery.
- AI for radiation therapy planning: AI systems for optimizing radiation dose delivery and treatment planning in cancer care.
- Autonomous robotic healthcare systems: the development of fully or semi-autonomous robots for tasks such as drug dispensing, physical therapy, or patient monitoring.
- Ethics, Trust, and Fairness in AI for Healthcare
- AI bias and fairness in healthcare applications: addressing issues related to biased algorithms and ensuring equitable access to AI-driven healthcare services.
- Data privacy and security: ensuring robust data governance and compliance with privacy regulations such as GDPR and HIPAA in AI healthcare systems.
- Trustworthiness and transparency: research on building trust with healthcare providers and patients, ensuring AI systems provide transparent, explainable decisions.
- Emerging AI Trends in Healthcare
- Federated learning for healthcare: leveraging decentralized AI models to protect patient privacy while learning from distributed healthcare data.
- AI in epidemiology and public health: using AI models to predict disease outbreaks, model the spread of infectious diseases, and optimize vaccination strategies.
- AI and wearable technology integration: combining AI with wearable devices to monitor health in real time and deliver tailored health interventions.
This Special Issue will highlight groundbreaking work in these areas, showcasing how AI is revolutionizing every aspect of healthcare, including the process from diagnostics to treatment, administration, and public health interventions.
Dr. Athanasios Anastasiou
Dr. Antonios N. Gargalionis
Dr. George Dimitrakopoulos
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence (AI)
- machine learning (ML)
- deep learning (DL)
- biomedicine healthcare technology
- medical AI
- health informatics
- medical imaging AI
- precision healthcare clinical NLP
- AI for drug discovery
- telemedicine AI
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