AI and Big Data Analytics for Medical E-Diagnosis
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".
Deadline for manuscript submissions: 31 March 2025 | Viewed by 98
Special Issue Editors
Interests: data stream mining; big data; advanced analytics; bio-inspired optimization algorithms and applications; business intelligence; e-commerce; biomedical applications; wireless sensor networks
Special Issues, Collections and Topics in MDPI journals
Interests: internet of medical things; web intelligence; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: Internet of Things; big data; industry 4.0; security/risk and cloud/edge/fog computing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The field of medical diagnosis is rapidly evolving with the integration of artificial intelligence (AI) and big data analytics. These advanced methods, combined with diverse medical data—such as electronic health records, medical imaging, clinical tests, and sensor-based measurements—offer new opportunities for improving e-diagnosis. However, challenges like data noise, missing values, and class imbalances need to be addressed to ensure effective and reliable AI-driven diagnostics.
AI plays a crucial role in enhancing healthcare outcomes through accurate, timely diagnoses that support effective treatment planning and patient management. By leveraging AI and big data, advancements in early disease detection, personalized treatment, and proactive interventions are possible, potentially improving patient care, reducing costs, and saving lives.
This Special Issue will explore the challenges and opportunities in AI and big data analytics for medical e-diagnosis, with a focus on the role of sensors in providing real-time data to improve diagnostic accuracy. We invite researchers worldwide to contribute their work, aiming to address current challenges and unlock the full potential of AI-enhanced e-diagnosis. The high-quality submissions we have received underscore a strong international interest in advancing this crucial area of healthcare.
Dr. Simon Fong
Prof. Dr. Sabah Mohammed
Prof. Dr. Victor Chang
Guest Editors
Manuscript Submission Information
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Keywords
- AI-driven medical diagnostics
- big data analytics in healthcare
- sensor-based e-diagnosis
- healthcare data integration
- real-time diagnostic systems
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