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AIHC 2027: International Conference on AI in Healthcare

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截稿日期:
2026-08-30
通知日期:
2026-09-25
会议日期:
2027-01-23
会议地点:
Hyderabad, Telangana, India
届数:
1
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征稿

AIHC 2027 (International Conference on AI in Healthcare) is an academic conference held in Hyderabad, Telangana, India on 2027-01-23. The paper submission deadline is 2026-08-30. Acceptance notifications are sent on 2026-09-25.

Conference Theme Artificial Intelligence and Machine Learning for Intelligent, Ethical, and Inclusive Healthcare The theme of AIHC 2027 highlights the transformative role of Artificial Intelligence and Machine Learning in shaping next-generation healthcare systems that are intelligent, reliable, and human-centric. AI and ML are revolutionizing clinical diagnostics, drug discovery, medical imaging, and personalized medicine, while IoT, edge computing, and data analytics enable real-time patient monitoring and connected healthcare services. Together, these technologies empower physicians and healthcare providers with predictive insights, facilitate efficient decision-making, and lead to improved patient outcomes. By fostering interdisciplinary research and collaboration between academia, healthcare institutions, and industry, the conference envisions an ecosystem where technology enhances accessibility, transparency, and equity in healthcare delivery. Major Achievements / Areas to be Covered AIHC 2027 will provide a platform for researchers, practitioners, and industry professionals to explore the latest advancements in AI and Machine Learning applied to healthcare. The conference will cover, but is not limited to, the following areas: Deep Learning Models and Architectures for Healthcare – Advanced neural networks, model optimization, and architectures tailored for medical data. Generative AI and Large Language Models in Medicine – AI-driven synthesis of medical data, clinical text generation, and decision support using LLMs. Computer Vision and Medical Image Analysis – Image classification, object detection, segmentation, and radiology/medical imaging applications. Natural Language Processing and Clinical Text Mining – Analysis of electronic health records (EHR), medical notes, and conversational AI in healthcare. Edge AI and Embedded Intelligence in Healthcare Devices – Low-latency AI for wearable devices, mobile health (mHealth) applications, and IoT-based patient monitoring. AI for Healthcare and Biomedical Systems – Predictive analytics, diagnostics, drug response prediction, and personalized treatment systems. AI for Communication and Medical Data Networks – Intelligent networking, semantic data processing, and secure data exchange in healthcare systems. Smart Sensing, Signal Processing, and IoT Integration – Body sensor networks, wearable devices, ambient assisted living, and contextual reasoning in healthcare IoT. AI for Robotics, Automation, and Human-Robot Interaction in Medicine – Robotic surgery, AI-assisted rehabilitation, and collaborative healthcare automation. Ethical, Fair, and Explainable AI in Healthcare – Explainable AI (XAI), bias mitigation, privacy-preserving ML, and responsible AI deployment in clinical settings.
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