CAI 2025 seeks original, high-quality technical papers, presentations, and proposals describing the research and results that contribute to advancements in the following AI applications and verticals. We also welcome paper submission(s) that are not confined to the topics mentioned in the vertical tracks listed below.
Explore cutting-edge AI applications revolutionizing healthcare, from personalized medicine to medical imaging analysis.
The “Healthcare/Life Sciences” vertical at CAI highlights AI’s transformative role in healthcare. Key areas include personalized medicine, predictive analytics, medical imaging, and AI-driven drug discovery. Featuring cutting-edge research, tools, and real-world applications, it will address ethical considerations, regulatory compliance, and the role of explainable AI (XAI) in fostering transparency and trust. Join experts to explore AI’s potential in shaping patient-centric, data-driven healthcare solutions.
Challenges for AI in healthcare/life sciences, but not limited to:
- Personalized Healthcare and Precision Medicine: Developing AI models for individualized treatment plans and genomic analysis, while addressing ethical concerns and managing big data challenges.
- Medical Imaging Informatics: Leveraging AI for early disease detection, predictive analytics, and enhancing medical image analysis for accurate diagnostics, while managing large-scale data (number, image and text) and addressing fairness and bias issues.
- AI in Drug Discovery and Clinical Trials: Accelerating drug discovery and optimizing clinical trial processes using AI, while addressing ethical concerns and big data integration challenges.
- Clinical Decision Support Systems and Explainable AI (xAI): Designing AI tools for clinical decision-making that are transparent and interpretable, fostering trust, fairness, and accountability.
- Data Privacy, Health Equity, and Remote Monitoring: Addressing data privacy, security, and regulatory compliance while using AI to reduce healthcare disparities, ensure equitable access, and support remote monitoring and telemedicine solutions, such as Tele-Health.
The Transportation and Aerospace Intelligence vertical covers AI technologies to create intelligent, efficient, and secure systems across ground and air domains. In transportation, key applications include predictive maintenance using sensor data, traffic management through real-time analysis, and safety monitoring systems, while aerospace applications extend to flight optimization through weather pattern analysis and aircraft performance data. The technology enables route and air traffic optimization, customer service automation, and monitoring systems while revolutionizing supply chain management through autonomous vehicles and drones. In aerospace operations, AI powers quality control through vision systems, advanced pilot training simulators, and aircraft design applications that simulate optimal configurations. These innovations enhance decision-making, improve system performance, and lead to increased safety, fuel efficiency, and operational cost reduction across both transportation and aerospace sectors.
Transportation and Aerospace, encompassing the operation and management of ground and air mobility systems, are established disciplines that have been transformed by Artificial Intelligence (AI). Though AI has existed for decades, its full potential in transportation and aerospace domains was not fully exploited until recently due to limitations in processing power, sensor technology, and data analytics capabilities. With technological advances (e.g., IoT sensors, cloud computing, and Big Data), the situation has entirely changed in recent years in favor of AI and its applications across mobility sectors. For instance, major corporations such as Boeing, Airbus, Tesla, and Toyota utilize AI techniques for predictive maintenance, autonomous systems development, route optimization, and real-time safety monitoring. Transportation applications include traffic management through real-time analysis and supply chain optimization, while aerospace implementations extend to flight optimization, aircraft design simulation, and advanced pilot training. Despite various developments, the application of AI technologies in transportation and aerospace has not been fully exploited, which leaves ample room for further improvement concerning system reliability, safety assurance, operational efficiency, environmental impact, and regulatory compliance. This vertical will focus on AI-enabled transportation and aerospace systems to address research challenges as well as practical applications and development trends.
Key challenges in transportation and aerospace AI implementation include but not limited to data security, environmental adaptability, regulatory compliance, AI decision transparency, edge case management, system integration, and ethical frameworks, particularly in autonomous navigation, flight controls, and predictive maintenance systems.
Transportation (Autonomous Vehicles):
Aerospace:
Cross-domain Challenges:
This vertical explores cutting-edge, latest AI/ML/LLMs services and applications for engineering and manufacturing, from predictive maintenance to smart factory automation.
This vertical will explore how advancements in AI are transforming manufacturing processes, from automating complex tasks to optimizing supply chains and enhancing process and factory efficiency.
We seek contributions that highlight the integration of AI into manufacturing systems and environments, with a focus on autonomous systems, AI/ML services for engineering/manufacturing, collaborative systems, lifecycle engineering, robots, and smart factories. Join us to engage in meaningful discussions on cutting-edge developments and to shape the future of AI-powered manufacturing.
We welcome original contributions addressing (but not limited to) the following topics:
The convergence of Artificial Intelligence(AI) and sustainability is forging new paths toward a greener future. Sustainable AI focuses on reducing the environmental impact of AI itself while harnessing its power to drive clean energy innovation, optimize resource use, and bolster climate resilience. AI has the potential to offer groundbreaking sustainability solutions, but it is crucial to minimize the immediate environmental impacts of its computing requirements, including energy consumption, resource utilization, and carbon footprint.
From smart grids and energy markets to sustainable transportation and smart buildings, AI could help mitigate the effects of climate change. By advancing materials science, optimizing supply chains, enhancing ESG reporting, and revolutionizing agricultural practices, AI helps create sustainable solutions across various industries. Additionally, AI plays a vital role in biodiversity conservation and ecosystem management, offering new tools for monitoring and protecting our natural world. This vertical also explores how AI can be applied to nature-based solutions, carbon dioxide removal, climate modeling, and prediction to build a more resilient world.
The vertical of Business Intelligence aims to convey emerging AI4B(usiness) technologies and applications, which are helpful in creating intelligent, profitable, efficient, reliable, secure and sustainable businesses. These include, among others, AI-powered business processes and applications, business data storage mechanisms, business data processing and visualisation tools, price and cost management applications, and QoS management systems, leading to improved decision making processes, and developing new insights and recommendations that that can be used to identify new opportunities, improve productivity, efficiency, reliability and performance of E-business.
E-business, encompassing the processing, operation and management of business activities through the Internet, is a much younger discipline than Artificial Intelligence (AI). Though AI has existed for a number of decades, its full potentials were not fully exploited in E-business (and other domains) until recently due to a lack of high processing compute, network and data storage and processing resources. With technological advances (e.g., Internet, cloud, IoT and Big Data) the situation has entirely changed in recent years in favour of AI and its applications in various domains. For instance, large corporations such as Amazon, Alibaba, and Rakuten utilise AI techniques for mining user’s reviews, for online interaction through chatbots and recommendation engines, and for processing and analysing big data. Despite various developments, the application of AI technologies in E-business has not been fully exploited, which leaves ample room for further improvement concerning data processing and analysis, security, privacy, accuracy, fairness, and so on. This vertical will focus on AI-enabled E-business in order to address research challenges as well as practical applications and development trends.
To make sure that AI has a positive impact, it is imperative to design it, develop it, and use it in a way that support human values, enhances human experience and trust in the technology, and augments human decision making. The human-centered AI vertical welcomes submissions on any topic related to this theme.
Whether AI operates autonomously or in collaboration with humans, ethical considerations must underpin its development and deployment. Human-Centered AI, which prioritizes human-AI interaction and collaboration, is instrumental in shaping positive user experiences. Key challenges in AI development include ensuring responsibility, reliability, explainability, trustworthiness, fairness, accessibility, diversity, and inclusivity. Augmenting human intelligence through modalities like natural language and conversational interfaces offers significant potential, but also presents complex challenges. To realize the benefits of such human-AI interactions, the design and operation of AI systems must incorporate strategies, methods, tools, and processes that support positive user experiences.
Robotics and Unmanned Aerial Vehicles (UAVs) are at the forefront of transforming ecosystems such as smart manufacturing, healthcare, precision agriculture, logistics, and smart cities. These systems rely on advanced AI-driven perception, intelligent decision-making, and effective interaction with humans, other robots, and their environments. However, creating robust, efficient, and adaptable robotic and UAV systems requires addressing challenges across system design, data processing, behavior learning, control, autonomy, and interaction paradigms.
The Robotics and UAVs vertical at IEEE CAI focuses on the latest AI advancements driving the evolution of robotics and UAVs. It provides a platform for showcasing cutting-edge research and industry contributions that expand the capabilities of these systems, enabling them to perceive, make decisions, interact, and impact the world in transformative ways. We aim to cover diverse themes, including the development of novel robotic/UAV systems, intelligent algorithms, interface technologies, and computational methods, alongside studies on how these components integrate to achieve complex system-level behavior. Particular emphasis is placed on how AI advances the functionality, autonomy, and usability of robots and UAVs in real-world applications.
Important Dates
Please see the conference’s important dates: https://cai.ieee.org/2025/authors/
Author’s Information
Authors are invited to submit research and industry contributions. All submissions must adhere to the conference’s submission instructions. Papers satisfying length and formatting requirements will be reviewed based on relevance, originality, significance, clarity, and soundness. Submissions are expected to meet the high standards of publication expected by IEEE CAI.
Papers submitted to this vertical may not be submitted in other venues during the IEEE CAI 2025 review period, nor may they be under review, accepted, or published in other venues.
Please refer to the conference’s pages for detailed author’s information: https://cai.ieee.org/2025/call-for-papers/
Submission Instructions
Submissions must be made via EasyChair. Submitted papers must be anonymized for double-blind review, must adhere to the page limits for the relevant submission type (research, abstract, or industry), and must follow the provided author template for formatting. Details are available here: https://cai.ieee.org/2025/paper-submission-and-guidelines/
Program Committee
TBD
We invite high-quality submissions, including research papers, abstracts, and industry papers, that address the following topics:
– Localization, Mapping, and Autonomous Navigation
– Behavior Learning and Intelligent Control for Robots/UAVs
– Cognitive Robotics and AI-Driven Autonomy
– Machine Learning and Optimization Techniques for Robots/UAVs
– LLMs for Robots/UAVs
– Robotic Manipulation and Adaptive Interaction
– Motion and Path Planning
– Task Planning, Scheduling, and Automation
– Human-Robot and Human-UAV Interaction
– Multi-Agent and Multi-Robot Systems
– Multimodal Perception, Sensor Fusion, and Situational Awareness
– Novel Applications of Robots and UAVs in Industry and Society
– Case Studies on AI-Driven Robotics and UAV Solutions
As systems grow to become more interconnected and autonomous, the need for secure and safe operational frameworks is critical for preserving functionality, resilience, and trust in diverse applications. The Security and Safety vertical invites submissions addressing the latest advancements, challenges, and innovations in ensuring robust security and safety measures for the rapidly expanding autonomous applications. This vertical’s focus is on practical and theoretical advancements that will provide solutions for real-world challenges in different applications such as Critical Infrastructure, Healthcare systems, Autonomous and Intelligent Systems, Financial and Economic systems etc. Researchers, practitioners, and industry experts are encouraged to contribute original research, technical solutions, and case studies that enhance understanding and implementation of security and safety in critical domains.
This vertical includes, but is not limited to, the following areas:
Security
Safety
Cross-Domain Perspectives
Emerging Challenges
Develop and optimize foundational AI infrastructures to support scalable, efficient, and robust AI systems. Explore innovations in computing architectures, cloud and edge deployments, and resource management to enable seamless AI integration across industries.
The AI Infrastructure vertical focuses on the foundational systems and architectures enabling scalable, efficient, and robust AI technologies. This includes hardware innovations, energy-efficient designs, cloud-edge integrations, and systems to support large-scale data processing and foundational models. Submissions should highlight cutting-edge research and applications in areas such as AI accelerators, datacenter optimization, secure infrastructures, and emerging paradigms like neuromorphic and quantum computing.
For more details on submission guidelines and deadlines, visit the Call for Papers page.
Focus: This vertical emphasizes hardware and foundational infrastructure innovations that enable scalable, efficient, and robust AI systems. Each challenge area highlights how infrastructure advancements can address key limitations and accelerate progress across AI technologies.
Performance and Scalability:
Innovations in hardware architectures, such as AI accelerators and memory systems, are essential to managing the growing computational demands of advanced AI models, including foundational and neural network models.
Energy Efficiency:
Designing energy efficient low cost systems for datacenters and edge devices, including efficient power delivery networks, energy-optimized hardware and optimal cooling solutions, is critical to minimizing the environmental and operational footprint of AI systems.
Data Management:
Infrastructure-level solutions, such as high-speed interconnects, advanced storage systems, and optimized pipelines, are necessary to support the vast data requirements of modern AI workloads.
Edge and Cloud Computing:
Hybrid architectures combining edge and cloud systems rely on robust hardware designs, such as low-latency processors and distributed compute nodes, to balance resource efficiency and real-time AI applications.
Security and Trust:
Infrastructure plays a pivotal role in securing AI systems, from hardware-level trust anchors and secure boot mechanisms to datacenter protections against physical and cyber threats.
Datacenter Optimization:
Hardware innovations in cooling, modular systems, and power distribution enhance the scalability and reliability of datacenters, ensuring they meet the demands of AI training and inference workloads.
Emerging Paradigms:
Foundational infrastructure is the backbone of next-generation AI technologies, enabling breakthroughs in neuromorphic computing, quantum systems, and photonic processors that redefine the limits of AI performance.
Advance AI testing methodologies and automation techniques to ensure reliability, efficiency, and accuracy in AI-driven systems. Focus on streamlining development pipelines, improving system validation, and enhancing performance monitoring for autonomous operations.