Speakers
Description
Point-of-care diagnostics represents a critical frontier in healthcare delivery, particularly in resource-limited settings where traditional diagnostic infrastructure is often inadequate or inaccessible. Despite significant advances in artificial intelligence and edge computing technologies, existing medical diagnostic solutions predominantly rely on single-modality analysis and cloud-based processing, creating substantial gaps in accessibility, privacy protection, and real-time decision support for healthcare providers operating without reliable internet connectivity or expensive infrastructure. This research addresses these critical limitations by developing a comprehensive multi-modal AI framework that integrates open-source large language models with computer vision capabilities, specifically optimized for deployment on low-cost Raspberry Pi platforms to enable simultaneous analysis of textual clinical data and medical imaging without internet dependency. The proposed system incorporates federated learning mechanisms to ensure privacy-preserving collaborative model improvement while maintaining local data control, advanced model compression techniques to achieve real-time inference on resource-constrained hardware, and offline training capabilities that eliminate dependency on cloud services. The outcomes include a significant reduction in diagnostic costs, improvement in diagnostic accuracy through multi-modal data fusion, a decrease in time-to-diagnosis, and the creation of an open-source framework that democratizes access to sophisticated medical AI technologies while building local technical capacity in underserved healthcare environments worldwide.
Any other info we should know?
The proposed talk directly aligns with the conference’s focus on open-source computing and benefits attendees in several key ways. Firstly, the project prioritizes open-source tools like Python libraries and pre-trained models, allowing for easy replication, customization and further development by the community. Secondly, the cost-effective and widely available Raspberry Pi platform makes the solution accessible to those with budget constraints. Furthermore, the demonstration delves into the practical implementation of integrating the minimal components of the latest algorithms within the Raspberry Pi environment. This provides a valuable roadmap and insights for attendees looking to implement similar functionalities in their own projects. By openly sharing design choices, challenges, and potential optimizations, this work fosters knowledge sharing and encourages further innovation within the open-source computer vision community. Ultimately, this project not only presents a functional point-of-care digital medical assistant but also serves as a valuable learning resource for attendees interested in leveraging open-source tools for real-world computer vision and natural language processing applications on resource-limited hardware platforms.
Session author's bio
Priyam Chakraborty is an Aerospace Engineer with B. Tech., M. Tech. and PhD from the Indian Institute of Technology Kharagpur. He committed to the use of computational tools as a lead data scientist in a startup ecosystem, followed by post-doctoral fellowship at the University of Waterloo and the Indian Institute of Science. He is currently working on smart navigation of active matter in the department of Artificial Intelligence at IIT Kharagpur. His fascination with collective intelligence, initially sparked by the efficiency of bird flocks, has turned into a belief in the power of biomimetic design that invokes automation and machine learning to unlock hidden patterns within complex datasets. By exploring, analyzing, and potentially challenging existing assumptions, Priyam aims to create affordable intelligent systems that can automatically analyze video data, extract meaningful insights about human activities, and potentially be applied in areas like security monitoring, human-computer interaction, and medical diagnostics.
Wajoud Noorani is a committed data scientist at ChangeJar, where he operates as a full-stack developer. His responsibilities span the entire data lifecycle, from extraction and processing to manipulation and pattern discovery. With a keen interest in computer vision and natural language processing, he leverages his expertise to derive actionable insights from complex datasets. His work involves employing advanced machine learning techniques to solve real-world problems and drive innovation within the company. Wajoud is passionate about exploring the frontiers of AI, particularly in how it can enhance data-driven and economical decision-making and improve various business processes.
Prof. Suman Chakraborty of IIT Kharagpur is a globally recognized expert in microfluidics and biomedical engineering, known for translating deep science into affordable healthcare solutions for underserved populations. A Sir J.C. Bose National Fellow and recipient of the 2026 TWAS Award (UNESCO), he has pioneered technologies like paper-and-pencil microfluidics and the COVIRAP molecular diagnostic platform. His work bridges fundamental fluid mechanics with low-cost medical diagnostics, including cancer screening tools and blood tests deployable in rural settings. With over 525 research publications, 25+ patents, and 50 Ph.D. graduates, his contributions span both fundamental science and impactful innovation. He leads a National CRTDH to promote indigenous medical device manufacturing and rural entrepreneurship. Prof. Chakraborty is a Shanti Swarup Bhatnagar Awardee, Infosys Prize winner, Fellow of top global academies, and a driving force in democratizing healthcare technologies by empowering local communities with science-driven solutions.
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