Page 79 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

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Item Details
Testbeds or Pilot Deployments Minimum Viable Product (MVP) in initial discussions/devel-
opment with features around 4.1-Healthcare
1 Real-Time Health Data Collection (Controlled Environ-
ment)
2 Remote Patient Monitoring & Shadow Mode Streaming
3 AI Diagnostics & Decision Support
Autonomous Triage System (Shadow Mode)
Code repositories Not Available

2 Use Case Description

2�1 Description

This initiative introduces an AI-driven healthcare model to tackle the persistent healthcare
disparities in remote and underserved areas. These disparities typically arise from inferior
infrastructure, a shortage of clinicians, and inadequate connectivity. Traditional healthcare
systems often falter in such environments, leading to delayed treatment and an increase in
mortalities. Coupled with ultra-reliable, low-latency communications, this new solution aims to
bridge the critical care gap. By leveraging mobile health units and wearable/remote sensors,
the framework is designed to enable real-time diagnosis, real-time monitoring, and remote
consultations. This approach effectively transforms healthcare delivery in resource-limited
environments. The framework's core aims are clear: to significantly improve healthcare
access and the quality of care in underserved communities, to encourage early detection
and intervention for both chronic and acute conditions, and to alleviate the burden on central
hospitals and emergency services. This innovative solution integrates an AI-supported health
advisor for diagnosis and decision support, utilises intelligent kits and wearables for remote
patient monitoring, and employs context-aware personalisation based on environmental and
patient data. Furthermore, it establishes real-time communication channels between patients,
local healthcare workers, and urban specialists, ensuring timely and expert medical advice.
The use of digital twin technology, creating dynamic virtual patient representations from live
data, coupled with AI models, allows for continuous monitoring, predictive risk assessment,
and enhanced clinical decision-making. Deployment will follow a phased strategy, beginning
with onsite validation in controlled clinic environments to test sensor integration and AI model
performance, progressing to hybrid deployment with remote monitoring supported by nearby
clinics, and culminating in full remote deployment with edge AI for autonomous triage. The
selection of wearables is modular and tailored to specific conditions, including general health
monitoring, maternal and infant care, chronic disease management, and neurological or elderly
care. Adaptive AI model training will incorporate techniques like transfer learning, federated
learning, and modular design to ensure efficiency and privacy, while model explainability
mechanisms such as feature attribution, visual heatmaps, and confidence scores will be crucial
for building clinician trust and ensuring transparent decision-making. The user interface will
be multimodal and context-aware, featuring chat-based AI assistants, mobile dashboards
for clinicians, and support for various data inputs, including text, voice, image, video, and
sensor streams. The anticipated impact of this AI-powered framework is substantial, promising

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