Offline AI Nurse Development
Budget / Salary₹12,500–37,500
TypeFreelance project
LocationRemote
Posted2 hours ago
This project centres on a fully-offline “AI Nurse” that runs identically on Android tablets/phones and on a Raspberry Pi 5 equipped with a touchscreen. The core experience must feel fast, intuitive, and clinically trustworthy.
Primary focus
• Natural voice assistance that works without cloud calls, guiding users through every step.
• Tap-to-map injury marking on an on-screen human anatomy model, with support for multiple wounds per casualty.
• Instant, dataset-driven first-aid guidance that adapts to the mapped injuries and spoken inputs.
Beyond those priorities, the application also needs to screen casualty severity, triage several patients in succession, and output safe, evidence-based pre-hospital decisions in plain language.
Key expectations
• Entirely offline operation: all ML models, triage rules, and data tables reside on-device.
• Identical codebase (or tightly aligned builds) for Android and Raspberry Pi 5.
• Touch-optimised UI; microphone and speaker integration for voice I/O.
• Design choices and algorithms must be defensible against current clinical guidelines; cite sources where applicable.
• Packaged deliverables: Android APK, Raspberry Pi image or install script, reproducible build notes, and a brief validation report.
When you reply, please show similar health-tech or embedded AI work you have already shipped; real past work carries the greatest weight in selection. I will supply the anatomy assets, first-aid datasets, and validation criteria once development begins, and we can agree milestones around each feature set.
Primary focus
• Natural voice assistance that works without cloud calls, guiding users through every step.
• Tap-to-map injury marking on an on-screen human anatomy model, with support for multiple wounds per casualty.
• Instant, dataset-driven first-aid guidance that adapts to the mapped injuries and spoken inputs.
Beyond those priorities, the application also needs to screen casualty severity, triage several patients in succession, and output safe, evidence-based pre-hospital decisions in plain language.
Key expectations
• Entirely offline operation: all ML models, triage rules, and data tables reside on-device.
• Identical codebase (or tightly aligned builds) for Android and Raspberry Pi 5.
• Touch-optimised UI; microphone and speaker integration for voice I/O.
• Design choices and algorithms must be defensible against current clinical guidelines; cite sources where applicable.
• Packaged deliverables: Android APK, Raspberry Pi image or install script, reproducible build notes, and a brief validation report.
When you reply, please show similar health-tech or embedded AI work you have already shipped; real past work carries the greatest weight in selection. I will supply the anatomy assets, first-aid datasets, and validation criteria once development begins, and we can agree milestones around each feature set.
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