ANDROID, AI, HEALTHCARE, RESEARCH

DrugQ

Research Period: 2025.07 - Present

Current Status

100%
  1. Concept
  2. Prototype
  3. Core Build
  4. Validation
  5. Output
Platform AndroidMobile
Research mHealthHealthcareHCI
Technical Multimodal AIVoice InputImage InputPrompt Design
Output Research PrototypeManuscript in Progress

Overview

DrugQ is an Android research prototype for a multimodal OTC drug safety assistant. It supports voice and image inputs to help users make safer medication decisions, then returns an easy-to-understand safety verdict with an explanation.

Scope

  • Implemented as a working Android prototype (not only a concept).
  • Initial medication set: Tylenol, Aspirin, Benadryl, and Rolaids.
  • Primary symptom contexts: common everyday cases such as cold/flu-like symptoms and allergies.

Problem

  • OTC medication guidance is often unclear for non-experts.
  • Users may not know how to interpret warnings, contraindications, or drug interactions.
  • When users feel unwell, they need a fast, low-effort way to get a safety-oriented answer.

System Design

Inputs

  • Voice: users speak the drug name and describe symptoms. Voice is transcribed via OpenAI Whisper.
  • Image: users capture photos of the medication (package/label or pill). The image is processed using Gemini + prompt-based interpretation.

Reasoning and Decision

  • The system uses an LLM-only approach (no separate rule engine) with prompt design to produce a final safety verdict.

Outputs

  • The assistant returns one of three outcomes:
    • Do not use
    • Consult a doctor
    • Safe for use
  • Each outcome includes a short, user-facing explanation based on the study’s questionnaire-driven criteria.

What I Built / Contributed

  • Built the full Android app end-to-end using Kotlin + Jetpack Compose.
  • Implemented the multimodal pipeline (voice + image) and integrated the model calls.
  • Designed and iterated prompts to make outputs consistent with the three-category verdict system.
  • Conducted AI integration testing and refined the interaction flow based on lab feedback.

Tech Stack / Tools

  • Android (Kotlin, Jetpack Compose)
  • Firebase (including Firebase AI Logic for on-device API calls)
  • Gemini (prompt-based reasoning for image and decision)
  • OpenAI Whisper (speech-to-text)
  • Figma (flow prototyping)

Collaboration

  • Conducted as a lab study project with Prof. Eun-Kyung Choe’s team (University of Maryland).
  • I owned the full engineering scope: mobile implementation, AI integration, and prompt design.

Outcome

A working multimodal prototype that produces a clear three-level safety verdict (“Do not use / Consult a doctor / Safe for use”) with explanations. The prototype was developed and iterated within a lab-study setting.

Publications

DrugQ: Multimodal OTC Drug Safety Assistant

Target venue TBD Manuscript in progress

Prototype-focused paper planned around multimodal input, safety verdict design, and prompt-based medication guidance.