AI-Powered Prescription Digitization with ICD10 Mapping

About Project

Google partnered with EMed HealthTech for the development of an AI-based healthcare research product aimed at digitizing handwritten medical prescriptions. The project’s focus was to build an intelligent system that could recognize various handwriting styles and extract medicine brand names, strengths, and associated clinical data across different languages.

The challenge was not just recognizing text but also associating each medicine with its composition, linking those compositions with symptoms, and further mapping those symptoms using ICD10 codes for disease classification—crucial for building EMR/EHR systems and improving AI-based diagnosis models.

EMed HealthTech stepped in to provide high-quality, structured medical datasets by leveraging its internal medicine database and a team of medical coders. We delivered:

This data served as a core component in training Google’s ML models and strengthening their upcoming healthcare technology initiatives.

Key Challenges

No structured dataset linking medicines, symptoms, and diseases.

Lack of ICD10-coded data for training ML models.

Difficulty in interpreting handwritten, multilingual prescriptions.

Solution

Google collaborated with EMed HealthTech to bridge the healthcare data gap by delivering structured medical information curated for AI training. The solution focused on creating foundational datasets to power clinical intelligence tools.

Supplied drug-to-symptom dataset curated in-house

Ensured medical accuracy with expert coders

Delivered ICD10-coded reference sample for mapping

Enabled AI training for prescription digitization

ICD10 Dataset Mapping

 

 

This dataset module included detailed mappings of symptoms to ICD10 codes and linked them with relevant drug compositions. It enabled AI systems to understand not just the medicines prescribed, but also the implied symptoms and diseases—laying a path for smarter clinical decisions.

Medicine-to-Symptom Relationship Dataset

A comprehensive dataset built by analyzing various brand names, compositions, and their clinical relevance. The dataset enabled Google’s system to match medicine names to symptoms and support the extraction of structured data from scanned handwritten prescriptions.

Our Process

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Discover

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Design

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