Custom AI Model Development in Healthcare
Budget: $750 – $1,500 USD
We are seeking a skilled and forward-thinking developer (or development team) to partner with us in building an innovative, AI-driven healthcare platform. The system will streamline the collection of patient-reported symptoms and support physicians with intelligent clinical decision-making tools.
Project Overview
The goal of this project is to develop a scalable platform that enables:
Structured symptom intake and normalization
Expert annotation by physicians
AI-powered predictions for diagnosis and treatment recommendations (future phase)
The project will be executed in two distinct phases:
Phase 1: Symptom Collection & Annotation System (Core Infrastructure)
This phase focuses on building a solid foundation for clinical data collection and annotation. Key deliverables include:
A web-based or chat-based interface for patients to submit symptoms (via forms or conversational input)
Validation and storage of collected data in a structured database (SQL or NoSQL)
Symptom normalization and mapping using established ontologies (e.g., mapping "high temperature" to "fever")
A secure, intuitive doctor-facing interface to manually annotate patient data with:
Provisional diagnoses
Recommended investigations
Treatment plans
Structured data export and storage ready for downstream AI/ML model development
Phase 2: AI-Based Clinical Decision Support (Optional/Extended Phase)
In this phase, the objective is to develop machine learning models that can:
Predict likely diagnoses from reported symptoms
Recommend relevant investigations and treatment plans
This will be achieved through supervised learning from the annotated dataset created in Phase 1. The system is expected to assist, not replace, medical professionals — serving as a clinical decision support tool.
Required Expertise
We are looking for developers with a proven track record and genuine interest in AI applications in healthcare. Candidates with relevant experience in clinical or biomedical informatics are especially encouraged to apply.
Core Technical Skills:
Backend Development: Python (Django, FastAPI) or Node.js
Database Design: Relational (PostgreSQL, MySQL) or NoSQL (MongoDB, Firebase, etc.)
Frontend Development: React, Vue.js, or other modern frameworks
API Development: RESTful or GraphQL APIs as needed
Preferred/Bonus Skills (Phase 2):
Machine Learning: Experience with frameworks like Scikit-learn, TensorFlow, or PyTorch
Natural Language Processing (NLP): For understanding user input via chat
Healthcare Data Standards: Familiarity with SNOMED CT, ICD-10, or HL7 FHIR
Project Budget & Timeline
Phase 1: System Development
Budget: $350–500 USD
Timeline: 2–4 weeks
Phase 2: AI Model Integration
Budget: $700–1000 USD
Timeline: To be determined based on data readiness
Note: Phase 2 will be initiated only after successful completion of Phase 1.
Project Overview
The goal of this project is to develop a scalable platform that enables:
Structured symptom intake and normalization
Expert annotation by physicians
AI-powered predictions for diagnosis and treatment recommendations (future phase)
The project will be executed in two distinct phases:
Phase 1: Symptom Collection & Annotation System (Core Infrastructure)
This phase focuses on building a solid foundation for clinical data collection and annotation. Key deliverables include:
A web-based or chat-based interface for patients to submit symptoms (via forms or conversational input)
Validation and storage of collected data in a structured database (SQL or NoSQL)
Symptom normalization and mapping using established ontologies (e.g., mapping "high temperature" to "fever")
A secure, intuitive doctor-facing interface to manually annotate patient data with:
Provisional diagnoses
Recommended investigations
Treatment plans
Structured data export and storage ready for downstream AI/ML model development
Phase 2: AI-Based Clinical Decision Support (Optional/Extended Phase)
In this phase, the objective is to develop machine learning models that can:
Predict likely diagnoses from reported symptoms
Recommend relevant investigations and treatment plans
This will be achieved through supervised learning from the annotated dataset created in Phase 1. The system is expected to assist, not replace, medical professionals — serving as a clinical decision support tool.
Required Expertise
We are looking for developers with a proven track record and genuine interest in AI applications in healthcare. Candidates with relevant experience in clinical or biomedical informatics are especially encouraged to apply.
Core Technical Skills:
Backend Development: Python (Django, FastAPI) or Node.js
Database Design: Relational (PostgreSQL, MySQL) or NoSQL (MongoDB, Firebase, etc.)
Frontend Development: React, Vue.js, or other modern frameworks
API Development: RESTful or GraphQL APIs as needed
Preferred/Bonus Skills (Phase 2):
Machine Learning: Experience with frameworks like Scikit-learn, TensorFlow, or PyTorch
Natural Language Processing (NLP): For understanding user input via chat
Healthcare Data Standards: Familiarity with SNOMED CT, ICD-10, or HL7 FHIR
Project Budget & Timeline
Phase 1: System Development
Budget: $350–500 USD
Timeline: 2–4 weeks
Phase 2: AI Model Integration
Budget: $700–1000 USD
Timeline: To be determined based on data readiness
Note: Phase 2 will be initiated only after successful completion of Phase 1.