HealthSage AI
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About
HealthSage AI is a digital health company using Generative AI to transform unstructured medical data into actionable insights. Their flagship product, Note-to-FHIR, converts unstructured data into FHIR, SNOMED, and ICD-10 outputs, improving clinical workflows, financial operations, and research data. The HealthSage AI Platform is a generative AI platform dedicated to healthcare, offering a range of AI models, tools, and applications. The company is committed to evidence-based practice and ethical standards, operating from Utrecht, Netherlands, and Paris, France. They are currently hiring for various roles including Clinical Scientist, Clinical Data Analyst, and ML Engineer.
Platform
Features
• simplifies complex data management
• transforms clinician notes into icd-10 output
• seamless integration with healthcare information systems
• boosts efficiency
• uncovers new insights
• enriches existing data sets with information from unstructured clinical notes
• automates the encoding of clinical assessments
Job Opportunities
Clinical Scientist & Regulatory Specialist
HealthSage AI uses AI to transform unstructured medical data into actionable insights, streamlining workflows and improving financial operations and research. Their platform offers various AI models and applications for healthcare.
Education Requirements:
MD and/or PhD degree in biomedical sciences, or a related discipline, with at least 3-5 years of working experience
Master’s degree with demonstrable experience consistent with the position responsibilities
Experience Requirements:
Proven experience in running or supporting clinical investigations
Prior industry experience in a customer-facing role is desired, but not required
Hands-on experience with ISO 13485 and Software as a Medical Device regulations
Other Requirements:
Exceptional interpersonal and communication skills
Demonstrated ability to work both independently and collaboratively as part of a team
Ability to take a hands-on approach to problem-solving and project management/execution
Must have the ability to travel to hospitals, healthcare IT partners, and attend medical congresses
Responsibilities:
Engage and collaborate with clinical experts
Maintain and develop a network of clinical Key Opinion Leaders (KOL’s)
Provide feedback and input on the roadmap towards the long-term clinical strategy
Write research proposals, publish studies with our customers, and apply for grants
Act as the clinical voice towards our engineering team. As a Clinical Product Owner you'll help define and prioritise the backlog.
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Clinical Data Analyst
HealthSage AI uses AI to transform unstructured medical data into actionable insights, streamlining workflows and improving financial operations and research. Their platform offers various AI models and applications for healthcare.
Benefits:
Work where and when you want
Bruto hourly wage fitting your experience
We are always available for your questions
Education Requirements:
At least third-year medical student
Experience Requirements:
Affinit y for the technical aspect of medicine
Other Requirements:
Precise
Let us know if you encounter problems
Responsibilities:
Help develop our Large Language Model (LLM) by annotating medical data
Indicate the content of each part of a piece of medical text
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ML Engineer
HealthSage AI uses AI to transform unstructured medical data into actionable insights, streamlining workflows and improving financial operations and research. Their platform offers various AI models and applications for healthcare.
Education Requirements:
Master’s (with 3-5 years of experience) or Ph.D. degree in Computer Science, Engineering, Statistics, Applied Mathematics, or a related field with a focus on machine learning, artificial intelligence or related field
Experience Requirements:
Previous experience working with LLM is highly desired
Other Requirements:
Strong programming skills in languages such as Python, C++, and proficiency in relevant libraries and frameworks for machine learning (PyTorch)
Excellent problem-solving skills
Strong communication skills
Responsibilities:
Collect, clean, and preprocess data
Keep up with the state of the art, develop and train machine learning models
Optimize model performance
Integrate machine learning models into the product infrastructure and deploy them to production environments
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