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Blogs

Insight at the intersection of data science and real-world health

The Talking Room

Demystifying AI, LLMs and machine learning

Use Cases

How teams put the signals to work

Compliance Hub

Data integrity, ethical AI and regulatory standards

DrugVoice

Built to be defensible.

Compliance, ethics, and data integrity are part of the process.

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TMLABS

About us

Where our proprietary models are trained, tested and refined

Articles & scientific publications

Peer-reviewed methodologies and validated outcomes

Latest paper

The New GLP-1 Consumer

Articles & Scientific Publications

Read the Paper

DRUGVOICE

DrugVoice

Measure how your messaging lands and evidence what moved

SEE IT IN ACTION

Bring us a question you can’t answer.

We’ll show you what the conversation already contains, using your therapy area and your audience.

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ABOUT US

About us

Where our proprietary models are trained, tested and refined

ESG

Responsible innovation and ethical data practice

WHO WE ARE

Data scientists who speak pharma.

A team built around one problem: making real-world conversation usable at scale.

Meet the Team

Articles & Scientific Publications

Talking Medicines regularly contribute Articles and Scientific Publications. You will find a collection of published papers authored by TMLabs on groundbreaking innovation. These publications sit alongside the Patents that have been filed by Talking Medicines to shape the future of intelligence sourced from Conversational Health Data using Advanced Data Science and Artificial Intelligence

The New GLP-1 Consumer

Understanding Unstructured Consumer Conversations in the GLP-1 Space

Large Language Models and Regulatory-Grade GLP-1 Content: Challenges and the Need for Accurate Message Measurement

Classifying patient voice in social media data using neural networks: A comparison of AI models on different data sources and therapeutic domains

Comparative analysis of Drug-GPT and ChatGPT LLMs for healthcare insights: Evaluating accuracy and relevance in Patient and HCP contexts

A Comparative Study on Patient Language across Therapeutic Domains for Effective Patient Voice Classification in Online Health Discussions

Classifying patient and professional voice in social media health posts

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