Health and Work AI Lab projects

Key technologies: Web mining, Natural Language Processing, Anomaly detection,

An AI‑Driven Pulse Monitoring System is a text‑mining tool that scans large volumes of public online content—such as parliamentary debate transcripts, thousands of million tweets, and news articles—to track how important societal and political topics evolve over time. Using advanced AI pipelines, the system identifies patterns, shifts in public debate, and changes in mood, giving a detailed and up‑to‑date picture of what people and politicians are talking about. This high‑resolution view helps detect early signals of changing public sentiment and offers a scalable way to monitor conversations across multiple sources.

Technologies used: RAG, LLM.

LLM-based chatbot for parents with questions about upbringing of their child, based on guidelines and education. Guardrails are incorporated for escalation to professional.

Key technologies: Simulation.

The Carebots project is a pilot exploring how autonomous robots can support hospital logistics. The focus is on helping staff—especially pharmacy assistants, who experience high workload—by automating routes where bulky or time‑sensitive items must be transported. The pilot studies robot behavior in real hospital conditions, including navigation among people, handling manual doors, timing deliveries, and managing risks like lift usage or dependence on staff. It also identifies opportunities for improvement, such as larger drawers, better flexibility, shared control, and reducing anxiety for children.

https://www.nlrobotics.nl/carebots

Technologies used: Ontologies, RAG, LLM.

LLM-bases system to provide decision support based on care protocols and unstructured data in the EHR.

Key technologies: AI, body keypoint tracking, anomaly detection, multi-modal fusion, spatio-temporal graph-based machine learning, privacy enhancing technology.

Subtle patterns or abnormalities in movement, speech, and language behaviour often go unnoticed through traditional assessment methods, even though they may represent early signs of disease onset. As a result, diagnoses are frequently delayed, and opportunities for timely intervention and treatment are missed. Artificial intelligence (AI) offers the potential to support healthcare professionals by improving the accuracy and efficiency of early detection through a multimodal approach. Our team is developing and evaluating the feasibility and design of a reliable, privacy‑preserving, AI‑driven system that integrates video, speech, and additional parameters to help identify early indicators of Duchenne Muscular Dystrophy and other developmental disorders in children. Early diagnosis can substantially reduce both healthcare costs and human suffering, making this approach highly promising from both clinical and societal perspectives.

https://openlab.tno.nl/projecten/ai-ontwikkelings-aandoeningen-kinderen/

Technologies used: LLM, Data Analysis, Data Vizualization.

A data-driven dashboard that uses LinkedIn data from RevelioLabs to explore labour market dynamics across Europe. The tool enables cross-country comparisons of skills and career transitions, provides information on career trajectories, including skills and occupations. It also provides insights into geographical distribution of occupations.

The dashboard currently covers six countries - the Netherlands, Italy, Poland, Bulgaria, Germany, and Luxembourg – and contains data from over 26 million unique job profiles.

It also includes a 2D representation of the embedding space of all of the ESCO skills and knowledges. Allowing the user to analyze skills and knowledges distributions for specific occupations. This tool might serve the researchers and legislators with interest in the labour market landscape in Europe.

https://diamonds.tno.nl/european-labour-market-dashboard/external-app/run/21

Technologies used: LLM, NLP, self-training AI models.

INoVA Hub provides a smarter way to explore screening models. Its main purpose is to support researchers in the quest for suitable screening models to test their hypotheses. This includes non-animal innovations like organoids and in vitro systems, but also the animal-based models in use.

https://inovahub.tno.nl/

Key technologies: Speech-to-text, LLM.

As experienced maintenance and installation workers age and retire, their extensive knowledge retires with them. Transferring that knowledge is the key to long-term business success, but creating accurate, effective instructions can be challenging and costly. We have developed a tool that automates this process, reducing the barriers to creating work instructions and thereby increasing productivity. With Instant Instructions, workers use a tablet to film each other performing routine and specialised maintenance and installation tasks, and explain each step of what they are doing. The power of AI then translates that input into easy-to-follow, step-by-step written instructions and accompanying video of each step. Once uploaded into the platform, the instructions can be controlled for quality, improved where needed, and translated into any language that might be required.

https://ventures.tno.nl/portfolio/instant-instructions/

Award-winning automated text coding system, winning the Eurostat "Occupations for Online Job Advertisements Challenge" and evolving into our ObjectivEye spinout.

Key technologies: LLM, RAG, Classification.

https://statistics-awards.eu/announcements/winners-wi-2nd-round

KnowledgeMiner is an online AI-based tool that assists experts in systematically finding evidence in scientific literature. It does so by facilitating the creation and expansion of dedicated ontologies and using them to mine substantial volumes of literature. Results are presented in heatmaps and datatables that can easily be used to further explore, analyze and filter relevant results.

https://diamonds.tno.nl/projects/knowledgeminer