Services
AI Applications
At AINIGMA Technologies, we translate advanced AI research into impactful real-world healthcare applications. Our work spans five key pillars, each grounded in leading-edge technologies and driven by collaborative research projects across Europe.
1. Research on Clinical Trials
We pioneer the use of digital twins to improve the efficiency, ethics, and scalability of clinical trials. By creating synthetic representations of patients, we enable data-driven simulation of control arms—reducing the need for placebo groups. Our digital twin-based synthetic control arm frameworks allow for faster, more adaptive trials, and open the door to virtual emulation of long-term outcomes.
2. Medical Image Processing
We develop state-of-the-art deep learning solutions for automated analysis of medical images. Our models support clinicians by enhancing the accuracy and speed of diagnostics. In Project ONCOSCREEN, we are building AI models for early detection of colorectal polyps using colonoscopy videos, improving screening effectiveness. Meanwhile, Project RELEVIUM focuses on automated sarcopenia detection from musculoskeletal ultrasound images, providing tools to assess frailty in older patients. These applications exemplify our commitment to clinically relevant and explainable image AI.
3. Biomedical Signal Processing
AINIGMA builds intelligent pipelines to extract meaningful insights from complex physiological signals. In Project AIPROGNOSIS, we develop machine learning models that predict Parkinson’s disease progression using wearable accelerometer data. In Project I-PROLEPSIS, we apply temporal modeling to forecast FLAIR patterns in patients with psoriatic arthritis, enabling more personalized treatment strategies. We also contribute to EEG-based research in Project RAISE, where we build robust pipelines for preprocessing and analyzing EEG signals in brain-computer interface studies.
4. Prediction of Disease Outcomes
We use AI to model complex health trajectories based on clinical, environmental, and lifestyle data. From Project AURORA and ISMED, we investigate the impact of environmental exposures on long-term health outcomes using EHR and public health data. In cancer care, our models for risk stratification in prostate (PHASE-IV) and rectal cancer (DIOPTRA) support personalized decision-making. We also apply AI to optimize nutritional plans tailored to patient physiology, closing the loop between prediction and intervention.
5. Large Language Models for Health Use Cases
Our work extends to the use of Large Language Models (LLMs) in health and wellbeing. In the ASSETS project, we develop conversational agents to recommend evidence-based social and nutritional interventions, enhancing community-level care delivery. From chatbots offering dietary advice to tools assisting healthcare professionals with contextualized patient communication, we aim to make LLMs both safe and effective for clinical and public health applications.
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Data Harmonization
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Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency. Neither the European Union nor the European Health and Digital Executive Agency can be held responsible for them.
