Your AI Development Partner in Life Sciences
From diagnostics to care delivery — Modulai builds AI systems that ensure measurable results across the life science spectrum.
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Explore how AI is reshaping life sciences and health
We help health, pharma, and medtech organizations make AI real. From R&D to production, we build and deploy machine learning systems that are measurable, scalable, and secure.
– “AI in Life Science”, Modulai White paper
Our Offerings
Diagnostics
Drug Discovery
Clinical Development
Deep learning and signal processing enable automated interpretation of medical imaging, ECG, CGM, and other biosignals. These systems assist clinicians in detecting anomalies earlier and with higher precision than manual review alone.
CASES:
Detecting underlying paroxysmal atrial fibrillation for Zenicor
Detecting food intake in glucose time-series from diabetes patients
AI accelerates molecule design and target identification by analyzing chemical structures, genomic data, and biological pathways. Models are used to prioritize candidates, reduce experimental cycles, and increase the likelihood of successful outcomes in preclinical research.
Machine learning supports smarter trial design, site selection, and patient recruitment. Predictive models help estimate outcomes, optimize inclusion criteria, and reduce dropout rates, making trials more efficient and cost-effective.
Personalized Treatment
Manufacturing & Supply Chain
Knowledge & Research Systems
AI integrates patient-level data — from genomics to health records — to recommend tailored treatments and dosing strategies. Models adapt over time, continuously improving precision as more data becomes available.
AI monitors quality, safety, and efficiency across pharmaceutical operations. Predictive analytics can identify risks in supply chains, detect anomalies in production, and optimize throughput without compromising compliance
CASE:
Predicting the risk of mission failure in shipments of pharmaceuticals
Retrieval-augmented generation (RAG) and multi-agent systems enable researchers to navigate vast volumes of literature, real-world data, and trial results. These systems synthesize knowledge into actionable insights, accelerating discovery and decision-making.
Our success stories
Detecting underlying paroxysmal atrial fibrillation for Zenicor
Together with Zenicor Medical Systems AB, we trained a deep learning model to predict an underlying paroxysmal atrial fibrillation (AF) condition in patients.
Every day, thousands of people suffer from stroke due to an underlying undiagnosed AF condition. Stroke often results in conditions such as paralysis or, in many cases, death. For this reason, precise and early diagnosis of AF is an essential part of stroke prevention.
Detecting food intake in glucose time-series from diabetes patients
Together with Digital Diabetes Analytics, we created a meal detection system using glucose level data to help people with insulin-treated diabetes.
Today, more than 5,5 million people worldwide live with type 1 diabetes and need insulin-based treatment to survive. When it comes to diabetes, there’s no “one treatment fits all”, therefore doctor’s need support in optimizing treatments for each patient.
Predicting the risk of mission failure in shipments of pharmaceuticals
We applied a set of machine learning techniques to predict the risk of shipment-failure in shipments of temperature-sensitive pharmaceutical goods.
This client is one of the world’s leading providers of solutions for tracking sensitive pharmaceutical goods during shipment and throughout the whole supply chain. Each shipment is tracked minute-by-minute by a temperature logger.
Read our detailed Tech Blog
Building a prototype for One-Click drug discovery
We built a prototype end-to-end ML system that demonstrates how drug discovery workflows could be streamlined into a computational workflow.
Starting from a disease name, it identifies relevant biological targets, learns from real bioactivity data, and ranks candidate molecules by predicted activity. While not a tool for finding real drugs without substantial experimental validation, it illustrates how AI can structure and accelerate the discovery process.
Classification of hypoglycemia causes in blood sugar time series
We give an overview of a study conducted in collaboration with Daniel Espes and Per-Ola Carlsson at Uppsala University aiming to improve the treatment of type 1 diabetes.
Type 1 diabetes is one of the most common chronic disorders among children and adolescents but affects people of all ages and globally more than 5 million people are affected.
Explainable AI in medicine: Detecting AF in ECG data
Machine learning models usually do not explain their predictions. This is a significant barrier to adaptation in domains like medicine, where understanding how the model works is vital.
In this blog post, we take a look at how explainability techniques can be used on a deep learning model predicting atrial fibrillation from sinus ECG (electrocardiogram) data.
