Spotlab

Spotlab, established in 2018, operates as a specialized AI tool provider for biopharma research and diagnostic support. The company originated as a spin-off from the Universidad Politécnica de Madrid and was co-founded by Miguel Luengo-Oroz (CEO), Maria Postigo Camps (COO), and Alexander Bakardjiev. Luengo-Oroz brings a significant background to the firm, having previously served as the first Chief Data Scientist at the United Nations, where he focused on applying data science to address global challenges like epidemics, human rights, and climate change. This experience directly informs Spotlab's mission to create impactful technological solutions for healthcare, particularly in democratizing medical diagnostics.

The company's core business involves creating customized AI models that analyze multimodal clinical data—including images, video, and text—to identify digital biomarkers. These biomarkers are crucial for assessing disease progression and treatment response, pushing forward the capabilities of personalized medicine. Spotlab serves clients such as pharmaceutical companies and global health institutions, providing them with tools to enhance diagnostic efficiency, increase accessibility, and standardize analyses across different locations. Its revenue model is based on providing these AI solutions and services, with the company having secured its first sales in early 2023. The business operates in the digital health and AI diagnostics market, with a focus on areas ranging from blood cancers to neglected tropical diseases.

Spotlab's main offering is its TeleSpot® technology platform, an ecosystem for creating and deploying AI models in clinical settings. This platform features a cloud architecture connected to web and smartphone applications, and it can utilize 3D-printed devices to turn a standard smartphone into a medical-grade imaging tool. The platform is designed to be end-to-end or modular, offering deployment via the cloud, on-device (Edge-AI) for real-time analysis without internet connectivity, or through an API for integration into client systems. Specific products include MedulAI, for interpreting bone marrow samples in hematological diseases, and ParasitAI, for identifying parasites in stool samples. A key feature is the use of proprietary foundational AI models and Large Language Models (LLMs) to integrate diverse data types and function as an intelligent assistant for clinicians, ensuring high accuracy even with limited data.

Keywords: AI diagnostics, biopharma research, digital biomarkers, medical imaging analysis, personalized medicine, mobile health, point-of-care diagnostics, hematology AI, neglected tropical diseases, clinical decision support, multimodal AI, MedulAI, ParasitAI, TeleSpot, cloud diagnostics, edge AI healthcare, digital pathology, global health technology, computational biology, biomedical data analysis, automated microscopy