AI in pharma & healthcare

We support companies in the healthcare sector in increasing the efficiency and quality of their services through the effective use of artificial intelligence. Our solutions help to make better use of medically relevant data, optimize processes and develop new, patient-centric business models.

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LEADING HEALTHCARE COMPANIES RELY ON OUR KI SOLUTIONS:

Better care and faster innovation in healthcare using AI

We use AI to make diagnoses more precise, personalize treatments and reduce the workload of medical professionals. In the pharmaceutical industry, we accelerate research and development along the entire value chain. Our scalable systems ensure reliable results and seamless integration. The aim is to increase efficiency and quality in the healthcare sector through innovative, patient-centered AI solutions.

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Industry Problem

Key challenges in pharma & healthcare and our solutions

01 INCREASING PRESSURE ON HEALTHCARE SYSTEMS

Problem

Healthcare systems are under pressure: an ageing population, a shortage of skilled workers and rising costs meet the demand for high-quality care. Resources must be used efficiently without jeopardizing patient outcomes or the speed of innovation.

Merantix solution

Prescriptive analyses: AI-supported forecasts for patient flow and resource management
AI image analysis: Automated evaluations from imaging procedures (drug candidate screens, microscopy images, MRI images, behavioral studies, quality assurance, etc.)
AI-driven workflow optimization: automation of administrative processes for more focus on patients

02 DATA MANAGEMENT AND INTEROPERABILITY

Problem

Enormous amounts of medical data are generated every day, from patient records and diagnoses to study results and images. However, the sheer volume of data and lack of interoperability between systems make it difficult to use it in a structured way. Valuable information remains isolated and slows down data-driven care, research and cross-sector collaboration.

Merantix solution

Medical writing: Automated creation of medical documentation with AI
Increased data accessibility: AI models and large language models for easy data retrieval and use
Advanced data integration: integration of data platforms for seamless exchange and better collaboration

03 PERSONALIZED PATIENT CARE

Problem

Personalized care is crucial, but challenging due to different patient needs and the complexity of individual treatments. The pharmaceutical industry, health insurers and care providers want to provide patients with the best possible support based on available health data, but are faced with the task of using large amounts of data effectively for customized solutions.

Merantix solution

Risk-adjusted health programs: Risk-adjusted health insurance rates and funding individual health programs, through a deep understanding of patient data
AI-supported diagnostic tools: Analyze AI-based patient data to enable more accurate diagnoses and tailored treatment plans
Personalized treatment plans: development of AI-driven solutions that take into account each patient's medical history and needs for individualized care

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AI in pharma & healthcare

Bertram Weiss

Merantix Solution

We develop AI solutions that make complex data usable in research, clinics and production

Phenotype analysis with AI

Instead of qualitative assessments, you receive precise, quantitative insights from image and video data - for example in microscopy, tissue analysis or phenotypes.

AI for imaging procedures

Support medical professionals with AI-supported evaluation of CT, MRI or skin images for faster diagnoses and better decision-making

AI-accelerated documentation

Whether doctor-patient consultations, production documents or MLR reviews: With AI, documentation processes are structured, accelerated and implemented in compliance with regulations

LLMs and NLP for scientific texts

Analyze scientific literature automatically to extract key topics, relate them to your own clinical or research data and identify new hypotheses and causal relationships, and make internal text sources such as studies, protocols or reports usable in a targeted manner using RAG models.

Prediction of disease progression and therapy outcomes over time

Use AI to predict the progression of diseases or treatments over time to support informed decisions for personalized and effective care.

Network and graph data analysis for drug development

Use graph-based AI methods to analyze target structures, molecular interaction networks and signaling pathways. This supports the discovery of new active substances and the understanding of complex biological processes.

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Our projects