DATA & AI PROJECTS FOR pharmaceutical companies
We have used our experience from over 1.000 projects in the last 8 years to develop a holistic system for data & AI projects – our [at] Data Journey. A consistent data strategy forms the basis and the framework for the efficient use of data in your company. The goal is to test Use Cases as quickly as possible in order to develop a prototype with real data from the concept in a timely manner. In the Data Factor the Use Cases are industrialized into finished products. The absolute main focus is on scaling and the sustainable generation of added value – as such the user is just as much the focus here as well. In our DataOps, we operate and maintain your platforms and machine learning algorithms.
Since our founding in 2012, we have become a leading provider for Artificial Intelligence, Data Science and Big Data in the German-speaking region. Together with our customers, we generate real added value from data. To this end, we develop and implement data-driven innovations and new business models. We empower our customers to develop their own strengths and accompany them on their journey from a data strategy to the development of algorithms and the construction of IT architectures to maintenance and operation.
Opportunities of AI for the pharmaceutical industry
Reduce development time
Development time of new drugs can be shortened
Access to all relevant data with a data warehouse
Make more effective and intelligent decisions
AI assists in finding a suitable dose of active ingredient
Measure patient satisfaction and treatment adherence
Reduce resources through process automation
Projects of our customers
We have demonstrated our data science and AI expertise in the pharmaceutical sector for various projects. Read some references about AI in the pharmaceutical industry here. If you have any questions, please feel free to contact us.
AI-based visibility of drug position throughout the cold chain
NLP-based text analysis to reduce the time required for drug discovery and development.
Timely prediction of quality deviations
Intelligent system for automatic adjustment of parameters for material disposition
Drug approval delay based on centralized approval management.
Biopharmaceutical performance optimization
Industry Exchange #KIpharma
Compared to other industries, the pharmaceutical industry has so far neglected artificial intelligence. Yet this is precisely where a wide range of opportunities arise: AI can predict reactions – and even suggest reactions that produce specific active ingredients. In our Industry Exchange, experts from the pharmaceutical sector came together to talk about the opportunities, challenges and also the reasons for the comparatively hesitant use of artificial intelligence to date. Regulatory and ethical requirements were also considered. And ultimately also about encrusted leadership cultures in an established industry. Check out our Industry Exchange #KIPharma anytime, for free.
DEVELOPMENT & EXAMPLES OF AI PHARMA
IQVIA research highlights ten potential areas, including the use of digital applications in healthcare, artificial intelligence (AI) and machine learning (ML), next-generation biotherapeutics and insights from healthcare practice.
According to this research, the use of artificial intelligence and machine learning will soon become the norm for life science companies. Currently, the most advanced method is to use intelligent algorithms to analyze large and complex data sets, especially in clinical and preclinical research. The algorithm is used to screen preclinical drug candidates for new drugs and identify potential targets based on supply data. Overall, they have been found to improve the efficiency of clinical development. However, to better subdivide patient groups or to better identify undiagnosed patients, predictive analysis supported by ML can be used.
According to IQVIA, the U.S. Food and Drug Administration (FDA) is receiving more and more requests to approve mobile apps for therapeutic purposes. These digital therapies (DTx) require prescriptions and the use of digital technology for treatment. They are expected to make significant progress, particularly in the areas of health behavior and cognition. On the other hand, the evaluation of new treatments by other stakeholders is more rigorous because their benefits have not been demonstrated in practice.
• Information about competitors can be collected, analyzed and prioritized more quickly
• Chatbots provide automated feedback to patients and healthcare providers
• Patient cohorts can be identified and thus recruited for research studies
• Advanced analytics helps identify suitable drugs more quickly for alternative applications
3 reasons for choosing the data & ai experts at [at] [at]
Leader in AI and Big Data
We operate independently of manufacturers. We strive to find the right technology for our customers according to their needs and support them in implementing it.
Data & AI experts for pharma industry
We have successfully delivered over 1.000 AI & Data Science projects, including over 50 in the pharmaceutical industry.
Coming from the automotive industry, we have been able to transfer our Data and AI approach to other industries. In the meantime, more than 50% of all Dax corporations are among our customers. As an AI & Data Science consultancy, we have already implemented over 1.000 projects for customers in a wide range of industries across the entire value chain.