How fast can the best suited drug against a viral infection be developed: in years, months or weeks? That depends on the application – and to which extend new technologies such as AI (artificial intelligence) are used in the development process? A prime example was provided by an AI algorithm from the biotechnology company Atomwise in drug discovery to develop a treatment for Ebola infections. The algorithm accurately identified two drugs that could lead to a significant reduction in Ebola infectivity. At the same time, it reduced the timeframe for that analysis to less than a day.

However, this discovery was made 7 years ago. Since then, AI applications have developed tremendously in terms of variability and performance. And the spectrum of future technologies has grown as well with AR-VR, IoT, health apps, RPA (Robotic Process Automation), organ-on-chips and 3D printing.

For pharmaceutical, medical technology and biotech companies, the vast amount of application fields and tools present a major challenge: making the right decision to invest at short notice in order to develop new product pipelines and shorten timeto-market periods — which, of course, must be done without compromising quality or regulatory compliance.

The msgTechRADAR provides you with guidance. The rating platform supports our customers in quickly acquiring the necessary knowledge about future technologies and in identifying the right applications for their own needs. We will gladly provide trial access upon request via This email address is being protected from spambots. You need JavaScript enabled to view it.. The following use cases provide examples of technologies that are particularly relevant in the healthcare industry.

Quality management with AI agents

How can errors in the manufacturing process be detected more quickly and classified more precisely? For this task, an automotive manufacturer uses deep neural networks as “AI agents”. The “Fast AI” toolkit inspects the vehicles in auditing production using camera systems. The AI automatically detects errors or quality deviations and classifies them; then generates an entry in the system.

This application is also attractive for the process industry, as industry-specific requirements in quality management can easily be integrated into a suitable adapted application. In this way, the AI allows for constant improvement towards faster and easier error pattern detection, which is helpful in the context of constant camera guided visual monitoring of drug batches, for example. The automatic transfer of the identified error pattern into the auditing system is also valuable for obligatory documentation. A time-consuming manual search for the correct parts/material names and error descriptions is no longer necessary; this also favours initiatives for paperless production and documentation. Since the AI application also automatically “fills” all relevant error attributes, data completeness is also ensured.

Process automation with RPA

Robotic Process Automation (RPA) accelerates and improves the efficiency of data exchange between legacy systems in a company. A variety of complex, labour-intensive and time-consuming tasks that do not require user interaction from a regulatory point of view is carried out by software robots: for example, digital purchasing assistants process order requests from departments across the company and thus improve the efficiency and quality of the purchase requisition-to-order process. They simplify and speed up the submission of market authorization applications, improve the recording of production and sales activities and support the monitoring of side effects of licensed drugs. RPA is also being used in the research phase by increasing the consistency of data entries and quality checks in clinical development and thus reducing the cost for clinical studies.

One of the advantages of software robots is that they are not programmed but are “trained” by means of recorded user interactions. This not only enables a smooth implementation that does not change the existing systems and applications - but also a targetbased deployment in the field of application with the highest demand and potential for improvement. Based on the experience from our projects, the average ROI of an RPA application is less than a year.

Eliminating human errors with AR

Pharmaceutical and medical technology companies must regularly tackle with monotonous, time-consuming and error-prone processes that require a high level of concentration on the part of the operating personnel. After first promising industry experiences with Augmented Reality in action, we are underway in developing first use cases in production and quality control in conjunction with our customers for future MES and LIMS implementations.

→ Find out more: smart assistants for pharmaceutical and medical technology What does the msg.COVID-19 bot do? How do AI-supported voice assistants improve processes in the pharmaceutical industry? We explain these and other application examples in the article “Smart assistants for pharmaceutical and medical technology”.