Cutting-Edge Artificial Intelligence
We believe that artificial intelligence (AI) will have a positive impact on our society and environment. AI allows us to address urgent yet unsolved problems in a broad range of fields, from specific applications within organisations to problems of global significance.
For instance, AI can improve patient health through the safe and robust application of computer vision, natural language processing, and time series methods.
Complex problems require custom, cutting-edge AI solutions – and that’s where we come in. Zühlke has a world-class team with years of research and engineering experience in building and running AI solutions.
AI is not a silver bullet. Applied irresponsibly, AI carries significant risk of biased and unfair decisions that can negatively impact people’s lives. That’s why we focus on building cutting-edge, safe, robust, and explainable AI solutions in a principled, responsible way, from the initial vision to large-scale deployment.
The application of AI in the area of medical imaging is a highly promising development in healthcare. For a Swiss MedTech company, which recently was awarded the CE certification for medical devices, we set up a medical machine learning process and also designed, implemented, and validated a regulatory compliant data platform and computer vision use cases.
Computer vision can greatly increase the efficiency and scale of animal observation. Automatic species identification and even individual animal tracking facilitate the study of behavioural dynamics of whole populations. This supports the understanding of ecosystems and increases biodiversity.
Early detection and forecasting of wear of mechanical parts is an important element of timely scheduling of maintenance. Together with a Swiss transportation company, we built a machine learning solution to automatically measure wear of pantographs on passenger trains. Using computer vision segmentation of photographs of the trains, our algorithm can measure deterioration of materials. This allows early scheduling of maintenance and therefore minimises the downtime of rolling stock.
Electronic Medical Records
Electronic medical records (EMR) contain vast amounts of information about a patient’s medical journey. Machine learning methods and EMR can be used to prevent disease and improve treatment decisions, which improves patient outcomes. For a Swiss healthcare provider we developed cutting-edge recurrent (LSTM) deep learning models to classify EMR to support healthcare professionals and streamline hospital processes.
Natural Language Processing can be used to automatically classify documents and take appropriate action. For a Swiss transportation company, we developed a Transformer-based deep learning system for high-volume email processing. Our solution understands and classifies each email's topic, automatically assigns emails to appropriate agents and analyses end client problems and trends over time.
Question Answering (QA) systems allow for information retrieval from large bodies of knowledge, automatically answering questions posed in natural language. For a Swiss authority we developed a QA system based on state-of-the art Transformer-based deep learning methods. With our solution, frequently asked questions of the Swiss population can now be answered automatically, while users are guided through a natural dialogue-based interface.
Climate change is a key challenge of our time, and addressing it with a broad spectrum of solutions will ensure the best possible outcome. Machine learning will be a key part of this solution, for example in improving energy production and use, optimising transportation and routing to reduce emissions, enhancing production to reduce waste, and monitoring the environment and the climate as a whole.
In the financial sector, quantitative methods play a key role. For a telecommunications provider we developed predictive time series models to perform financial performance forecasting on multiple time series, which supports the provider’s financial experts in their work.
The transition to renewable energy will help mitigate climate change. For an energy provider we developed time series models to predict the risk of failure in wind turbines based on a large quantity of near real-time sensor signals, enabling their service partners to proactively perform maintenance, ensuring a high degree of uptime and optimal energy production levels.
Helping general practitioners diagnose complex diseases can lead to faster and better treatment that would otherwise be missed due to underdiagnosis. Based on a machine learning model from the research department of a large pharmaceutical company, we developed a system to support physicians in their differential diagnosis for pulmonary diseases. To do this, we brought the model under design control to meet all regulatory requirements for a validation study, and to create the basis for FDA approval.
Continuously monitoring a patient’s vital signs is an important step in preventing morbidity and mortality. Machine learning can help interpret these signals and give early warnings for high-risk events like sepsis or circulatory failure.
FemTech aims to positively impact women’s health and focuses on digital health solutions and products in the area of fertility and period-tracking, pregnancy, sexual wellness and menopause. We helped AVA Women to extend the functionality of their fertility tracking bracelet to further indications. This put the medical device wearable in a higher risk class. We reviewed and improved the processes used for developing the machine learning models. Thanks to this, AVA now fulfils the regulatory needs and successfully obtained FDA clearance of its 510(k) application.
Dr. Philipp Morf holds a doctorate in engineering from the Swiss Federal Institute of Technology (ETH) and holds the position head of the Artificial Intelligence (AI) and Machine Learning (ML) Solutions division at Zühlke since 2015. As Director of the AI Solutions Centre, he designs effective AI/ML applications and is a sought-after speaker on AI topics in the area of applications and application trends. With his many years of experience as a consultant in innovation management, he bridges the gap between business, technology and the people who use AI.
Tobias Joppe studied automation and control engineering at the TU Braunschweig and was most recently head of a innovation team at Siemens AG. He has been with Zühlke since 2008, is a partner and, as Director Customers Solutions, is responsible for the Trend Lead Data Science in Germany. In his role, he builds the bridge between cutting-edge technology and current customer needs. Together with customers, he translates visions and goals into a strategic roadmap and concrete project procedures. As Director Customers Solutions, many completed interdisciplinary projects form the basis of his experience.
Dan has a great passion for data and leads Zühlke Engineering’s data practice. Throughout his data driven career, Dan provides stakeholders with reliable advice and has helped plan enterprise data transformation programs and led delivery teams. An engineer by training, Dan bridges the gap between the boardroom and teams working at the coalface and couples his strategic thinking with his commitment to data and years of experience.
Nicolas oversees the Healthcare and MedTech vertical at Zuhlke Singapore. As part of the Business Development team, he works on digital innovation initiatives together with both global and local customers. Nicolas finds joy in helping clients connect the dots, from conceptualisation to creation of exciting products.
Cristian Hofmann is Business Solution Manager and since März 2020 at Zühlke. After his studies of Computer Science and Doctorate in "Digital Multimedia" and "User Experience", he worked at Fraunhofer Gesellschaft as Senior Researcher and Project Manager. From 2012 to 2020, he was employed at adesso Austria, for the time being as Project Manager, and later as Managing Consultant and Head of Consulting. His emphasis is the field of UX/CX and Innovation Management as well as Web and Mobile Applications. He was able to employ these skills at customers of diverse business areas, such as Insurance, Utilities or Public.