Center for Artificial Intelligence in Medicine

CAIM Fellowships Call 2023

The CAIM Research Fund is opening a call for CAIM Fellowships. The project funding to support early-career researchers with promising career prospects in translational AI in medicine research will support projects for up to 2 years with up to 100’000 CHF. Deadline for applications is 25 September 2023.

More information and application documents...

Trailblazers "AI in Medicine" series

New series in fall & spring, organized by the Institute for Tissue Medicine and Pathology as part of the initiative "Diversity for AI in Medicine" (DAIM). The seven talks by speakers from Belgium, Canada, Israel, Singapore, Switzerland and UK can be booked separately or as the whole series. Fall 2023 features "Women in Digital Pathology", spring 2024 "Women in Spatial Omics".

All further information and registration...

Artificial Intelligence Symposium 2023

Hybrid AI Symposium on challenges in translation of AI research into healthcare practice and adoption, encompassing safety aspects, teaching approaches for healthcare professionals as well as trust building.
Rounded off by a panel discussion by representatives of clinic, research and industry.

Learn more and register...

Gaming for Science

Mike Falkner spends up to 10 hours a day behind a screen. What can't possibly be healthy, actually improves memory and fine motor skills. But first things first...

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AI-assisted brain tumor measurements

Automated tumor segmentation for glioblastoma is promising, yet little is known about longitudinal accuracy of automated measurements to assess treatment response. The Medical Image Analysis lab at the ARTORG Center for Biomedical Engineering Research at the University of Bern compared assessment consistency by two AI-based tumor segmentation tools against expert ratings over time, identifying a need for further improvement.

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AI in Digital Pathology: Opportunities and Challenges

First part of the "Trailblazers" series. Anne Martel, Professor in Medical Biophysics at the University of Toronto, will outline some of the unique challenges of working with these extremely large whole slide images in computational pathology and discuss some of the approaches that her lab has developed to overcome the problems of sparse annotations and weak, noisy labels.

Register here for the online talk...

Machine learning limited in IBD subclassification

The Inselspital and ARTORG Center, Uni Bern, have tested a supervised and unsupervised machine learning approach using antibodies in lab samples to distinguish Crohn’s disease (CD) and ulcerative colitis (UC) as subtypes of inflammatory bowel disease (IBD). The approach showed good sensitivity to correctly assign CD and UC, but could not aid in finer distinction of subtypes such as IBD-unclassified due to the lack of specificity of the analyzed markers.

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Deep Learning Dose Prediction for Radiotherapy

Radiotherapy treatment requires accurate contouring of tumor volumes and organs at risk. In a collaboration between the ARTORG Center for Biomedical Engineering Research, Uni Bern, and the Department of Radiation Therapy, Inselspital, a deep learning architecture for automatic segmentation and 3D dose prediction for glioblastoma patients has shown good sensitivity and robustness.

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"Deep Learning can contribute to improving surgical outcomes."

Negin Ghamsarian is a postdoctoral researcher at the ARTORG Center for Biomedical Engineering Research. She believes that self-supervised and semi-supervised deep learning can overcome current constraints in the feasibility of AI techniques for surgical video analysis to better predict postoperative complications and offer more precise surgical interventions.

Read the interview...

"We want to make eye testing and monitoring more widely available."

As part of the Venture Leaders program 2023, PeriVision CEO Patrick Kessel allows insights into the young company´s technology, market potential as well as his personal approach towards entrepreneurship.

Read the VentureLab interview...

PeriVision closes first Seed Round

PeriVision, a spin-off from the University of Bern, is utilizing the power of AI, virtual reality, and cloud computing to redefine eye testing and care. This spring, the company successfully closed the first tranche of a seed round, raising CHF 0.5-1.0M to further accelerate research and development, and prepare for the launch of their inaugural product in Q1 2024.

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AI assisted epilepsy diagnosis

In a clinical validation, the Support Center for Advanced Neuroimaging (SCAN) of the Inselspital has compared the accuracy of combining an AI assistant developed at SCAN with a human expert reading of MRI imaging with the performance of a neuroradiologist reading to diagnose hippocampal sclerosis. The team found that the "human + machine" model improved the mean accuracy of the raters from 77.5% (MRI only) to 86.3% (MRI + AI assistant).

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DAIM Lunch Talk Lisa Falco

CAIM is thrilled to host Lisa Falco, Lead Consultant for AI & Data at Zühlke, for a DAIM Lunch Talk on "Responsible (Generative) AI". Lisa has 15+ years of industry experience in machine learning for healthcare and has led the development of two AI based medical device products, that were brought to market under regulatory requirements. She is the author of “Go Figure! The astonishing science of the female body”, an advocate for women’s health and active in the FemTech community.

More info & registration...

Statistical learning in lab data

Laboratory information systems are struggling to manage and analyze the amount of complex and entangled data generated in laboratory medicine today. Statistical learning, a generalized framework from machine learning and AI, is predestined for processing this “Big Data” and holds the potential to revolutionize the field. To ensure quality, transparency, and public acceptance, human experts should carefully validate AI-based systems, including patient-privacy protection.

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Tumor budding T-cell graphs

Minimally invasive types of colorectal cancer (pT1) may only necessitate removing the polyps instead of requiring full colon resection to stop cancer progression. To assist pathologists, the Institute for Tissue Medicine and Pathology of the University of Bern has evaluated a Graph Neural Network based approach for risk stratification based on the interaction of tumor buds and T-cells as well as other histopathological risk factors with about 20% higher specifitiy than current guidelines.

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First anniversary DAIM

In May 2023, our initiative "Diversity for AI in Medicine" became one year old! We are extremely happy to witness the commencement of such a vibrant community of AI researchers for medicine here in Bern. Special thanks to all our supporters and the many speakers that have inspired us this past year!

Discover DAIM’s first year here...

AI in interstitial lung disease

Over 200 diagnoses exist for the umbrella term "interstitial lung diseases" (ILDs). These are currently determined by an expert board based on CT imaging, patient data, pulmonary function tests, and histology. AI methods could help aid in accurate ILD diagnosis and predict prognosis and progression in a holistic system, as a review paper by the ARTORG Center and Inselspital comparing current approaches shows.

More on the review...

Un centre consacré à la recherche sur l'IA en médecine

In a special issue dedicated to AI in medicine, the Société Vaudoise de Médecine presents our research center. The short introduction to CAIM include the essential role of ethics and our strive for multi-perspective work environments through the inclusion of diverse teams and medical as well as engineering professions. It also gives an overview of our Master's program AI in Medicine and the five research projects CAIM is currently funding.

Read the article (in French)...

"AI supports conscious nutrition"

Artificial intelligence for the management of diabetes or obesity is gaining importance in everyday life and in medicine. Prof. Dr. Stavroula Mougiakakou, Professor of Biomedical Engineering at the ARTORG Center of the University of Bern, gives an insight into the development of food apps and shows how much humanity is still in the algorithms of AI.

Read the interview in gastroMAG (German) ...

Can AI chatbots transform patient care?

Generative AI such as ChatGPT are currently on everyone's lips. Also in healthcare, such systems have an enormous potential to revolutionize existing processes. An overview by the Computational Medicine Group, University Institute of Clinical Chemistry, Inselspital Bern, illustrates the opportunities and risks of this fascinating technology.

Read the article in Netzwoche (in German)...

Tele Emergency Medicine Congress

Forth Swiss congress under the focus "Metaverse in Acute Medicine: Entering digital worlds". New this year are Medical Extended Reality Workshops at the Verkehrshaus Luzern from 9.00-12.00h. The afternoon sessions from 13.30h are online.

More information and registration...

How AI is changing medicine

In an article on current implementation of artificial intelligence in healthcare today, Barbara Reye explores the disruptive potential of machine learning in medicine. Examples range from intensive care monitoring and false alarm reductions over early signs for cerebral hemorrhage, medical image analysis and diagnostic support, skin cancer, newborn health and a nutrition app developed at the University of Bern to help users stay on track with a health-promoting Mediterranean diet.

Read the article...

"I believe that AI is the key to a new era of healthcare."

Song Xue is a biomedical postdoctoral researcher specialized in deep learning at the AI for Translational Theranostics (AITT) group of the Department of Nuclear Medicine, Inselspital. Applying artificial intelligence to nuclear medicine, Song aims to reduce radiation in diagnostic PET imaging and to personalize dose prediction for radionuclide therapy.

Read the interview...

Bern Interpretable AI Symposium

One-day meeting to bring researchers together in the medical image interpretable AI community. Our hope and objective is to attempt to “open the black box”, share insights into challenges and breakthroughs in this field and to foster better interaction with each other.

Program & registration...

"With deep learning you can explore rich patterns of neural activity."

Florence Aellen is the deep learning specialist at the Cognitive Computational Neuroscience Lab of the Institute of Computer Science, University of Bern. For her PhD she works with a very interdisciplinary research team to unravel interrelations between electrical brain activity and states of consciousness. Herself originally coming from a background in mathematics and theoretical physics, she now puts her computational expertise in the service of clinical applications, learning a lot in the process.

Read the interview...

AI Medtech Founders

Ready to start your own AI business? Networking and insights sharing event on Women´s Day 2023 featuring four Medtech founders and hands-on tips on how to get support and funding and how to build your network.

More on the event...

AI to detect lymph node metastases in colorectal cancer

Screening of lymph node metastases in colorectal cancer (CRC) could be solved by an AI-assisted diagnostic solution. A group of pathologists and data scientists led by the Institute for Tissue Medicine and Pathology, University of Bern, has proposed a deep learning–based workflow for the evaluation of CRC lymph node metastases. The approach showed very good performance, providing the basis for a computer-assisted diagnostic tool for easy and efficient lymph node screening in CRC patients.

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Measuring blood glucose with the smartwatch

Researchers at the Inselspital, Bern University Hospital, and the University of Bern developed a method that uses machine learning and smartwatch data to detect hypoglycemia. The approach was tested in a pilot study with 31 diabetes patients and could in the future be used to non invasively measure blood glucose levels as a valuable complement to existing methods for blood glucose monitoring.

Read the study...

Bridging deep learning and clinical radiotherapy

The ARTORG Medical Image Analysis group (MIA) and the Department of Radio-Oncology of the Inselspital have developed an open-source, deep learning framework-independent Python package feasible for processing DICOM RT Structure Sets, in collaboration with Varian a Siemens Healthineers Company. “PyRaDiSe” (Python (Py), radiotherapy (Ra), DICOM-based (Di), auto-segmentation (Se)) goes beyond current 2D reconstruction, potentially supporting acceptance by healthcare professionals.

View the package…

Predicting heart attack from routine hospital data?

The Department of Clinical Chemistry and the Insel Data Science Center at the Inselspital have conducted a proof-of-concept study to infer the diagnosis of myocardial ischemia from predictive analytes extracted from routine hospital lab data. Authors could demonstrate that an assembly of algorithms was able to create unbiased guidelines for laboratory diagnostics by using computational evidence in today’s era of healthcare digitalization. This is an important first step towards evidence-based laboratory diagnostics and, therefore, improved patient care.

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AI-assisted interpretation in cardiotocography

Artificial intelligence is gaining interest in the field of cardiotocography interpretation in obstetrics, since it promises to remove existing biases and improve the well-known issues of inter- and intra-observer variability. A review publication by the Department of Obstetrics and Feto-maternal Medicine, Bern University Hospital, with CSEM hints at potentials of AI to help detect complications in the unborn child earlier.

Read the review paper...

Portrait Athina Tzovara

How can artificial intelligence help to better understand states of consciousness? Assistant Professor TT in Machine Learning in Medicine, Athina Tzovara, explores brain activity data with AI to understand how brain functions change during sleep or in coma patients. She works both at the Institute for Informatics of the University of Bern and for NeuroTec, the research platform of the Inselspital Neurology Department.

Watch the video portrait...

LUMIERE Dataset for Glioblastoma Released

The Medical Image Analysis group at the ARTORG Center of the University of Bern in collaboration with colleagues at Inselspital, Bern, has released the first publicly available single-center dataset of Glioblastoma Magnetic Resonance Imaging (MRI) with expert ratings. It comprises over 600 multi-sequence MRI acquisitions and expert readings of selected studies according to the response assessment in neuro-oncology (RANO) criteria, automated tumor segmentations, and a rich set of complementary information, such as advanced imaging biomarkers, patient demographics and pathology information.

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"I would like to convert my research into a useful tool for clinicians."

Amith Kamath wishes to facilitate faster radiotherapy treatment for patients with glioblastoma through AI-supported therapy planning. The CAIM Young Researcher Award winner is pursuing his PhD at the Medical Image Analysis group of the ARTORG Center for Biomedical Engineering Research and looks forward to translating his research into a clinical tool, also through the personalized business coaching by be-advanced he received as part of his award win for “translation”.

Read the interview...

CAIM supports Ukrainian scientists

The Center for AI in Medicine supports scientists from Ukraine in these difficult times. Following the measures and call from the Swiss National Science Foundation to help Ukrainian researchers as well as the University of Bern’s initiative to solidarize with Ukraine and its universities, CAIM calls its members and partners to reach out and proactively seek ways to help our colleagues.

Contact info.caim@unibe.ch with ideas or requests for support.

Deep Learning detects neurodegeneration at the micrometer scale

A research group from Inselspital and the University of Bern has developed a deep learning tool that can measure neuro-degeneration reliably and significantly faster than established methods. A scientific paper now shows that the algorithm can be trained to read different types of MRI images used in everyday clinical practice. Research at CSIRO's Australian eHealth Research Centre has furthermore shown in an independent evaluation that the open-source method can measure subtle changes in the range of up to 0.01mm (reference 0.06mm).

Read the studies...

Survey on artificial intelligence in medicine

The Ethics Lab of the Center for Artificial Intelligence in Medicine (CAIM) collected voices from the public to learn about their perception of opportunities or risks associated to the use of artificial intelligence in medicine. The results will flow back into the center’s research activities.

Read the article...

SDiPath Annual Assembly and Industry Fair: AI in Pathology

SDiPath’s second industry fair, this time focusing on AI applications in pathology.
The event takes place on-site, from 10-17:00 at the Institute of Pathology, University of Bern on Thursday, January 19th, 2023.

More information and registration...

"I felt I was part of something much bigger than us."

CAIM Young Research Award winner Charlotte Kern and her colleague Verena Schöning use different approaches such as data mining, machine learning, and modelling and simulation studies in pharmacometrics to predict the effects of medication on diseases and thus inform clinical decision making. During the COVID-19 pandemic, their research directly contributed to improved patient care.

Read the interview...

Averting severe embolisms through artificial intelligence

Experts in laboratory medicine at Inselspital, Bern University Hospital, have developed a diagnostic prediction model in a long-term prospective multicenter study in collaboration with data scientists at the University of Bern, which provides treating physicians with an indicator to reliably diagnose HIT (heparin-induced thrombocytopenia) - a rare but life threatening complication for hospitalized patients after major surgery or severe inflammation. The diagnostic machine learning tool derived from this research in the field of laboratory medicine is unique in the world.

Read the media release...

Great Interest in the CAIM Research Symposium

The first Research Symposium of the Center for Artificial Intelligence in Medicine since its official opening in March 2021 was attended by more than 100 researchers from the Bernese ecosystem around the University and University Hospital. CAIM Director Raphael Sznitman was excited to witness the beginning of promising interdisciplinary scientific discussions in the wake of the two keynote speeches and during the networking breaks. "It is great to have such a vibrant community around AI research for healthcare here in Bern," he said.

Impressions CAIM Research Symposium 2022...

Amith Kamath and Charlotte Kern win CAIM Young Researcher Awards

The very translational research of PhD students Amith Kamath, ARTORG Center, University of Bern, and Charlotte Kern, Department of Clinical Pharmacology and Toxicology at the Inselspital, Bern University Hospital, has been recognized at the CAIM Research Symposium with one of the new CAIM Young Researcher Awards each. Awards were sponsored by the Bern Economic Development Agency with a total of CHF 1000. The research developed yet a bit further into a product (Amith Kamath) additionally received 16 hours worth of personalized entrepreneurial coaching by be-advanced.

Using interpretability to improve medical AI

The Medical Image Analysis lab at the ARTORG Center has just published a study where interpretability informed sample selection from a set of radiological images. The lab, which has focused on interpretability both as a means for more transparency in medical AI workings and as a tool to improve AI itself, reports that their novel sample selection approach based on graph analysis to identify informative samples in a multi-label setting improved model performance, learning rates, and robustness when compared to state-of-the-art Active Learning methods.

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CAIM Research Symposium

On 24 November, 8.30-13.30h CAIM will be presenting updates on its funded research projects as well as two invited keynotes on AI for medicine opportunities and challenges at the University of Bern main building top floor event venue (Kuppelsaal). Meet our Executive team, our ethics specialists and the people behind our initiative DAIM and exchange ideas during coffee breaks!

Learn more and register...

"Artificial Intelligence Advances Medicine"

Stavroula Mougiakakou, head of Artificial Intelligence in Health and Nutrition at the ARTORG Center, conducts applied fundamental research and with her team aims to close the gap between theory and practice. In the highly interdisciplinary and very competitive field of AI in medicine she sees a clear need for rigorous scientific testing that can assure approaches, tools and processes that can benefit users. At CAIM, she is Co-Responsible for Education and leads the master program "AI in Medicine".

Read the interview...

Less radiation with AI-enhanced PET

PET/CT can be used to clarify tumor diseases regarding their extension and therapy response, as well as to answer neurological and cardiological questions. The Bern University Hospital uses the world's most sensitive PET scanners with comparatively low radiation exposure. AI-supported image attenuation could further lower radiation and result in better imaging, as demonstrated by the Nuclear Medicine Department in Nature Communications.

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Maintaining a Mediterranean diet with your phone

Researchers at the ARTORG Center in collaboration with Oviva and with the Epidemiology, Biostatistics and Prevention Institute (EBPI), University of Zurich, have demonstrated that an AI system on a smartphone can effectively track whether a person is adhering to a Mediterranean diet (MD). MD can decrease the risk of non-communicable disease and prevent overweight and obesity. The feasibility study of the system was just published in Nature Scientific Reports.

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MADiMa 2022

The main scope of MADiMa2022 is to bring together researchers from the diverse fields of engineering, computer science and nutrition who investigate the use of information and communication technologies for better monitoring, assessment, and management of food intake.

More information and how to submit...

DAIM Lunch Talk

Dr. Lisa Koch, Institute for Ophthalmic Research, University of Tübingen, will present approaches "Towards Safe and Effective Medical Image Analysis Systems" at this lunchtime event in hybrid mode hosted by the ARTORG Center for Biomedical Engineering Research. This event runs under the umbrella of the initiative DAIM - Diversity for AI in Medicine. Participation is free. Please register.

Details and registration...

Bernese AI system receives FDA approval

An artificial intelligence system for brain tumor segmentation from multisequence MRI has received 510k clearance by the US American Food and Drug Administration on 29 September 2022. The system was developed by Prof. Dr. Mauricio Reyes and his team from the Medical Image Analysis lab, at the ARTORG Center and the Center of AI in Medicine, Univ. of Bern. Work on this important milestone by many talented PhD and Master students has started at the @MICCAI 2011 conference, receiving the 2016 MICCAI Young Scientist Publication Impact Award as well as the 2016 Ypsomed Innovation Award. It underlines the importance of Bernese research on a global scale.

Read the media release…

AI: Friend and helper of medical professionals?

AI is profoundly changing the healthcare system. This process poses various challenges for the medical profession. With the brochure "Artificial intelligence in everyday medical practice", the Swiss Medical Association FMH offers the medical profession an overview of AI in the medical environment. Ten demands raise awareness for the essential aspects of AI in medicine.

Read the FMH media release (German & French)...

Research Awards

CAIM launches its student research award! Apply from 6 September for best student project CAIM. Two winning projects will be announced at the CAIM Research Symposium on 24 November and will receive CHF 500 each.

Learn how to apply...

Smart Tools for Dexterity Home Training in Parkinson’s

Limited hand and finger movement due to Parkinson's disease can be counteracted to some degree with targeted physical therapy. To provide patients with individual training options at home, the Gerontechnology and Rehabilitation group at ARTORG Center has now evaluated the usability of two new interactive game sensor-based hand devices.

More about the study…

Making the most out of lab data

Every large hospital produces a wealth of data each day, holding the key to new insights into diseases and improved patient care. For machine learning and Big Data approaches to be utilized on this data, lab data needs to be FAIR: findable, accessible, interoperable, and reusable, propose a team of researchers from the Department of Clinical Chemistry at the Inselspital.

Read the study…

CAIM at Uni Bern Researchers’ Night

Save the Date!
Our Center will present itself to the broader public with an interactive discovery trip of what Artificial Intelligence means and does and how it is applied today (and tomorrow) in healthcare.

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Sensor-based early detection of age-related diseases from home

Researchers at the University of Bern and Inselspital, Bern University Hospital have demonstrated how sensors that record movement patterns could help detect health problems in the elderly, including old-age depression, risk of falls or cognitive impairment, at an early stage. In the future, this could help seniors to live a self-determined life at home for longer and relieve increasing pressure on the healthcare system.

Read the media release...

Interview with neurosurgeon Andreas Raabe

In his career, neurosurgeon and clinic director Andreas Raabe has seen major developments in medical technology that open unprecedented possibilities for interventions on the brain and spine. With a keen interest in physics, he develops new technologies for the operating room emerging from practice at Inselspital together with the ARTORG Center.

Read the interview...

Hypo- and Hyperglycemia Prediction

Blood glucose decompensations of hospitalized patients poses a frequent and significant risk for patient outcomes and safety. A team of data scientists and doctors from the University of Bern and Bern University Hospital have generated a broadly applicable multiclass classification model for predicting decompensation events from patients’ electronic health records to indicate necessary adjustments in monitoring or therapy.

Read the study...

Esther Brill

Esther Brill is a PhD student in cognitive neuroscience at the University Hospital of Old Age Psychiatry and Psychotherapy Bern. As a digital native she considers connecting technology and clinical work essential to fight ever more prevalent neurodegenerative diseases such as dementia through computerized cognition games.

Read the interview...

ARTORG Center to develop digital care assistant with QUMEA

Together with digital health startup QUMEA, the ARTORG Center for Biomedical Engineering Research, University of Bern and the Bern University of Applied Sciences (BFH) have started a project to enable hospital care teams and doctors to adapt and improve care via sensor-based technology. The project is funded by an Innosuisse grant of CHF 1.2 million under the impulse program Swiss Innovation Power.

Read the QUMEA media release...