Health, Medicine and Genomics

Artificial Intelligence (AI) has become a powerful and pervasive technology in recent years, influencing numerous aspects of our daily lives. We encounter AI through recommendation algorithms in online stores, voice-activated smartphone assistants, or the widespread use of technologies like ChatGPT. However, its rapid growth and integration into society raise complex questions and concerns among the public. Public opinions on AI vary widely; while some people are enthusiastic about its potential to revolutionize industries and enable breakthroughs in fields such as medicine, others fear that AI could lead to undesirable outcomes, such as a loss of human control or privacy. These views are often shaped by media narratives and the competing interests of different stakeholders, which play a significant role in influencing both public opinion and policy decisions.

Our team of scientists and communication experts aims to enhance the understanding of AI technologies among the Swiss people, with a particular focus on teenagers and female students, to create a positive societal impact. Building on the foundations of our previous project, NewsOnAI, we will expand beyond traditional media such as newspapers and employ diverse methods, including artistic performances and interactive exhibitions. We will design these activities to be highly interactive, encouraging active participation and dialogue. Activities will include themed theater plays that explore AI’s impact on everyday life, exhibitions where participants can interact with AI tools, and workshops specifically designed for teenagers and female students to discuss AI’s future role in society. Feedback collected will include real-time audience reactions, structured questionnaires, and focus group discussions, which will be analyzed to continuously refine and adapt our engagement strategies.

Our primary audience includes Swiss citizens interested in cultural activities, particularly teenagers who are keen to follow new trends. Additionally, we are committed to addressing gender aspects by designing content and activities that specifically appeal to female students. We aim to inspire and empower young women to take on more prominent roles in shaping the digital world, acknowledging that they have historically been underrepresented in these fields. As societal attention shifts toward greater inclusion, our project will contribute to fostering a more balanced and equitable digital future.

While many individuals in our target groups may lack in-depth technical knowledge of AI, they often encounter new AI products, companies, and social issues through various media channels, including newspapers and science fiction movies. As a result, they may be aware of recent developments but also susceptible to misunderstandings and controversies related to technologies such as ChatGPT, Elon Musk's brain-chip startup, and other emerging AI applications. It is crucial to recognize that media portrayal significantly influences public opinion on AI, both positively and negatively. Media creators, even if they are not experts in AI, often produce content that captures public attention, which high-profile figures, including entrepreneurs, CEOs, and politicians, may leverage to advance their agendas. This can sometimes lead to skewed public perceptions, whether intentionally or unintentionally. Given this landscape, it is essential for AI scientists to collaborate with media creators, providing evidence-based insights to ensure accurate and balanced information is shared with the public. Our project fosters such collaboration, ensuring that both the potential and limitations of AI are clearly communicated. By sharing our findings through diverse media outlets, we aim to reach a broad audience, extending beyond Switzerland. Furthermore, our proactive engagement efforts will foster dynamic, two-way communication between scientists and the public, using interactive methods in exhibitions and theater plays to engage teenagers and female students specifically. Analyzing the feedback from these initiatives will provide invaluable insights into public perspectives on emerging technologies. This understanding will guide scientists in pursuing research directions that effectively address societal concerns, demonstrating the tangible benefits of our project for both scientific advancement and societal well-being. We anticipate that our efforts will have a multiplying social impact over time, promoting informed public discourse and a deeper understanding of AI technologies.

Project DREAM aims to develop a scalable foundation model for multi-channel EEG signal analysis, spanning from basic polysomnography to high-density and intracranial recordings. The goal is to create a unified AI model capable of adapting to various EEG configurations while maintaining simplicity, interpretability, and computational efficiency. By leveraging convolutional neural networks and attention mechanisms, the model will extract robust features for diverse tasks, including sleep stage classification, epilepsy detection, and cognitive function assessment. Collaborations with leading hospitals will provide real-world datasets for validation, ensuring the model’s generalization. Ultimately, this project seeks to advance AI-driven biomedical signal processing, enhancing diagnostic and monitoring capabilities in neurology and sleep medicine.

This project aims to understand how evolution shapes animal physiology in response to prolonged exposure to juvenile undernutrition, whereby juvenile animals are forced to grow and develop in spite of chronic nutrient shortage. It will address four general questions:(1) In what way can evolution modify metabolism of a growing juvenile animal to alleviate consequences of undernutrition for Darwinian fitness?(2) What changes in allocation of metabolic resources does this adaptation entail?(3) To what degree are these changes mediated by genetic variants in 'master' genes with large effects on multiple aspects of the adaptation?(4) To what degree is evolution of tolerance to poor diet mediated by genetic variants in genes that regulate growth (i.e., the 'demand' for biomass building blocks) versus genes whose products catalyze and regulate their acquisition and allocation (i.e., the 'supply' side of growth)? The project will use lines of Drosophila melanogaster characterized by extraordinary genetically-based tolerance to larval undernutrition, a unique resource generated through >17 years of laboratory experimental evolution. To address the above questions, we will elucidate causal changes in growth regulation and metabolism underlying this highly polygenic and phenotypically complex evolutionary adaptation. First, we will quantify the rate of amino acid turnover and the allocation of resources to different components of biomass (proteins, triglycerides, glycogen, etc.) in the 'Selected' (malnutrition-tolerant) and 'Control' (unselected) larvae. Together with gene expression data, these data will be used to model the core metabolic network of Drosophila. This will generate testable predictions about differences in metabolic fluxes underlying malnutrition tolerance and identify nodes of the metabolic network critical for differential fluxes and resulting allocation patterns (question 1 and 2). It will also be used to evaluate the roles of nutrient supply versus demand on metabolic output in shaping the patterns of metabolic flux (question 4).Second, we will verify the contribution of a cis-regulatory variant in an ecdysone oxidase gene fiz to enhanced tolerance to undernutrition, and test whether the adaptive value of this variant is contingent on the presence of other elements of this complex adaptation. fiz emerged as a candidate with potential large effects on this adaptation (questions 3); it is thought to regulate growth by deactivating ecdysone, but this hypothesis appears incompatible with the direction of its effects on growth. To elucidate the effect of variation in fiz on ecdysteroid signaling we will study its effect on the abundance of different ecdysteroid species, identifying those most likely to mediate its growth-regulating effect. Third, we will combine the two above threads by investigating how fiz expression affects the rate of nutrient acquisition, metabolite abundance and the allocation of metabolic resources. Given that fiz is thought to act by modulating the demand for key metabolites, we will be able to ascertain to what degree increased demand for biomass building blocks has effects that propagate through metabolism and affect resource allocation question 4). To achieve these aims, we will combine experimental evolution with state-of-the art approaches, including genome editing, metabolomics, isotope tracing, high resolution mass spectroscopy and genome-scale flux balance analysis. This project will advance our understanding of a poorly understood and ecologically important evolutionary adaptation. It will also throw light on the broader fundamental question of how changes indifferent metabolic and regulatory elements interact to generate complex adaptations that enhance Darwinian fitness under conditions of environmental stress. Through novel application of recent experimental techniques and system-scale computational models the project will expand the boundaries of dissecting this complexity.

The algorithmic bias remains one of the key challenges for the wider applicability of Machine Learning (ML) in healthcare. Statistical modeling of natural phenomena has gained traction due to increased representation capacity and data availability. In medicine, particularly, the use of ML models has increased significantly in recent years, especially to support large scale screening, and diagnosis. However impactful, the study of demographic bias of newly developed or already deployed ML solutions in this domain remains largely unaddressed. This is particularly true in the medical imaging domain, where it remains challenging to associate demographic attributes with features. Out of the most recent results, the "impossibility of fairness" establishes some criteria for demographic impartiality cannot be reached simultaneously. Among other factors, the lack of raw data, in particular for intersections of minorities, is one of the greatest issues that remain unaddressed due to their challenging nature. This proposal addresses three important challenges in the domain of ML fairness for medical imaging: (i) Create novel ways to train ML models for medical imaging tasks, that can be automatically adjusted to become more useful (maximize performance), group or individually fair, (ii) Quantify fairness boundaries of ML models and associated development data, and finally, (iii) Build systems whose joint performance with humans in the decision loop is fair towards various individuals and demographic groups. To achieve these goals, we will develop a novel evaluation framework and loss functions that take into account model utility together with all aspects of demographic fairness one may wish to address. A generative framework, trained to isolate tunable demographic features, will provide large-scale data simulation covering minorities and intersections. We will then study fairness (safety) boundaries through a modified learning curve setup, analyzing and quantifying limits in both ML models and training data. Finally, we will study how humans-in-the-decision-loop affect the fairness of hybrid human-AI systems, and address post-deployment utility/fairness tuning by embedding weight coefficients directly into the trained model. The development of methods and tools to detect, mitigate, or remove bias will improve the safety of ML models deployed in healthcare. We expect our work will help define new operational boundaries for the responsible deployment of artificial intelligence tools.

The main objective of this project is to verify the feasibility of using innovative artificial intelligence algorithms to analyze vine based on vineyard images. The aim is to automatically extract the essential components and use them to recommend appropriate pruning.

The project provides an augmented dialogue tool that exploits verbal and non-verbal indices to improve interviews’ quality. Its main business application is to support HR interviews.

Nowadays, 1 in 59 children is diagnosed with autism spectrum disorders (ASD), which makes this condition one of the most prevalent neurodevelopmental disorders. The hereby project is grounded on the recognition that, on the one hand, early diagnosis at scale of autism in young children requires the development of tools for digital phenotyping and automated screening, through computer vision and Internet of Things sensing. On the other hand, current gold-standard approaches in autism are not intended to provide a precise quantitative estimate of ASD symptoms in children. We therefore aim to examine the potential of digital sensing to provide automated measures of the extended autism phenotype, for the purpose of stratifying autism subtypes in ways that would allow for precision medicine. Recent developments in digital sensing, big data and machine-learning have offered unforeseen opportunities for seamless sensing of body movement, social scene capture, and measure of object manipulation. Together, these tools are key for modeling social interactions and offer avenues for both improving screening and fine-grained characterization of autistic symptoms in young children. Despite considerable efforts invested to explore such automatic behavioral analysis, most studies in ASD digital phenotyping have been conducted on modest samples sizes, used mono-modal approaches, were focused on eliciting very specific behaviors by largely controlled prompts, and have suffered from technical difficulties in behavior sensing (view points, children population, image resolution for gaze). To address these limitations, we propose an interdisciplinary project combining the skills of experts in clinical research, engineering and computational social sciences in order to address these clinical, scientific, and technical challenges. It is grounded on the Geneva Autism Cohort consisting of young children with ASD and their age-matched typically developing peers, extensively assessed with gold standard standardized clinical and cognitive assessments, as well as neuroscience tools. Further, our preliminary results demonstrated that relying on a substantial dataset it is feasible to successfully train a deep neural network directly from on a global scene representation (people poses) to predict ASD with above 80% accuracy. This Sinergia proposal is set to stretch a giant leap forward, by investigating three key research directions. First, from a clinical research perspective, we will design digital tools for screening and automated profiling of autism phenotype. We will test these tools in a structured setting with well-established clinical protocol, as well as in a less structured environment (free play in day-care centers). Second, with Internet of Things (IoT) sensors, we will investigate the motor skills of very young children, through the integration of inertial and low-cost UWB indoor localization data. Additionally, we will develop a solution for the longitudinal monitoring of fine-grained motor skills development. Last but not least, our project is rooted in modern computational perception and machine learning. We will investigate novel deep learning and computer vision techniques by leveraging the availability of large behavioral and clinical annotation data. At the core of this effort, we will develop multimodal machine-learning methods and models for the analysis of motor and gaze coordination patterns which are at the core of ASD, and for ASD diagnosis and profiling with a focus towards interpretable models.