A conversation with Henri Max Deda
In a world where scientific progress holds the key to solving humanity’s most pressing challenges, Henri Max Deda, co-founder of scienceOS.ai, shares his journey from molecular biologist to science communicator and entrepreneur. Speaking with Jo Havemann, Deda reflects on how his diverse background and a pivotal lecture on the “Network Effect” inspired him to co-create a platform that empowers researchers to collaborate and innovate more effectively. The realization that vast amounts of unpublished research data lead to redundant R&D spending became a driving force behind scienceOS, a tool initially designed for life science researchers but now serving a broader scientific community.
At its core, scienceOS leverages artificial intelligence to streamline essential but time-consuming tasks like literature reviews, reference management, and data visualization. Henri explains that while literature research is a cornerstone of the scientific process, it rarely generates new insights and often slows progress. By integrating advanced AI capabilities—such as search algorithms, PDF parsing, and citation analysis—scienceOS transforms this bottleneck into an opportunity for efficiency and discovery. Researchers can now ask complex questions, explore citation networks, analyze PDF content, and even draft texts by organizing and interacting with thousands of documents. The platform also fosters collaboration through shared projects, making it a comprehensive tool for modern research workflows.
One of the standout features of scienceOS is its “AI Actions,” customizable automated prompts that adapt to specific research needs. For instance, users can sort references based on curated examples or create entirely new workflows tailored to their projects. Despite its powerful capabilities, scienceOS prioritizes user privacy, ensuring that all data—whether chats, uploads, or outputs—remains secure and is not used for training AI models. This commitment to transparency extends to the platform’s reliability measures, which include citation markers, source lists, and full-text highlighting to verify the origins of information.
As the adoption of AI in research grows, scienceOS addresses concerns around academic integrity and responsible AI use. By providing tools like disclosure templates and fostering open discussions, the platform aims to destigmatize AI while making ethical practices more accessible. Partnerships with non-profits and the global scientific community further amplify its impact, offering discounts, webinars, and opportunities for feedback to organizations that share its mission of driving positive change.
Reflecting on the journey since launching scienceOS, Henri notes that user feedback has been instrumental in shaping the platform. Regular interviews with researchers reveal a common desire for simplicity and intuitiveness, guiding the team to refine their tools continually. As an EU-based company, scienceOS also emphasizes data privacy, proving that cutting-edge AI solutions can respect user rights.
For researchers eager to accelerate their workflows and collaborate on groundbreaking projects, scienceOS offers a transformative approach to scientific inquiry. Explore the platform today and join a growing community dedicated to advancing science for the betterment of all.
Visit scienceOS.ai to learn more and start your journey.
References selected for you with assistance from scienceOS
These articles provide insights into the integration of AI in research workflows, ethical considerations, and the automation of literature reviews, aligning closely with the themes discussed in the podcast.
Gridach, M et al. (2025). Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions. ArXiv, abs/2503.08979. https://doi.org/10.48550/arXiv.2503.08979
This article explores how AI systems are transforming scientific workflows, including literature reviews, hypothesis generation, and data analysis. It also addresses challenges like system reliability and ethical concerns, emphasizing the importance of human-AI collaboration.
Ofori-Boateng, R et al. (2024). Towards the automation of systematic reviews using natural language processing, machine learning, and deep learning: a comprehensive review. Artif. Intell. Rev., 57, 200. https://doi.org/10.1007/s10462-024-10844-w
This comprehensive review discusses AI techniques for automating systematic reviews, including search, screening, and data extraction. It highlights the potential of AI to streamline research processes while identifying gaps and challenges in current methodologies.
Runcan, R et al. (2025). Ethical AI in Social Sciences Research: Are We Gatekeepers or Revolutionaries? Societies. https://doi.org/10.3390/soc15030062
This study examines the ethical dilemmas posed by AI in research, focusing on issues like governance, bias, and transparency. It calls for proactive ethical stewardship to ensure responsible AI use in academia.
Omar, A S et al. (2025). Leveraging AI and Automation in Research Project Planning and Execution: A Systematic Literature Review. International Journal of Research and Scientific Innovation. https://doi.org/10.51244/ijrsi.2025.121500044p
This article reviews how AI and automation tools enhance research project management, from predictive accuracy to resource allocation, while also addressing challenges like algorithmic bias and accessibility.
Tomczyk, P et al. (2024). AI meets academia: transforming systematic literature reviews. EuroMed Journal of Business. https://doi.org/10.1108/emjb-03-2024-0055
This study synthesizes the role of AI in systematic literature reviews, emphasizing efficiency, methodological quality, and human-machine collaboration. It proposes a conceptual model for integrating AI into research workflows.