Mental Health, AI and Data Science: Key Insights from the MQ and DATAMIND Meeting

Last week, the MQ and DATAMIND Data Science Meeting & Workshop took place in London. Here is an account from early career researcher, Noah J Marshall, about their experiences.

Working Between Mental Health and Data Science

As an ESRC Scholar at the University of Bath, my research focuses on the development, evaluation, and implementation of generative AI (GenAI) tools for mental health support. More broadly, I am interested in both the opportunities these technologies create and the ethical, clinical, and societal challenges that come with them (Marshall et al., 2025).

Working in this area has naturally meant embracing interdisciplinarity as a central part of my research. My collaborators span a wide range of fields – from sociology and computer science to clinical psychology and public health. Over the years, learning to communicate across these disciplines has been both rewarding and challenging. Different fields often approach the same problem in completely different ways, whether through language, theory, methodology, or even what counts as 'evidence'.

Although the work is incredibly fulfilling, working between fields can also be surprisingly isolating. In my work, research communities often exist in silos – either highly technical AI and data science spaces, or more traditional mental health research environments. One of the biggest challenges throughout this journey has been finding communities that genuinely sit between these disciplines.

A Day of Data Science, Mental Health, and Big Questions

That was one of the reasons the MQ and DATAMIND Data Science April 2026 Meeting stood out to me. It felt like a rare opportunity to be in a room with people asking similar interdisciplinary questions about AI, data, healthcare, and mental health, all at the same time.

Held at Deutsche Bank’s offices in central London, the event brought together researchers, clinicians, NHS professionals, and people with lived experience from across the UK. From the start of the day, there was a real sense that this was not simply a conversation about technology, but about the future directions of mental health research more broadly.

What I appreciated most was the range of perspectives across the talks and discussions. The programme moved across topics including early identification of mental health difficulties in young people, wearable technologies, electronic health records, fairness in machine learning, and the growing role of large language models in healthcare. While the research itself varied hugely, many of the same underlying questions kept resurfacing throughout the day: How should AI be used in mental healthcare? What are the risks? Who benefits? And how do we make sure these systems are safe, ethical, and genuinely useful?

A particular highlight for me was Dr. Kezhi Li’s presentation on HopeBot and the use of large language models in mental health screening. A lot of what he discussed reflected challenges I have encountered in my own research – particularly the difficulty of working in a space where public interest and technological progress are moving more quickly than evidence, regulation, and policy. It was reassuring to hear similar tensions being discussed so openly by others working in the field.

The panel discussion on responsible AI was another standout moment. What made the conversation especially interesting was that it did not fall into the usual extremes of either overhyping AI or dismissing it entirely. Instead, the discussion focused on the difficult middle ground: questions around trust, bias, patient safety, transparency, implementation within healthcare systems, and the importance of lived experience perspectives in shaping these technologies. It felt like a much more honest conversation about what responsible innovation actually looks like in practice.

Presenting my own research during the early career researcher session was also a real highlight. The conversations afterwards were thoughtful, challenging, and genuinely interdisciplinary in a way that is often difficult to find elsewhere. By the end of the day, what stood out most was not just the quality of the research itself, but the shared recognition that these problems are too complex to be solved from within a single discipline alone.

Why Spaces Like This Matter

For early career researchers, these spaces are particularly important. Presenting work can sometimes feel intimidating, especially when working across multiple disciplines, but the atmosphere throughout the event was supportive and genuinely collaborative. Some of the most valuable parts of the day were the informal conversations between sessions – hearing how other researchers are approaching similar problems, navigating similar tensions, and thinking about the future of the field.

I left the meeting feeling more connected to this research community and more confident that mental health, AI, and data science research is moving in a thoughtful direction. As these technologies continue to develop, spaces that encourage critical, interdisciplinary, and public-facing discussion and collaboration will only become more important.

References

Marshall, N.J., Loades, M.E., Jacobs, C. et al. Integrating Artificial Intelligence in Youth Mental Health Care: Advances, Challenges, and Future Directions. Curr Treat Options Psych 12, 11 (2025). https://doi.org/10.1007/s40501-025-00348-x