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Can artificial intelligence help reduce health inequities? Drawing on experiences from the MSc in Applied Digital Health, Sergej Kucenko reflects on responsible innovation, sociotechnical thinking in healthcare and why technology alone cannot solve unequal health outcomes.

Photo of Sergej Kucenko at matriculation

About the author:

Sergej Kucenko is an MSc in Applied Digital Health student. He holds a BSc in Health Sciences. Alongside his studies, he has worked on responsible digital health research, with a particular interest in health information generated by large language models, the assessment of AI trustworthiness and applications of virtual reality to improve health outcomes. 

 

Over the past 20 years, the life expectancy gap in Germany has increased. The gap in life expectancy between people in the wealthiest living areas and those with the highest socioeconomic deprivation is 4.3 years for women and 7.2 years for men.  

Health inequity is a real and severe issue. Growing up in Hamburg with people from highly contrasting living areas, I have seen such unfair and avoidable differences in health conditions, behaviours and health knowledge. 

AI and the promise of reducing health inequities 

Some people say that artificial intelligence (AI) will reduce health inequities. Large language models such as ChatGPT can provide easily accessible, plain-language and personalised health information and advice. AI can also use data to identify people at high risk of developing a disease or missing care, enabling earlier and more targeted interventions for those who might otherwise not receive the support they need. The list of AI tools that aim to address public health issues goes on and on. 

When I tell family members, friends and acquaintances about my studies, common questions are, ‘What do you think about AI? Are you pro-AI? Will it solve our problems?  

I do believe that AI tools can help reduce health problems. I also believe that there are many ways new technologies can improve healthcare access for those who need it the most. But: technology cannot be the solution in itself when there are deeper healthcare system-specific and social issues at play. This is exactly what the MSc in Applied Digital Health taught me more about – particularly how digital innovation, healthcare systems and social inequalities interact in practice 

Before I came to Oxford, my work on responsible AI in healthcare research introduced me to the tension between rapid innovation and risk mitigation, the danger that those most in need of care may benefit the least, and ethical challenges such as algorithmic bias. My time on the MSc has not only helped deepen my understanding of these issues, but also highlighted potential ways that I can contribute to responsible digital health innovation. If digital technologies are implemented prematurely without accounting for barriers such as digital literacy, they can unintentionally deepen existing health inequities. It has been very valuable that the Applied Digital Health course extensively covered the complexity of implementing digital technologies in healthcare settings.  

Why sociotechnical innovation matters 

If you asked me about one term I associate with my degree, it is sociotechnical innovation – the idea that successful technologies depend not only on the technology itself, but also on the people, organisations and systems around them. For example, in the ‘Foundations in Digital Health’ module, Dr Chrysanthi Papoutsi taught us about the NASSS framework she co-developed, as it helps evaluate health and care technology adoption, abandonment, scale-up, spread and sustainability by analysing complexity across seven domains, including the technology, people, organisation and wider system. One particularly useful experience was applying the NASSS framework in two assignments to analyse the challenges of implementing an insomnia therapy app and app-based smartphone-use metadata collection for depression monitoring in older adolescents’ in German primary care. 

Another learning highlight was the ‘User Focused Design and the Lifecycle of Digital Health Innovation’ module. I was aware that the design of digital health technologies can either help address or reinforce health inequities, particularly when affected communities are not sufficiently involved in the process and when designers’ biases influence technology design. But I had no design education and experience before this course. In my assignment, I applied a structured process that emphasised the consideration of user needs in designing a mobile application for bulimia management. I also learned how to prototype an app in Figma and reflected on how to prevent potential accessibility barriers in my app, which improved my understanding of user focused design and made me interested in studying how digital health technology design can influence health inequities. 

Preparing for research and future impact 

Engaging with classmates from diverse disciplines and countries, learning from guest speakers who shared how their research translates into real-world impact, and receiving extensive guidance from the MSc teaching team all strengthened my preparation for a career focused on responsible innovation and reducing health inequities. Especially the conversations I had with Dr Francesca Dakin and Dr Lei Clifton about Large Language Models (LLMs) use as health information sources and the opportunity to observe how they approach research and generate ideas have supported my development as a researcher.

I entered the Applied Digital Health master’s hoping that it would help me prepare for a PhD, and thanks to the encouragement for my academic research ambitions I received, I applied and recently received an offer for the DPhil in Primary Health Care at Oxford. I plan to study public perceptions and use of LLMs as health information sources and the connection to primary healthcare seeking, as LLMs are not just an AI tool but also relate to healthcare access questions. The MSc in Applied Digital Health at Oxford gave me the knowledge, perspective and research environment to turn my initial interest in LLM use for healthcare into the foundation of my doctoral research.  

 

 

Opinions expressed are those of the author/s and not of the University of Oxford. Readers' comments will be moderated - see our guidelines for further information.

 

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