Stephen Chanda

Please share a little about yourself and what sparked your interest in your field of research. 

I am an Epidemiologist at the Zambia National Public Health Institute, working as a Mortality Surveillance Coordinator. My research focuses on mortality surveillance, verbal autopsy, cause-of-death attribution, civil registration and vital statistics strengthening, and the use of epidemiological models to improve public health decision-making. My interest in this field was sparked by the persistent challenge of under-reported deaths and poorly defined causes of death in many low- and middle-income settings. I became interested in how better data systems, verbal autopsy, and analytical methods can help countries generate more accurate mortality evidence for planning, outbreak response, and health system strengthening.

How did AI become part of your research journey? 

I have been using AI to help with several of my other admin tasks and begun to appreciate very quickly how AI is very good at reviewing large amounts of data, identifying patterns in these data and providing innovative ways of displaying this information. When I critically looked at the central problem affecting my day-to-day work, of mortality surveillance, I realised that the fundamental problem of cause of death attribution is similar to the other tasks that I was using AI for. That is, I have several sources of information about the death, but no single cause of death. So, I begun to explore how I could use AI to sift through all the available data about the death, explore how I can incorperate other relevant data sources, and then have AI classify the cause of death based on all the available sources of information

Can you describe a moment when AI made a meaningful difference in your research? 

As a non-first language english speaker, communicating complex ideas is often a daunting task. When I started using AI to proofread my scientific writing and communication, I was able to articulate my ideas more clearly. This reduced the time for back and forth between myself and my supervisors that was previously spent clarifying ideas.

What challenges or pitfalls have you encountered along the way? 

Garbage in Garbage out. You need to use AI within the limitations of its development and its capabilities. AI should not be used when the boundaries of a topic are not familiar to the user, as it will hallucinate and produce seemingly good answers. If you can’t verify the output, do not use AI.

What have you learned about using AI responsibly and effectively? 

I view the AI tools I use as a very smart but naive assistant who I can give supervised tasks, but I have to check everything after its done before I pass it off. It shouldn’t be used as an all-knowing supervisor

What excites you most about the future of AI in health research?

Coming from Clinical practice, the more information I have available to me about a patient’s health the better clinical decisions I can make affecting that patient’s health. In a similar way, I think AIs superpower is its ability to ingest multiple data streams to make more informed decisions.. As our systems get better at collecting more information, I think the possibility of using AI to bring all this data together is an exciting proposition.

Jodie Layman Lemphane

Adriaan Meyer

Please share a little about yourself and what sparked your interest in your field of research. 

I am currently a PhD candidate in the Division of Medical Physiology, Faculty of Medicine and Health Sciences, at Stellenbosch University. While working as a Medical Laboratory Scientist, my curiosity about medical-based questions motivated me to pursue my postgraduate studies. During my MSc degrees, I became more exposed to research and quickly realised how much I enjoyed it. I was fascinated by the process of asking questions, challenging assumptions, and using research to move closer to the ground truth. I believe research provides a meaningful way for me to contribute to the healthcare sector and society, while also allowing me to pursue a career that gives me a sense of purpose.

How did AI become part of your research journey? 

My introduction to Data Science did not start with Computer Science, but rather through biomedical research. During my Bachelors and later master's research, I became increasingly exposed to sophisticated datasets and realised the importance of improving my statistical skills. This motivated me to explore Data Science more broadly. I later started learning basic Python programming and machine learning concepts, including completing the IBM Data Science Professional Certificate

Can you describe a moment when AI made a meaningful difference in your research? 

My project is still in its early phase, but I am hoping to develop an 18-month cardiometabolic risk-prediction model for people living with HIV who are receiving antiretroviral therapy. This model will be based on a range of biochemical, hormonal, immunological, and anthropometric variables, with the most statistically relevant markers being used for the prediction model. If successful, this work could help identify individuals at increased risk of cardiometabolic disease earlier, allowing for a more personalised approach to treatment, monitoring, and prevention.

What challenges or pitfalls have you encountered along the way? 

One of the main challenges I have experienced with AI/ML is that it is highly dependent on the quality and structure of the data. In biomedical research, datasets can be complex, often containing missing values, outliers, small sample sizes, and variables that may overlap biologically. I have also learned that model interpretation is a major challenge, as a model may identify patterns that are statistically useful but not necessarily clinically meaningful

What have you learned about using AI responsibly and effectively? 

I believe that we should be careful when incorporating AI into the healthcare sector, especially when there is limited understanding of how these tools work. Although AI has great potential, there remains a knowledge gap among many researchers regarding the principles behind machine-learning models, their limitations, and when they should or should not be used. This is especially important in healthcare, where model outputs may influence research interpretation, clinical decision-making, or patient care. These tools can be extremely valuable, but they must be used with caution and should rather serve as guidance to support decision-making, instead of replacing clinicians and scientists.

What excites you most about the future of AI in health research? 

There's still so much potential in using these tools in the healthcare sector, especially in research. I believe the next few months to years will be interesting, as we begin to see how AI/ML tools are applied more widely in health research. For this reason, I think we should embrace these tools rather than avoid them.