Perception of AI in medical research among medical students and doctors in Nigeria: impact of a structured AI training program using a pre-post quasi-experimental design
This pre-post quasi-experimental study demonstrates that a structured two-day online training program significantly improved knowledge, self-efficacy, and other perception domains regarding AI in medical research among Nigerian medical students and doctors, although ethical concerns remained unchanged and warrant greater emphasis in future interventions.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
In the modern world of medicine, the tools for discovery are changing faster than ever before. Among the most powerful of these new tools is artificial intelligence, a form of computer intelligence that can learn from vast amounts of information to find patterns, make predictions, and assist with complex tasks. In medical research, this technology can help scientists analyze huge datasets, draft manuscripts, and spot errors that a tired human eye might miss. However, knowing that a tool exists is very different from knowing how to use it effectively. For doctors and medical students, especially in regions where resources are sometimes scarce, the gap between awareness and actual skill can be wide. Many people understand that artificial intelligence is important, but few have received formal instruction on how to apply it to their own scientific work. This disconnect raises a critical question: can a short, focused training program actually change how medical professionals think about and use these powerful tools?
To answer this, a team of researchers in Nigeria organized a structured learning event for medical students and doctors across the country. They wanted to see if a well-designed workshop could shift perceptions, boost confidence, and improve knowledge regarding the use of artificial intelligence in medical research. The study involved 135 participants, ranging from students still in medical school to experienced consultants, drawn from universities and hospitals in both the northern and southern parts of the nation. Before the training began, the researchers asked the participants to share their thoughts on several key areas: how much they knew about the technology, how useful they believed it to be, what obstacles they felt stood in their way, and what ethical worries they held.
The results showed that before the training, the participants had a clear but uneven understanding of the subject. They generally believed that artificial intelligence would save time and improve the quality of their research, yet they admitted to knowing very little about how to actually use it. The most common feeling was a lack of specific knowledge; for instance, while many understood what artificial intelligence meant in a general healthcare sense, very few could explain how the underlying computer programs were trained or validated. Furthermore, most participants had never received any formal education on the topic, relying instead on informal, self-taught methods. When asked about the risks of not learning these skills, they expressed concern about falling behind their peers, but their confidence in their own ability to learn and apply the tools was mixed.
The researchers then delivered a two-day online workshop designed to fill these gaps. The sessions covered the basics of the technology, its practical applications in research, the ethical rules that govern its use, and hands-on demonstrations of specific tools that could help with literature reviews and data analysis. Immediately after the workshop, the participants were asked the same questions again to measure any changes in their thinking. The impact was immediate and significant. The training successfully raised the participants' knowledge levels, turning a vague awareness into a much clearer understanding of how these tools work. Their confidence grew as well; they felt more capable of using the technology in their own work and more likely to take action to learn more. The barriers they had previously felt, such as a lack of technical skills or institutional support, were perceived as less daunting after the instruction.
Interestingly, not everything changed. The participants' concerns about the ethics of using artificial intelligence remained steady. Before the training, they were already worried about issues like data privacy, the possibility of computers making up facts, and who would be responsible if an error occurred. After the training, these concerns did not go away, nor did they become more intense. This suggests that while a short course can teach people how to use a tool, it does not necessarily resolve deep-seated ethical questions, which may require longer, more sustained discussion. The participants also reported high satisfaction with the workshop, with the vast majority saying the content was relevant, well-organized, and worth recommending to colleagues.
The study concludes that while Nigerian medical students and doctors are aware of the potential of artificial intelligence, they lack the specific skills to use it in research. A structured, practical training program can quickly close this gap, improving both knowledge and confidence. However, the researchers note that ethical concerns are a different matter; they are deeply held and do not shift easily with a single lesson. The findings suggest that future educational efforts should focus on teaching practical skills while also dedicating more time to the complex ethical discussions that surround the use of artificial intelligence in medicine. By bridging the gap between awareness and application, such training can help medical professionals in Nigeria and beyond harness the full power of these new technologies for the benefit of patient care and scientific discovery.
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