Gabriela Penelopé Carolus

New researchers encounter various tools as they begin their academic journey. You might have already learned about AI tools. Alternatively, you might be overwhelmed by the volume of AI tools and question their utility. These questions can leave you in the dark about which to select or if you should abide by the rumours or fears of not incorporating another tool into your research project. 

Fear not, simply put, Artificial Intelligence (AI) is changing scientific discovery by automating data analysis, hypothesis testing, and pattern recognition in fields such as astronomy, healthcare, materials science, and bioinformatics. Machine learning (ML) and generative adversarial networks are speeding up research and creating new opportunities for innovation. AI techniques in materials characterisation are improving efficiency and accuracy, potentially addressing reproducibility challenges in scientific research. In doing so, it adds expert knowledge to AI models, improving their ability to predict future scientific discoveries and inventions. Data-driven AI/ML innovations can speed up scientific discoveries by dealing with challenges such as high system complexity, large search space, incomplete knowledge, and small datasets.

Given the numerous advantages, let’s delve into the use of a specific tool-Generative Artificial Intelligence (AI). This tool, increasingly embraced by early career researchers, holds significant value and credibility in education and scientific applications. In this blog, I will share my insights on its use in Public Health research and highlight its potential to advance public health innovations.

Value vs reputation

Artificial Intelligence (AI) in scientific research raises valid concerns, including potential errors, ethical issues, and misconduct. The reputation of AI being negative stems from the fact that there are risks associated with AI-generated content, which has the potential to compromise scientific integrity. Furthermore, AI models may convey biases and false information. Therefore, as an early career researcher, it is crucial to integrate AI responsibly to maximise its potential without compromising scientific rigour. While there are concerns about AI’s impact on research, addressing these challenges alongside the exciting possibilities AI brings to one’s research is essential.

Scientific Use case

The value of this tool lies in its versatility. Scientists apply Artificial Intelligence (AI) across a wide range of disciplines, including chemistry, biology, medicine, engineering, and computer science. In the medical field, AI aids in diagnostics, treatment planning, and outcome prediction. Computer scientists use AI for network intrusion detection and game development. Additionally, engineers integrate AI into defence services, smart homes, and autonomous vehicles. These areas utilise various AI techniques, with artificial neural networks being the most common. Other techniques include fuzzy expert systems, evolutionary algorithms, and hybrid intelligent systems. The widespread integration of AI has enhanced efficiency and quality across diverse domains.

AI use in public health

In my field of interest, Public Health, I discovered that AI applications include spatial modelling, risk prediction, disease forecasting, and health diagnosis. Despite challenges like limited infrastructure and ethical considerations, AI can revolutionise African healthcare and research, particularly in disease surveillance, diagnostics, health communication, knowledge translation, health literacy/education and treatment optimisation. The integration into health informatics holds promise for improving public health outcomes across the continent, with implementation varying across regions due to differences in technology adoption.

Ensuring AI accuracy through scientific validation

AI is a powerful tool, but it’s not without its limitations. Ensuring AI accuracy in public health research involves modernising data governance, addressing workforce skills gaps, and setting up strategic partnerships while considering ethical implications. Combining AI with human expertise can improve diagnostic accuracy, but challenges like algorithmic bias and generalizability issues persist. Robust clinical evaluation and regulatory frameworks are essential for evidence generation and patient safety. Scientific validation is crucial, and caution is needed when using AI for diagnostic purposes. As such, I would caution graduate students to recognise these limitations and urge them to communicate the uncertainties of this tool when using AI systems to make decisions. 

AI Learning in my public health research

I am committed to improving my ability in data-driven AI/ML innovations for public health research. As I move forward, I am aware of the ethical implications of AI applications. My aim is to advocate for transparency, reproducibility, and health equity while contributing to the progression of research methodologies for the development of AI-powered applications.

I am establishing strategic partnerships with experts across various fields, such as AI/ML, human-computer interaction, design science, medicine, and software engineering, while also actively engaging with the public. This effort has been instrumental in helping me recognize the capabilities and limitations of working with an emerging tool. As a burgeoning researcher, I am mindful of both AI’s strengths and weaknesses. I am dedicated to embracing collaborative and transdisciplinary approaches through continuous learning to fully leverage the potential of these tools in academic environments.

Tshedimoso Makhene

Virology is more than just the study of viruses—it’s a lens through which we’ve revolutionized our understanding of life itself. From uncovering the hidden mechanisms that drive disease to shaping modern medicine and public health, virology stands at the forefront of scientific discovery. Nature Portfolio describes virology as “the scientific discipline concerned with the study of the biology of viruses and viral diseases, including the distribution, biochemistry, physiology, molecular biology, ecology, evolution, and clinical aspects of viruses.” But where does virology stem from? And how did I end up here? 

Where it all started

“Infectious disease is one of the few genuine adventures left in the world. The dragons are all dead, and the lance grows rusty in the chimney corner,” writes microbiologist Hans Zinsser in his book Rats, Lice and History, as he reflects on his life as a researcher in infectious disease. Although the book was published in 1935, viruses were discovered as early as 1796 by the likes of Edward Jenner, who made significant contributions to the eradication of smallpox. 

In 1892, Russian botanist Dmitri Ivanovsky discovered that the tobacco mosaic disease could pass through filters blocking bacteria, indicating a smaller organism, but he did not fully understand its implications. The breakthrough came in 1898 when the Dutch microbiologist Martinus Beijerinck replicated Ivanovsky’s experiment and concluded that the infectious agent was a “contagium vivum fluidum” or “contagious living fluid.” He coined the term “virus” (from the Latin word for poison) to describe this mysterious entity. Beijerinck’s work laid the foundation for the field of virology, although the nature of viruses remained elusive for many years.

The growth of virology

The true nature of viruses was not revealed until the 20th century with the advent of electron microscopy.

Frederick Twort and Félix d’Herelle discovered bacteriophages in the 1910s, viruses that infect bacteria, which helped broaden the field of virology. Bacteriophages became essential tools in molecular biology, leading to significant discoveries, including the understanding of genetic material and the development of recombinant DNA technology.

The tobacco mosaic virus (TMV) was one of the first viruses to be observed, and in 1935, American biochemist Wendell Stanley successfully crystallized TMV, proving that viruses were not just fluids but had a physical structure. Stanley’s work earned him the Nobel Prize in Chemistry in 1946 and solidified the understanding of viruses as distinct entities, paving the way for modern virology.

recognition and impact

Virology gained substantial recognition with the discovery of the role viruses play in human diseases. The 20th century saw the identification of viruses responsible for numerous illnesses, such as yellow fever, polio, influenza, smallpox, and later HIV/AIDS. The development of vaccines, particularly for smallpox, which led to its eradication in 1980, highlighted the critical importance of virology in public health.

The molecular revolution of the mid-20th century further advanced virology. The discovery of DNA as the genetic material and the subsequent understanding of RNA viruses opened new avenues for research. Techniques such as polymerase chain reaction (PCR) and next-generation sequencing have revolutionized the study of viral genetics and epidemiology.

In recent decades, the emergence of new viruses like SARS, MERS, and SARS-CoV-2 (the cause of COVID-19) has underscored the global significance of virology. These outbreaks have highlighted the need for ongoing research, surveillance, and preparedness to address viral threats.

My journey with virology

My journey with virology began when I had just completed my Honours degree in Chemical Pathology and was unemployed. I spent months seeking employment opportunities until the University of the Witwatersrand gave me an opportunity that I could have never imagined would result in me writing this blog post.

I was hired as an intern under a government program and was placed in the Virology Department. I didn’t know much about virology (except what I had learned in my second year in microbiology), but I was acquainted with people who were not only experts in the field but were passionate about virology. I went from not knowing much about virology and molecular lab techniques to demonstrating extraction techniques to masters students. Yes, MASTERS STUDENTS! How? I am just a small girl who holds an Honours degree! In Chemical Pathology.

It can only be destiny.

During the last two months of my tenure, I was given the opportunity to apply for a scholarship program that would take me through my masters. I wrote my proposal for the scholarship and my masters in two weeks and spent my days extracting 96 samples all in one go. This meant long hours in the lab and sleepless nights. 

Now? I mentor and supervise students both at masters and honours level. I participate in programs that will allow me to continue learning and stay at the forefront of my field. 

And I love virology.