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Artificial intelligence (AI) has become essential to advancing medical research and healthcare delivery. AI’s ability to process and analyse large datasets swiftly and accurately makes it a crucial tool for disease research, diagnosis, and treatment. This transformative impact is evident across various medical applications, highlighting AI’s potential to revolutionise healthcare.
One prominent example of AI’s application in medicine is its use in malaria research. Anto Satriyo Nugroho, the Research Centre for Artificial Intelligence and Cyber Security (PRKAKS) head at The National Research and Innovation Agency (BRIN), shared insights into this innovative approach during a PRKAKS webinar. He explained that AI is employed in microscopic imaging data analysis to detect malaria. “Using a thin patch of a malaria patient’s blood observed under a microscope, AI can help identify plasmodia within the blood cells,” he stated. This method leverages advanced digital imaging techniques to detect the presence of the malaria parasite, facilitating quicker and more accurate diagnoses.
Anto Satriyo Nugroho, the head of the PRKAKS at BRIN, shared insights into using AI for malaria research. At a PRKAKS webinar, he explained how AI is employed in microscopic imaging data analysis. “Using a thin patch of a malaria patient’s blood observed under a microscope, AI can help identify plasmodia within the blood cells,” he stated. This method leverages advanced imaging techniques to detect the presence of the malaria parasite, facilitating quicker and more accurate diagnoses.
However, Anto highlighted several challenges in this research. One major issue is the poor quality of microscopic images obtained from the field, which can hinder accurate analysis. Additionally, there is a quantitative limitation due to the restricted involvement of doctors or other potential users in the research and writing process. Despite these obstacles, the integration of AI in analysing microscopic images represents a significant advancement in malaria research.
AI’s potential extends beyond malaria research. Toto Haryanto, a lecturer from the Computer Science department at the Bogor Agricultural Institute, discussed AI’s role in cancer detection. He explained that AI could utilise data to generalise models for diagnosing diseases. “The approach is image-based,” Toto said. “This image analysis relies on extracting information based on the color, shape, and texture of the image object, which is crucial for distinguishing between different varieties.”By employing histopathological imaging, AI can identify abnormal body tissues in patients suspected of having cancer. This process involves the gold standard biopsy, where Hematoxylin and Eosin staining are used to differentiate cancerous cells.
In another significant application, Danang Waluyo, a young researcher at the Vaccine and Drug Research Centre-BRIN, employed deep learning methods to categorise images and videos in his research on microbial diversity. Similarly, M Rodhi Supriyadi, the first engineer at PRKAKS-BRIN, used AI to classify frog images using the InceptionV3 model. Danang emphasised the importance of microbial diversity in drug discovery, noting that microbiologists, biochemists, chemists, and natural material experts must collaborate closely to identify promising active compounds from microbes.
Rodhi further elaborated on the potential of the Cladosporium genus, known for producing compounds with anti-cancer, antimicrobial, and antiviral properties. “The type of machine learning that uses neural networks contains many hidden layers with the capacity to automatically represent data or properties and its capacity for transferring learning,” Rodhi explained. This advanced AI technique allows for the efficient classification and analysis of complex biological data, leading to more effective and targeted medical treatments.
The integration of AI in medical research and diagnostics is revolutionising the field. From malaria and cancer detection to microbial diversity studies, AI is an invaluable tool, enabling researchers to analyse vast amounts of data with unprecedented accuracy and speed. As these technologies continue to evolve, they hold the potential to significantly improve patient outcomes and advance our understanding of various diseases.
