From Discovering New Medicines to Detecting Diseases: How AI Is Transforming Science and Healthcare

Introduction: Can AI Help Us Solve Some of Science’s Biggest Challenges?

Imagine a scientist trying to understand a disease. They have thousands of gene-expression measurements, complex protein structures, and hundreds of chemical compounds to investigate. Analysing all this information manually can take a great deal of time. Now imagine using artificial intelligence (AI) to help identify patterns, predict molecular structures, and select promising compounds for further research. This is no longer just an idea. AI is already being used in scientific research and in certain healthcare applications. From helping scientists understand proteins to supporting doctors during medical procedures, AI is changing how we approach complex problems. However, its real value lies not simply in making predictions, but in helping researchers and healthcare professionals make better-informed decisions.

Let’s explore how this technology works, where it is being used, and what it can realistically achieve.

1. What Is Artificial Intelligence?

Artificial intelligence refers to computer systems designed to perform tasks such as recognising patterns, making predictions, or generating new content.

Three important approaches are:

1. Machine Learning (ML): Learns patterns from existing data to make predictions or classifications.
2. Deep Learning (DL): Uses multi-layered neural networks to learn complex patterns in images, biological sequences, and other data.
3. Generative AI: Generates new content, including text, code, images, and proposed molecular structures.

For example, a machine-learning model trained on gene-expression data may help researchers distinguish between samples from different disease groups. However, a prediction is only as useful as the data, model design, and validation behind it.

2. AI in Scientific Research: Understanding the Biology of Life

Modern biological research generates enormous amounts of information. Scientists study genes, proteins, cells, and chemical molecules to understand how living systems work.

AI can help analyse these datasets and identify patterns that might otherwise be difficult to detect.

Identifying Genes Associated with Diseases

Researchers can use machine learning and statistical analysis to investigate differences in gene expression between healthy and diseased tissues. For example, in a breast cancer research project, scientists may compare gene-expression profiles from tumour samples and normal tissue. They can identify differentially expressed genes and investigate whether selected genes could help distinguish between sample groups. These genes may become candidate biomarkers for further investigation. It is important to remember that a gene showing different expression levels is not automatically a reliable diagnostic biomarker. Its usefulness must be tested using independent datasets and, where appropriate, laboratory and clinical studies.

Predicting Protein Structures and Molecular Interactions

Proteins carry out essential functions in living organisms. Their three-dimensional structures influence how they work and interact with other molecules. Understanding these structures can help scientists investigate disease mechanisms and explore potential drug targets.

One important example of AI in this area is AlphaFold 3.

Real-World Example 1: AlphaFold 3

AlphaFold 3 is an AI model developed to predict the structures of biological molecules and their interactions. Its research paper was published in Nature in May 2024. The model can predict structures of complexes involving proteins, DNA, RNA, small molecules, ions, and certain modified molecular components.

Why is this useful?

Suppose a researcher is studying a protein associated with a disease and wants to investigate how a small molecule might interact with it. A predicted molecular structure can help the researcher develop a hypothesis and decide what to investigate next. Such predictions can support computational analysis and guide further experiments. However, AlphaFold 3 does not automatically identify a safe or effective medicine. A predicted structure is not proof that a molecule will bind as expected, produce a desired biological effect, or work safely in humans.

This example shows how AI can help scientists investigate complex biological questions while keeping experimental validation at the centre of research.

3. AI in Healthcare: Helping Detect Diseases

Healthcare professionals work with medical images, laboratory results, patient histories, and other complex information. AI can help analyse selected types of data and highlight patterns that may need closer examination.

Some applications include:

1. Medical imaging: Analysing X-rays, CT scans, MRI scans, and other medical images.
2. Eye care: Supporting the assessment of retinal images for signs of certain eye diseases.
3. Cancer research: Helping identify patterns in tumour images and molecular data.
4. Patient monitoring: Analysing health measurements to identify patterns or potential warning signs.

These systems are designed for specific tasks. Their performance depends on the quality of the data, the intended use, and the evidence supporting their application.

Real-World Example 2: GI Genius and Colonoscopy

A colonoscopy allows a healthcare professional to examine the inside of the large intestine. During this procedure, identifying abnormal tissue, including polyps, is important. GI Genius is an AI-enabled system designed to assist endoscopists by analysing endoscopic video and highlighting areas that may contain colorectal lesions. In April 2021, the US Food and Drug Administration (FDA) granted De Novo marketing authorisation to GI Genius for its defined intended use.

The system helps bring potentially relevant areas to the clinician’s attention. It does not replace the endoscopist’s judgement or independently guarantee that every lesion will be detected.

This is a useful example of AI moving beyond the research laboratory into a defined clinical application. The FDA also maintains a public list of AI-enabled medical devices authorised for marketing in the United States. This list demonstrates the growing range of AI applications in healthcare, although it is not a complete list of every system in use worldwide.

4. Can AI Help Scientists Discover New Medicines?

Developing a new medicine is a complex process. Researchers must understand the disease, identify possible targets, investigate chemical compounds, assess safety, and conduct extensive testing.

AI can support several stages of this process.

For example, researchers may use computational methods to:

1. Identify potential disease-associated targets.
2. Screen large collections of chemical compounds.
3. Predict selected molecular properties.
4. Prioritise promising candidates for further testing.
5. Investigate possible interactions between proteins and small molecules.
6. Help design experiments to evaluate promising candidates.

Consider a researcher studying an enzyme involved in a bacterial infection. After identifying the enzyme as a potential drug target, the researcher could use computational screening to shortlist compounds for further investigation. This may help prioritise experiments and reduce the number of compounds that need to be examined initially in the laboratory. However, a high molecular docking score or a promising AI prediction does not prove that a compound will work in living cells or patients. Compounds still need appropriate experimental testing, toxicity assessment, and further development.

A 2026 perspective published in Nature Reviews Drug Discovery highlights the need to evaluate AI methods by their contribution to meaningful drug-development decisions and clinical outcomes not just by their performance in computational tests.

The key lesson is that AI can support drug discovery, but developing a medicine still requires strong biological evidence and rigorous testing.

5. AI in Genomics and Personalised Medicine

People differ in their genetic makeup, disease biology, and responses to treatment. Researchers are investigating how genomic and other biological data can help explain these differences.

AI can help analyse large datasets containing genetic variants, gene-expression profiles, clinical measurements, and other information. For example, a research team might investigate whether particular gene-expression patterns are associated with different cancer subtypes or treatment responses. Such findings may contribute to the development of more personalised approaches to healthcare. However, a model trained on one population may not perform equally well in another. Researchers must test its reliability in appropriate datasets and populations before considering its use in healthcare decisions.

6. How Does AI Support the Drug Discovery Process?

Scientific discoveries and healthcare applications are connected, but moving from research findings to a potential treatment requires several steps.

Consider this simplified drug-discovery workflow:

AI-Assisted Drug Discovery Workflow

Disease Research → Identify a Potential Drug Target → Analyse Candidate Molecules → Select Promising Compounds → Laboratory Testing → Further Preclinical and Clinical Evaluation

AI may help at several stages by analysing data and prioritising candidates.

But a computational prediction alone cannot establish whether a target is biologically relevant, whether a compound is safe, or whether a treatment benefits patients. Similarly, an AI system that performs well in a research dataset must be evaluated carefully before its results can be relied upon in a real healthcare setting.

7. What Are the Limitations of AI in Science and Healthcare?

AI offers valuable opportunities, but several challenges must be addressed.

Data quality: Incomplete, inconsistent, or inaccurate data can lead to unreliable predictions.

Bias: If training data do not represent the intended population, a model may perform poorly for some groups.

Limited interpretability: Some AI systems make predictions that are difficult to explain, which can complicate scientific interpretation and clinical decision-making.

Prediction versus proof: A predicted protein structure, a potential biomarker, or a shortlisted drug candidate requires further evaluation.

Privacy and security: Genomic and medical data can be sensitive and must be handled responsibly.

Validation and accountability: AI systems need appropriate testing, oversight, and monitoring. Healthcare professionals must understand the intended role and limitations of the tools they use.

The World Health Organization’s 2026 report on ethics review and oversight of AI-related health research highlights the importance of ethical oversight, fairness, accountability, and the responsible use of health-related data.

These considerations are essential to ensure that scientific progress does not come at the cost of patient safety, privacy, or public trust.

8. What Does the Future Hold?

AI research continues to advance across molecular biology, genomics, medical imaging, and drug discovery.

Some promising directions include:

1. Combining genomic, imaging, and clinical information to study complex diseases.
2. Improving predictions of protein structures and molecular interactions.
3. Developing better methods for identifying and evaluating potential drug candidates.
4. Supporting more efficient scientific workflows.
5. Designing AI systems that are reliable, transparent, and suitable for their intended applications.

Future progress will depend not only on more powerful algorithms but also on high-quality data, independent validation, careful experiments, and responsible implementation.

Conclusion: AI Can Accelerate Research, but Evidence Still Matters

Artificial intelligence is changing how scientists investigate biological systems, explore potential medicines, and analyse medical information. AlphaFold 3 demonstrates how AI can support research into molecular structures and interactions. GI Genius shows how an AI-enabled system can assist a clinician during a specific medical procedure. These examples illustrate the potential of AI when it is applied to clearly defined problems and evaluated carefully. For students and researchers, learning AI alongside biology, statistics, and experimental methods can open opportunities in bioinformatics, computational biology, biotechnology, and biomedical research.

At Rebioin AI, we aim to help learners understand these technologies and explore how computational approaches can support real scientific problems.

Interested in AI, bioinformatics, and computational biology? Explore Rebioin AI to build your understanding and discover how computational tools can support scientific research.

References

1. Abramson, J., Adler, J., Dunger, J., et al. (2024). Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature, 630, 493–500. https://doi.org/10.1038/s41586-024-07487-w
2. Bender, A., Thomas, M. C., Scannell, J. W., et al. (2026). Artificial intelligence in drug discovery — what it is, where we stand and the path forward. Nature Reviews Drug Discovery. Published 7 August 2026. https://doi.org/10.1038/s41573-026-01496-2
3. World Health Organization. (2026). Artificial intelligence-related health research: ethics review and oversight. https://www.who.int/publications/i/item/9789240124073
4. US Food and Drug Administration. (2021). GI Genius: De Novo classification decision, DEN200055. https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/denovo.cfm?id=DEN200055
5. US Food and Drug Administration. List of Artificial Intelligence-Enabled Medical Devices. https://www.fda.gov/medical-devices/artificial-intelligence-enabled-medical-devices/list-artificial-intelligence-enabled-medical-devices