AI is changing the way we understand human health

This article is authored by Ravi Gupta, vice president, Bioinformatics, MedGenome Labs Ltd.

Advances in science are giving us an increasingly detailed understanding of the human body. Today, we can sequence DNA, analyse tissue at a molecular level, interpret medical images, and continuously monitor vital signs, all of which is changing how diseases are diagnosed, treatments are developed, and care is delivered. But the paradox at the heart of this progress is that our ability to generate biological data is outpacing our ability to interpret and translate that data into meaningful insights.

Artificial intelligence (Representative)

India faces an especially significant challenge. The country has roughly 64 doctors per 100,000 people, compared with a global average of nearly 150. With around 70% of the population living in rural areas and only an estimated 20,000–22,000 radiologists serving a population of 1.4 billion, the gap in access and capacity is significant. Meanwhile, global imaging volumes are growing by around 5% annually, as genomic and multi-omics technologies generate biological data at unprecedented scale and complexity.

The challenge, therefore, lies not in generating this data, but in processing, connecting and interpreting it quickly and efficiently enough to turn it into meaningful, actionable clinical insights. As the volume and complexity of health data continue to grow, executing this at scale is becoming increasingly challenging.

A pathology test can reveal cellular changes, a genomic test can uncover thousands of genetic variants, and a medical scan can reveal subtle patterns, that may be difficult to distinguish from noise. In some cases, a patient’s risk only becomes apparent when insights from these biological data are considered together. Computational biology, the use of statistics and algorithms to analyse and interpret biological data, offers a way to work through this complexity by analysing datasets and identifying relationships that would be difficult to process manually, helping translate this complex data into more meaningful clinical insights. However, conventional approaches can be time-consuming and prone to error due to manual processing, particularly when working with large, complex and heterogeneous datasets. The limited availability of specialists in every field further constrains the ability to cater to the growing volume of biological data efficiently.

The growing need for faster, more accurate and scalable health care has created an opportunity for emerging technologies like Artificial Intelligence (AI) and machine learning to complement computational biology and enable faster, and efficient analysis. Today, AI models are being integrated into computational biology where specialist expertise is limited or unevenly distributed, AI can be used to support high-volume preliminary analysis and flag cases that require closer clinical review. AI models can augment the analysis of medical images, genomic sequences and multi-omics data helping scientists and clinicians to detect abnormalities, classify disease, estimate risk and support biological discovery.

In Indian multi-site studies, automated reporting with human verification has nearly halved radiology turnaround times demonstrating how technology can strengthen clinical expertise and improve efficiency. AI-based tools are being used to support interpretation of chest X-rays and detect early signs of vision-threatening retinal abnormalities. In genomic testing, AI is employed to analyse large volumes of sequencing data and build tools that can identify specific genetic variants from vast amounts of data., These systems complement clinicians and help them gain a more comprehensive understanding of disease biology, saving time to prioritize cases that require closer review, and supporting clinical judgement with data-driven insights.

Multimodal AI models are now being developed to bring together imaging, genomics, electronic health records and wearable data within a common computational framework, potentially revealing relationships that remain difficult to see when each source is examined independently. This integrated view could gradually change clinical interpretation, moving from asking what an individual test reveals to understanding what the combined evidence says about a patient's biology.

AI has real potential to improve how health care is delivered, but it needs to be introduced carefully, with rigorous validation and human oversight remaining essential if innovation is to translate into safe, meaningful outcomes.

The change is not just from data to faster answers but from fragmented information to meaningful insights that can enable better health care decisions.

(The views expressed are personal)

This article is authored by Ravi Gupta, vice president, Bioinformatics, MedGenome Labs Ltd.