|
The Indian life sciences sector increasingly sees artificial intelligence (AI) as indispensable to propelling innovation across the value chain from drug discovery and clinical development to patient care. It is emerging as a strategic enabler accelerating insights, and enabling integrated decision-making. This connected approach is helping organisations move from individual AI projects to enterprise-wide transformation, creating greater value for researchers, healthcare providers, and patients.
Abhishek Rungta, founder & CEO, Indus Net Technologies, said that AI is beginning to change life sciences at almost every point where data, scientific decision-making and human experience intersect. Drug discovery may have been one of its earliest high-profile applications, but the opportunity now extends much further. Pharmaceutical and healthcare organisations are exploring AI across clinical development, regulatory workflows, medical information, enterprise operations and patient engagement. Enterprise systems support commercial and operational teams.
Even as drug discovery is the only starting point, it is one of the clearest examples of AI's potential. AI and machine learning can help researchers analyse complex biological and chemical datasets, identify potential targets, predict molecular properties and prioritise promising compounds for further investigation. For pharmaceutical organisations, that distinction matters. The real opportunity is not replacing scientific judgement, he added.
We are also seeing that clinical development is becoming more data-driven. Once a promising candidate enters clinical development, a different set of challenges emerges. AI can support teams in identifying potentially suitable trial participants, analysing clinical datasets, detecting patterns associated with trial risks and improving elements of trial planning. In fact, Generative AI creates another layer of opportunity because so much clinical information exists in documents and text. Large language models can help organise, summarise and retrieve information across complex datasets and documentation, Rungta told Pharmabiz in an email.
Of course, the bigger challenge is connecting the life sciences data environment. This is where the AI conversation becomes less about individual tools and more about enterprise architecture. A pharmaceutical organisation may have research data in one environment, clinical information in another, regulatory documents in separate repositories and patient or healthcare professional interactions flowing through entirely different platforms. Introducing AI without addressing those connections can simply create another layer of fragmentation. The challenge is no longer simply to build an AI application. It is to connect AI and data engineering with cloud infrastructure, application engineering, cybersecurity and digital experience so that intelligence can move safely across workflows, he noted.
The industry is moving from individual AI projects to enabling connected transformation across the entire life sciences value chain. Clinical development shows how it can support information-intensive workflows. Regulatory operations demonstrate the importance of traceability and governance. Patient engagement shows why technology ultimately needs to translate into a better human experience. Connecting these areas requires more than an AI model. It requires data engineering, enterprise integration, cloud infrastructure, cybersecurity, application development and digital experience to work as parts of the same architecture. For life sciences organisations, that may be the most important shift taking place, said Rungta.
|