Studies on digital adoption in healthcare companies have shown them to be enthusiastic with the uptake of blockchain and IoT, but slow in manufacturing and supply chain resulting in limited outcomes. A survey by Cognizant found that while 71% of life sciences organisations had planned manufacturing as the primary focus for digital innovation, only 10% had moved beyond planning to implementation. These findings highlight the importance of following through tech investments with robust execution plans.
With tech access being available to all companies, only those firms who move with speed and adapt their processes to changes in technology, market needs and regulatory requirements would be able to stay ahead of their competition. Recognising this, many organisations have started inducting AI in their business models to create compelling value propositions for their customers.
From the year 2010 onwards, AI and machine learning have started to get integrated into the drug discovery and healthcare systems. Companies such as Pfizer and Moderna could develop Covid-19 vaccines with the help of mRNA platforms. AI is supporting scientists in sifting through large datasets to identify new drug targets and predict molecule-target interactions. Traditional drug discovery takes 10-12 years or more and involves huge investments. AI helps in accelerating the process and reducing costs.
Further with the explosion of data through imaging, real world data, biomarker data and genomics, use of AI has become extremely helpful in studying and analysing complex data. Generative AI is being used to design molecules and predict properties. In clinical trials, AI is helpful in selecting appropriate patient cohorts, predicting drop-outs and optimising the overall trial timelines and operations. It is also helping in mining real world data to identify patterns and side-effects for different population segments.
There are many Indian startups that are building businesses on the strength of AI applied to omics/systems‐biology domain. Proteomica, the spinoff from the Indian Institute of Technology Bombay, is an example of venture that is blending AI with omics; it specialises in developing digital health solutions for personalised, evidence based wellness utilising protemics and multi-omics. Sravathi AI is another Indian startup that is working on the continuum from target discovery through to drug design in the systems biology/omics value chain. Vgenomics is using AI along with genomic data to accelerate diagnosis and target discovery aimed at rare diseases.
Pathology is an important part of diagnostics. Use of AI has been improving the speed, throughput and accuracy in pathology that help with treatment pathways. Ibex AI provides diagnostic solutions that help pathologists analyse tissue slides for cancer efficiently. A number of devices, telehealth and wearables have been introduced and adopted by customers. The extensive use of AI in diagnostics would help developing countries like India with cost effective wearables and devices and treatment for hitherto unaddressed diseases.
The potential for further AI integration is significant in the entire value chain of drug discovery, manufacturing processes, supply chain and in the device design and diagnostics support systems. However, the stakeholders engaged with designing and implementing AI-driven solutions recognise that the quality and access to data is complex, data may not be standardised. The methods used by AI models for arriving at decisions have to be made transparent as they concern the health and safety of consumers.
Regulatory approvals would be required for new AI models and they need to be also validated through appropriate channels. Ethical concerns, bias creep and patient data privacy are concerns that are yet to be addressed by all the stakeholders concerned while building AI models.
While there is a huge potential for deployment of AI in life sciences in India, there are still challenges of data infrastructure, interoperability and regulations that require urgent attention.