BENGALURU: The pharma industry is at the forefront of innovation and change globally. However, while artificial intelligence has the potential to reinvent the industry, there are also numerous challenges that impact the development and adoption of advanced technologies.
Manish Menon, office managing principal, ZS, a management consulting and technology firm, told TNIE that one of the primary issues with AI-led innovation is the lack of quality data due to fragmented data sources.
“The complexity due to fragmented data sources makes it challenging to understand interdependencies, eliminate redundancies and local data sets. While India presents a promising AI market, implementation in a country as populous becomes harder since there are issues with scalability and availability of skill sets. To combat these technical challenges, model selection, algorithm choice, model training and validation are essential steps,” he said.
As healthcare systems and patient data evolve, equal emphasis needs to be placed on ethical considerations to prevent inequitable outcomes for different patient groups. Regular updates and continuous monitoring of the model’s performance in real-world settings are essential for maintaining accuracy and reliability, Menon added.
On the medical side of pharma, he said AI has been heavily involved in determining drug efficacy and assisting in publishing scientific papers. For instance, in case of drug discovery, if the researcher is working with 100 molecules that are likely to have a commercial success, or will pass through the phase one, phase two, phase three, phase four trials, AI platforms have the potential to narrow it down to 20-4 molecules, he explained.
“AI helps look at a lot of patient level data, real-world data, and be able to simulate those molecules and the impact on different tumour types which improve the throughput dramatically. AI has found critical use cases in clinical trial planning and execution. AI tools have been developed to identify which hospitals are ideal for conducting trials by providing the right patient demographics, based on patient data and disease prevalence. Technology has improved the operational efficiency in the pharma sector across the value chain. When a drug is launched, AI is used to improve its management, from a commercial perspective,” Menon said.
According to him, advanced technologies, especially Generative AI, is the top driver of transforming the pharma and healthcare sector in the areas such as outpacing digital innovation, rising costs, drug pricing and reimbursement constraints. ZS reports point out that GenAI can mine unstructured data which makes up about 80% of all healthcare data and enables companies to do things they could not do before, such as pulling insights from far-flung, unstructured data at scale and generating new content.
Drug development is also an area where technology like GenAI can drive efficiencies through in-silico methods. “From diagnosis and patient care to new and effective drugs for a population as large as India’s, GenAI is pivotal in delivering better healthcare outcomes for all,” Menon said.