Transforming Healthcare: How Generative AI is Reshaping Life Sciences

The life sciences industry has always been at the forefront of medical innovation, striving to improve healthcare through the development of effective drugs and therapies. With the rapid advancements in artificial intelligence (AI), clinical and commercial pharma sectors can now benefit from AI-powered technologies like ChatGPT.

In this blog post, we’ll explore the key factors life sciences organizations can consider while scaling generative AI in a responsible manner.

“We are entering an era of generative AI that will become the DNA of everything we do across the life sciences ecosystem. It has evolved into a general-purpose technology.”

Life sciences, drug research, discovery, and clinical trials can benefit significantly from generative AI. Based on our experience and understanding, we have developed several use cases across the molecule market continuum, as depicted below:

“Generative AI has the potential to deliver 20%-40% productivity improvement for the life sciences industry, delivering advanced therapies to patients faster, more effectively, and economically.”

The life sciences ecosystem has developed a library already applicable to each of its markets & processes. Hence, discussing use case(s) is not the major differentiator or enabler.

To deliver the value of generative AI, life sciences companies must consider addressing the following key factors to deliver full potential:

Data Bias: The quality of data and curating the right kind of data becomes foundational to implementing a successful generative AI strategy. A lack of clean data or inherently biased data can lead to “AI Hallucination” and unintended consequences.

Generative AI can play a crucial role in drug discovery by analyzing vast research data, identifying patterns, and suggesting promising drug candidates.

“Whether it is drug discovery, clinical trials, or patient recruitment, a biased conclusion can have a devastating impact on the patient community.”

Right Use Cases: Large Language Models(s) [LLMs] are built on neural networks. It is sometimes very difficult to explain how a result of a conclusion is derived from LLMs, thereby affecting the transparency and reasoning behind findings or recommendations. It is, therefore, incumbent upon life sciences companies to build “responsible AI processes,” including curation, development, and data testing.

ChatGPT can assist potential compliance risks in the highly regulated pharma industry and offer recommendations to mitigate them, contributing to a safer and more efficient work environment. It is also essential to consider the lack of explainability behind the recommendations.

Security: Life science firms must develop measures to protect data and AI infrastructure from cyber security risks. The data risks considering the possibility of opening AI to a broader ecosystem, are significantly huge.

Legal & Compliance Alignment:  Leveraging generative AI can lead to several legal, ethical, and compliance questions. Hence as a part of building “responsible AI” capabilities, it is crucial to establish a framework that ensures legal and compliance alignment.

For example, Italy approved ChatGPT only in April 2023 after the company met the demands of its regulators.

While dealing with life sciences data, legal and compliance issues should be considered as soon as a use case is identified.

“Providing ChatGPT-based employee support in Italy would have been illegal in Jan 2023 while the rest of world would not have had a challenge with it.”

Continuous Improvement: Generative AI is not a one-time investment. It requires continuous improvement, investment, and updates, which can be expensive. As life sciences companies scale AI, they should look at building foundational teams that understand the implications of data, security, and legal aspects within their eco-system.

Environmental Sustainability: A MIT Technology Review reported that training just one AI model can emit more than 626,00 pounds of carbon dioxide equivalent –nearly five times the lifetime emissions of an average American car. All enterprises should consider mitigating the environmental impacts while still innovating.

Let’s spread smiles to the patient community.

From streamlining customer support and drug discovery to enabling personalized marketing and ensuring regulatory compliance, generative AI can revolutionize the industry and bring about a new era of innovation and efficiency.

“Spreading Smiles to the Patient Community” is the mission of the life sciences team at Hexaware. We realize it is not easy to fully fathom the impact of generative AI. Engaging in the exchange of ideas and notes and maintaining an ongoing conversation is the sole means by which we can deliver the complete value of this technology to patients who require care and support.