3 Mind-Blowing Insights from our Webinar on Generative AI in Pharma

generative AI in pharma
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“I attended your webinar, and I didn’t multitask as much as I thought I would,” a clinical trial manager of a global pharma company told me this when I called her to get feedback on our recent webinar on Exploring Generative AI in Pharma: Unlocking Possibilities Responsibly.

The webinar stood out as a remarkable exception in a realm where webinars often teeter on the edge of monotony. Featuring Novartis, a pharmaceutical industry leader, and Gramener this webinar explored the promising realm of generative AI in pharma.

Two brilliant minds, Anand S., the CEO at Gramener, and Ashwini Mathur, Head of Global Drug Development at Novartis, shared their insights, setting the stage for a unique intersection of generative AI and drug development.

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Key Takeaways from the Webinar

1. Expansive Potential of Gen AI in Pharma

Ashwini shared that Gen AI can simplify regulatory compliance through tailored questionnaires and streamlining processes.

Further, he emphasized that it is pivotal in creating scientific reports and documents, serving as a research starting point. In drug discovery, it aids in finding new candidates, optimizing compounds, predicting interactions, and identifying novel combinations.

Curious to know the latest disruption in the pharma industry? Check out our article on use cases of AI in drug discovery.

Gen AI also contributes to drug formulation, enhancing bioavailability, stability, and patient compliance while predicting adverse effects to reduce testing time and costs. Personalized medicine is achievable through tailored drug therapies. In drug manufacturing, AI lowers costs and maintains product quality, and in repurposing drugs, it expedites treatments.

Additionally, AI identifies biomarkers, analyzes patient data, optimizes clinical trials, and refines drug pricing and market access strategies by analyzing market data and pricing structures. These applications collectively hold significant promise for the pharmaceutical sector’s advancement.

2. Clinical AI vs. Admin AI

The webinar took an exciting turn when moderator Nutan Bhattiprolu (VP-Consulting, Gramener) initiated a discussion thread on clinical AI and admin AI.

Clinical AI and Admin AI represent two distinct facets of AI in healthcare. Clinical AI focuses on improving patient care and medical decision-making. It encompasses applications like diagnostic algorithms, treatment recommendations, and predictive analytics to aid healthcare providers in delivering more accurate and personalized care.

On the other hand, Admin AI focuses on enhancing the administrative and operational aspects of healthcare organizations. It includes tasks such as billing and coding automation, appointment scheduling optimization, and resource allocation to streamline administrative workflows and improve the overall efficiency of healthcare systems. Both Clinical AI and Admin AI have the potential to revolutionize the healthcare industry, ultimately leading to better patient outcomes and more efficient healthcare operations.

Anand highlighted the significance of framing specific prompts for Large Language Models (LLMs) like generative AI. He stressed the importance of segregating prompts for LLMs, search engines, and platforms like Google to ensure accurate responses.

Apart from discussing the positive impact of Generative AI in pharma, the panel shared concern for limitations, including hallucinations, which generate wrong answers while responding to prompts.

3. Gen AI and Hallucinations

Gen AI tools aim to predict word sequences matching user inputs. However, they lack logical reasoning and the ability to identify factual inconsistencies. This can lead to hallucinations, where AI deviates from the intended path to provide satisfying but potentially inaccurate responses. Addressing such issues is crucial to unlock the full potential of gen AI, particularly in sectors like pharma.

In conclusion, our webinar also offers innovative solutions that can revolutionize drug development, compliance, and patient care. While challenges exist, the potential for transformative impact is undeniable, making responsible exploration of generative AI imperative for the pharma industry’s future growth.

We recently published a blog on LLM Hallucinations and how to mitigate them. It covers how LLMs can be trained to produce unbiased and wrong information.

We’ll Help You Find the Best Gen AI Use Case in Your Company

Gramener empowers pharma companies to leverage Gen AI with its full potential. It includes producing synthetic medical data that retains crucial statistical characteristics of actual patient data while safeguarding patient privacy. This synthetic data proves invaluable for training AI algorithms without infringing on patient confidentiality. We helped one of the leading pharma companies to anonymize patient data during clinical report submission phases.

Our Latest Gen AI Use Cases

Get a view of our prominent Generative AI use cases to accelerate compliant pharma content 7 advanced sales training.

Moreover, we can empower businesses to build Generative AI-driven conversational chatbots as virtual healthcare assistants, offering support with medical guidance, appointment coordination, and personalized health suggestions. This, in turn, elevates patient involvement and accessibility to healthcare services.

Interested in knowing more about Generative AI solutions? We’re giving one free discovery call to chat and help you find out the best use case of Generative AI in your company.

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