The pharmaceutical industry is increasingly adopting AI and LLMs (like ChatGPT) to enhance efficiency, speed up R&D, assist in drug discovery, and revolutionize commercialization endeavors. Leading pharmaceutical firms are either acquiring or forging partnerships with AI enterprises, with estimated AI expenditures expected to hit $3 billion by 2025. We’ve compiled an exhaustive list of use cases, sourced from top-tier media articles.
In this blog, we explore the top 5 trending AI applications related to commercialization and clinical trials in Pharma. We’ve reviewed and analyzed recent pharma articles on AI, offering insights into the most influential applications Pharma is employing. The original articles are cited for those wanting a deeper dive into these use cases.
1. Real-time Medical Updates for Patients and Physicians & Improved Adherence
Through chatbots powered by LLMs, companies can deliver personalized health advice and address frequent questions regarding medications and treatments. This aids healthcare professionals in making informed decisions regarding treatment alternatives, subsequently enhancing patient outcomes. Moreover, LLMs can be instrumental in elevating patient education and adherence.
For instance, Pfizer’s digital assistants facilitate easier access to medical information for both patients and healthcare professionals. Pfizer’s Medibot supplies HCPs with specific details (like stability of temperature-sensitive medicines), ensuring well-informed healthcare choices. Pfizer’s Fabi provides immediate answers on drug availability and information about Pfizer’s products, streamlining patients’ access to essential medical information.
2. Precision Marketing and Sales
By leveraging AI insights, pharma companies can make data-driven decisions on whom to target, where those targets are and what would be the most effective method of targeting them.
Integrating AI in sales and marketing not only distinguishes these firms from their rivals but also guarantees that healthcare providers and patients obtain the most pertinent medical information, customized to their specific needs. This article from Forbes delves into how AI empowers corporations to refine sales tactics and craft personalized sales pitches for augmented efficacy.
3. Efficiency/Regulatory Modernization
In a sector known for stringent regulatory scrutiny, AI emerges as an invaluable asset, automating tasks and shaping standards. Pharma companies can refine regulatory intelligence monitoring by incorporating a semi-automated methodology. Traditionally, regulatory teams had to sift through agency websites manually for updates, a labor-intensive task. By adopting NLP and Large Language Models, this information collection and summarization can be semi-automated. Such amalgamation amplifies the team’s competencies, allowing them to pinpoint essential concerns, deadlines, events, and regulatory judgments with heightened efficiency, culminating in informed decision-making.
4. Pharmacovigilance and Drug Safety
AI-powered systems possess the capability to oversee and examine real-world data, pinpointing adverse drug responses and safety concerns.
With continuous surveillance of healthcare databases, social media, and other platforms,
AI can notify pharma corporations of potential issues. Through thorough analysis of components, effects, and outcomes, AI can guide these companies towards resolutions before making hefty investments in R&D
5. Patient Recruitment for Clinical Trials
The AI models can use past and current data to make performance predictions that could enable users to optimize clinical trial operations.
For example, Novartis uses Nerve Live platform, which applies analytics and machine learning to clinical trial functions, such as enhancing resource management, determining the optimal location for drug trials, and recruiting the most suitable patients at the right time..
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