AI Use Cases in Life Sciences You Can Start Implementing

Ask anyone about what's trending in their industry, and one of the replies will be AI or Gen AI. And if you belong to the Healthcare and Life Sciences industry, this term becomes more crucial.  

Because life science companies can earn $5-7 billion dollars in value from using artificial intelligence (AI). 

But it's not that simple. Life Sciences Gen AI Use Cases comes with its own set of challenges. For example, if the model makes any mistake, imagine the potential harm it can cause a patient. And what about the laws and ethical obligations around patient data privacy? What if there is a data breach? 

There is a long list of questions. So, in this guide, you will discover the practical use cases of Gen AI. The guide will help you decide how you can implement Gen AI in your organization. Plus, you will see examples of existing life sciences organizations using Gen AI successfully! 

Let's first start with understanding what Gen AI is. 

What is Gen AI? 

Think of it like how you learned to speak, write, or create. Gen AI works similarly. It is trained on vast amounts of data, such as written text, images, or audio. This “learning phase” allows Gen AI to recognize patterns and relationships in the data. Afterward, it can create new content (written text, producing images, or synthesizing audio.) The key idea is that it doesn't copy; instead, it learns from existing content to create something new with similar characteristics.  

What is Generative AI in Life Sciences 

Gen AI is a valuable tool in the HCLS industry. It can help organizations generate consumer insights, create targeted marketing materials, and transform healthcare interactions. Moreover, it also offers solutions for research, diagnostics, and patient engagement. 

However, like any technology, AI in Life Sciences has its limitations. One issue is the occurrence of hallucinations (where the AI generates incorrect or misleading information). Additionally, emerging threats such as prompt injection attacks—where malicious actors can manipulate the AI to bypass controls—pose significant challenges. 

So, Generative AI in Life Sciences is an enhancement tool rather than a replacement for human oversight. For example, it can help customer service representatives respond better to patient inquiries. However, you cannot 100% rely on AI in Life Sciences for critical tasks such as diagnosing a heart attack. Mistakes like these could result in severe consequences if not supervised by human professionals. 

Remember, only licensed professionals can practice medicine in regulated fields like medicine. And Gen AI, for now, is far from holding a license. 

So, how can you use Gen AI in life sciences to avoid these challenges? Let's find out. 

Practical Life Sciences GenAI Use Cases 

There are many life sciences GenAI use cases. It depends on your organization's needs and which you want to implement. However, research HCLS companies using gen AI for maximum functions are seeing the most impact. So, check out these use cases to find out where you can implement Gen AI. 

Use case 1: Automated Content Creation 

Benefit: 30% Increase in Patient Engagement 

If you face the challenge of creating personalized content, Gen AI is the one for you. 

Instead of manually creating personalized content, which can take hours, Gen AI steps in to handle it. 

Automating content creation leads to a 55% reduction in commercial operation costs! And it can also boost your patient conversions by 45% as well. How? 

The first AI Use Cases in Life Sciences is through Plain Language Summaries (PLS). PLS are AI-generated summaries that help non-expert audiences, like patients, understand complex clinical trial results. The second is through regulatory and scientific content creation. Gen AI speeds up the creation of regulatory documents, clinical reports, and other crucial medical content. 

Use case 2: Intelligent Study Deliverable Authoring 

Benefit: 25% efficiency boost 

We all know that drafting clinical study reports (CSRs) can be tedious and time-consuming. But what if you could automate most of that work? Gen AI makes it happen by pulling from existing documents and automating report creation. No more endless rework or manual drafting. This speeds things up and frees you to focus on higher-value tasks. From a company standpoint, it means fewer delays and lower costs. With this Life Sciences GenAI Use Cases, you can use the vast amount of clinical data you already have. 

Example 

Including Gen AI in regulatory document creation proved transformative for a leading biopharma company. The company needed to give an average of 50 submissions annually. And to streamline the authoring of Clinical Study Reports (CSRs), they used Gen AI. 

Result 25% efficiency boost, plus $13 million recurring annual savings. 

Challenge in automating content generation in life sciences and how to overcome it:  

No matter how enticing it looks, ensuring the accuracy and reliability of AI-generated content is a challenge. And the biopharma company was aware of the same. So, they implemented a robust “Quality Collaborative Review Agent." This AI-driven tool compares newly generated content with historical submission documents. It flagged areas with low confidence for further review. This AI Use Cases in Life Sciences, alongside human oversight, helped maintain regulatory standards and cut errors.  

Use case 3: Patient Assistance & Chatbots 

Benefits: 30% higher patient satisfaction. 

One of the major challenges in the HCLS industry is evolving patient expectations. Patients today want instant resolution of their issues; not doing so can dissatisfy them. Gen AI can tackle this. Advanced AI chatbots (powered by Gen AI) can handle real-time patient queries. They provide accurate and timely information and take care of routine HCP interactions. 

Chatbots Answer not only your patients' queries but also health questions 24 hours a day. For example, if a patient suffers from symptoms like fatigue or pain, they can go to a chatbot and track how treatments affect them. It’s like having a virtual assistant for your health. Gen AI also helps patients stick to their treatment plans with step-by-step reminders, ensuring they stay on track. 

That's why companies using Gen AI-powered chatbots see a 30% increase in patient satisfaction. 

Use case 4: Simplifying Documentation  

Managing documentation in pharma and related fields is a challenge. Especially when managing large volumes of regulatory documentation, often amplified by mergers and acquisitions. Generative AI in Life Sciences is here to help too. 

It simplifies the massive amount of documentation required. How? 

Faster Labeling 

Tasks like generating or updating regulatory labels, which used to take months, can now be completed in weeks. By summarizing and combining these documents, AI in Life Sciences can run similarity checks. It can then pinpoint sections that need updates when regulations change. 

Summarizing Patient Records 

AI Use Cases in Life Sciences can combine and summarize complex patient records, giving doctors easy access to vital information. 

Example  

AIS Global faced the challenge of managing large volumes of regulatory documents. Especially after mergers and acquisitions. Handling this was time-consuming, requiring months or even a year to process. 

By integrating Generative AI in Life Sciences, they could simplify and automate tasks like running similarity scores and summarizing key sections of documents. Gen AI quickly identified which documents needed updates when regulatory changes occurred. 

This innovation allowed AIS Global to reduce the time spent on regulatory documentation. 

Use Case 5: Data Monetization  

If you Work in the life sciences industry, you know the importance of data. But did you know that you can turn that data into revenue with Gen AI? Yes! 

Stringent data privacy laws, like the EU’s General Data Protection Regulation (GDPR), are essential for protecting consumer information but often limit how companies can use the data they collect. 

However, you can analyze anonymized and fragmented data sets using gen AI-powered tools, turning them into valuable assets. One proposed solution is to use Generative AI in Life Sciences to generate detailed user personas based on criteria like subscriptions, consumption behavior, and search history. This allows companies to recommend cross-selling and up-selling opportunities intelligently. 

Example

Pharmaceutical company could use Gen AI to assess the demand for newly published research or therapies. They can analyze trends in search behavior, journal citations, and patient or healthcare professional (HCP) interests. This AI-driven analysis helps editorial, marketing, and R&D teams make data-driven decisions about which therapies or products to prioritize and how to price them effectively. 

Use Case 6: Accelerating Drug Development 

Benefit: 40% of the value in R&D. 

One of the rising questions today in life sciences is- How does Gen AI help in drug development? AI in Life Sciences is speeding up the creation of new therapies by improving research processes. Let's find out how. 

• Drug Discovery & Repurposing AI can process massive datasets in a fraction of the time it would take humans. This way, humans can identify potential drug candidates faster. This cuts down the time to market and contributes up to 40% of the value in R&D. 

Plus, Gen AI can predict which patient phenotypes might not respond well to certain medications. So, HCPs can offer more personalized and safer treatments. 

• Target Discovery Gen AI is revolutionizing target discovery in pre-clinical research. Tasks like analyzing large datasets, predictive modeling, and running simulations are now automated. This reduces trial costs by up to 60%. 

Example 

AlphaFold 3 is an advanced AI model that uses generative AI to predict the structure and interactions of molecules (including proteins, DNA, RNA, and ligands.) By using AI in Life Sciences, AlphaFold 3 can accurately forecast how these molecules interact. This improves prediction accuracy by 50% compared to traditional methods. 

Moreover, AlphaFold 3 can simulate how proteins bind to antibodies, which is critical in developing new disease treatments. In fact, AlphaFold 3 is twice as accurate in specific molecular interaction categories, speeding up the drug design process. 

Use Case 7: Pharma Data Insights & Analytics 

Benefits: 10–20% faster patient enrollment 

Gen AI will not become a differentiating factor for your firm, but how you use it to analyze data will. 

Gen AI is critical in analyzing structured and unstructured clinical trial data. This capability enhances operational decision-making. It provides a centralized source of insights to clinical teams. Many top pharmaceutical companies have already implemented “clinical control towers.” These gen AI-powered towers offer actionable insights, smart alerts for early interventions, and automated communication drafting to improve team coordination. These tools promise a 20% cost efficiency improvement and a 10–20% faster patient enrollment process. 

AI Use Cases in Life Sciences also offers real-time insights into how drugs perform in the market. This is helping pharma companies track everything from patient drop-off rates to regional therapy outcomes 

Use Case 8: Regulatory & Compliance Management 

Benefit: 30% faster responses to HAQs 

Navigating global regulations can be tricky. But Gen AI helps life sciences companies stay compliant while reducing manual work. 

Medical Sentiment Analysis 

AI can gauge physician sentiment on key drug attributes, like efficacy and safety, and make regulatory reporting more accurate. 

Responsible AI Assurance 

Gen AI ensures compliance by monitoring and confirming its outputs, helping companies meet global regulatory standards. 

For example, regulatory agencies often issue Health Authority Queries (HAQs) during clinical development. This can delay drug approvals and market entry. AI for Life Sciences offers a solution by predicting HAQ patterns. It can also draft accurate and 30% faster responses and optimize submission strategies. By using predictive analytics, Gen AI enables teams to expect queries, reducing initial HAQs. This also reduces 50% follow-up queries and speeds up the process. 

Use Case 9: Personalized Treatment through Digital Health Solutions 

Generative AI transforms patient care by enhancing how companies engage with patients and manage treatment outcomes. One major use case is AI-powered platforms that improve real-time patient engagement and medication adherence. These solutions extract data from wearables, medical devices, and healthcare systems to deliver personalized interventions and support. 

Example 

The Hizentra app. This gen AI-powered app was designed to support patients in administering Hizentra (a treatment for individuals with rare and serious diseases.) 

The app was adopted by 20% of the U.S. patient population without dedicated marketing. 

This Patient Companion Mobile App uses Gen AI to answer patients' questions about their condition. It also features a Care Team Portal, where AI-driven summarization helps clinicians access key insights from patient data. The app offers real-time data and insights, helping CSL Behring continuously enhance patient care 

Use Case 10: Streamlining Prior Authorizations with Gen AI 

Benefit: Bringing down authorization time to 72 hours 

Generative AI is significantly impacting the complex process of authorizations in healthcare. By using AI to transform natural-language medical policies into machine-readable formats, healthcare companies can accelerate patient treatment access. 

Example 

Mindshift’s platform automates prior authorizations for healthcare providers. It addresses the current challenge of a 90% manual authorization process. The inefficiency in this process often causes delays in patient care. 

By utilizing Gen AI, Mindshift significantly reduces authorization times from 10-14 days to 72 hours. And companies are already experiencing the benefits. For instance, one large infusion care provider saved $22 million using Mindshift's platform to reduce claim denials and enhance patient management. 

Use Case 10: Precision Medicine & Personalized Treatment 

Benefit: 10% Increase in Possibility of success of a treatment 

Gen AI is leading the way in precision medicine. Thus, HCLS individuals can now design treatments specifically for individual patients. AI for Life Sciences helps healthcare providers recommend personalized therapies and predict patient behavior by analyzing patient data. 

• Personalized Therapy AI tailors treatment plans based on a patient’s specific genetic and medical history, leading to better outcomes. 
• Gene Therapy Optimization AI tools, like Formsite AI, help speed up gene therapy design, reducing the time from research to IND filing. 
• Behavior Prediction Predicting how a patient might respond to treatment allows for smarter, faster decision-making. 

Example 

You can combine genetic and phenotypic data with real-world data from medical records. AI for Life Sciences can offer a more personalized approach to cancer treatment using this. This helps create more diverse clinical trials. And provides insights into why certain patient groups respond differently to the same therapies. The result is improved treatment outcomes and personalized therapies for each patient’s profile. This can increase the possibility of success (PoS) for trials by 10%. 

Use Case 11: Amplified Quality Events & Procurement Management with Gen AI  

Benefit: Boost productivity by 50-80% 

Generative AI transforms quality event management and procurement in the HCLS industry. For quality events, Gen AI detects, investigates, and resolves issues within the manufacturing and supply chain. It minimizes downtime and reduces the risk of regulatory penalties. 

In procurement, you can use Gen AI bots to streamline processes. These processes are drafting RFPs, managing purchase orders, and analyzing supplier performance. These bots can also assist in contract management, providing insights by analyzing patterns from past negotiations. Using this bot, you can cut procurement costs by up to 10% and boost productivity by 50-80%. 

Things to Remember Before Embracing Gen AI in Life Sciences 

When integrating Gen AI into life sciences, having a clear strategy is essential. Leaders with well-defined plans and allocated budgets are more likely to experience results. 

Consider these key questions 

• Does the use case align with our objectives? 
• Can AI effectively address this need? 
• Are responsible AI guardrails in place? 
• Will the ROI justify the investment? 

With 79% of companies developing tailored Gen AI solutions, having a solid framework ensures success. Prioxis can guide you through implementing responsible, effective Gen AI strategies to maximize results.