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AI in Evidence Generation

How HTA Agencies Are Evaluating AI in Evidence Generation

As AI rapidly reshapes the evidence landscape, health technology assessment (HTA) bodies are beginning to define clear expectations for its use in submissions. At CapeStart, we work closely with life sciences clients navigating these changes—especially those integrating GenAI into literature reviews, RWE generation, economic modeling, Joint Clinical Assessment (JCA) support, Clinical Evaluation Reports (CERs), Patient…

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ROI of GenAI

How MadeAi™ Uses GenAI-Aided Review & AI as Reviewer to Beat Manual SLR Solutions

In today’s fast-paced life sciences environment, literature reviews are critical—but they’re also time-consuming, labor-intensive, and increasingly expected to support regulatory, HEOR, and market access submissions while maintaining full transparency and traceability.  While traditional platforms offer useful automation features, most are either rules-based classifiers or structured workflow tools, limiting their adaptability and explainability. That’s where MadeAi™ stands…

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Literature Review

How to Evaluate GenAI-enabled Literature Review Platforms: A Buyer’s Guide to Transparency, Accuracy, and Compliance

The rise of generative AI (GenAI) is transforming how life sciences organizations approach evidence synthesis, regulatory submissions, and market access activities. As literature review workflows become more complex—and timelines more compressed—life science professionals are increasingly turning to GenAI-enabled platforms to streamline systematic literature reviews (SLRs), reduce manual burden, and scale evidence synthesis. However, evaluating…

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Real Reviews

AI in Action: Real Results from Real Reviews

As the demands for faster, more accurate, and scalable literature reviews rise in life sciences, AI is no longer just a future promise—it’s a proven performance driver. Systematic literature review (SLR) teams, especially in HEOR and medical affairs, are adopting GenAI platforms not just for experimentation but to solve long-standing bottlenecks in screening, extraction, and…

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AI Implementation

The Biggest Challenges to Implementing AI in Life Sciences

The promise of Artificial Intelligence (AI) in life sciences is undeniable—offering advancements in drug discovery, clinical research, and evidence generation. Yet, many organizations still struggle to implement AI effectively. To better understand these challenges, CapeStart conducted a survey of 104 life science professionals, asking them:  What are the top three barriers to success for your…

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AI’s Biggest Wins

AI’s Biggest Wins: Three Core Business Areas Benefiting the Most

Across industries, businesses are turning to AI to address pressing challenges, drive innovation, and improve efficiency. According to CapeStart’s Life Science AI Research Report, organizations are increasingly identifying specific areas where AI can deliver the greatest value. By focusing on key business functions, companies aim to unlock new opportunities, streamline operations, and enhance their impact…

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