How an AI Research Assistant Simplifies Evidence Review Across Life Sciences Teams

I. Introduction

A.The Growing Evidence Burden in Life Sciences: Healthcare and life sciences are producing scientific information at an unprecedented pace. Every year, millions of new publications emerge across clinical trials, real‑world studies, regulatory decisions, conference abstracts, and health economics research. For teams across HEOR, Clinical Development, Medical Affairs, Regulatory, and Strategy, staying current with this expanding evidence base has become increasingly difficult. 

These teams rely on scientific literature to support trial design decisions, payer submissions, regulatory strategies, medical communications, and competitive positioning. Yet the sheer volume of data makes traditional, manual literature review unsustainable. As evidence requirements become more complex and timelines become tighter, manual approaches can no longer keep up. This has created an urgent need for smarter, faster ways to discover, review, and synthesise scientific evidence.

B. The need for faster and more accurate evidence discovery: HEOR leaders need faster, more precise methods for gathering the right evidence at the right time without sacrificing scientific rigour. Across life sciences organisations, leaders need confidence that critical evidence has not been missed and that insights are generated quickly enough to inform decisions. Manually searching databases, reviewing abstracts, and extracting insights is time‑intensive and prone to inconsistency. Slow or incomplete evidence discovery can delay internal decisions, weaken submissions, and limit an organisation’s ability to respond to regulators, payers, or internal stakeholders. Faster, more structured evidence workflows directly impact decision quality, operational efficiency, and strategic confidence. This is where AI-powered research assistants are beginning to change how evidence work gets done.

C. Introducing Pienomial's KnolAI research assistant: Pienomial’s KnolAI is an AI-powered, chatbot-style research assistant designed for life sciences teams. It helps users across Clinical, HEOR, Medical Affairs, Regulatory, and Strategy functions find, summarise, and analyse scientific literature faster using natural language queries, intelligent filtering, and context-aware synthesis. With KnolAI, your team can go from question to insight in minutes, whether you’re preparing a regulatory submission, validating trial comparators, developing value narratives, or scanning the latest oncology studies.

II. Challenges in Evidence Review for HEOR

Managing Large and Fragmented Evidence Sources: Life sciences teams typically work across multiple literature sources, public databases such as PubMed, internal repositories, conference libraries, regulatory documents, and published trial reports. Navigating these fragmented sources manually is slow and often incomplete. Even experienced researchers can struggle to ensure coverage across all relevant evidence, especially when balancing tight timelines and competing priorities. As a result, teams may lack a complete or up‑to‑date view of the scientific landscape when decisions need to be made.

Time Lost to Manual Searching and Summarisation: Traditional evidence workflows involve hours or days of manual searching, filtering, reading, and summarising. After identifying relevant studies, teams still need to extract outcomes, interpret findings, and translate them into concise insights for internal or external use. This manual effort diverts valuable expert time away from higher‑value activities such as interpretation, strategy development, and stakeholder engagement. Over time, it becomes a bottleneck that slows down evidence generation across the organisation.

The Risk of Missing Critical Evidence: When teams are under time pressure, important studies can be overlooked, particularly newly published research or evidence outside a narrow search scope. Missing key evidence can weaken comparative analyses, economic models, or strategic arguments. This risk is one of the main reasons life sciences organisations are increasingly adopting AI and machine learning to support research workflows. AI can cast a wider net, surface contextual connections, and continuously monitor evolving evidence while still keeping humans firmly in control of interpretation and decision‑making.

III. What an AI Research Assistant Does Differently

Natural Language Search for Complex Questions: An AI research assistant allows users to ask questions the way they naturally think. Instead of constructing complex Boolean queries, researchers can ask conversational questions such as those focused on comparative efficacy, specific patient populations, mechanisms of action, or safety outcomes. KnolAI understands biomedical language and research intent, enabling faster access to relevant evidence without requiring advanced search expertise.

Context‑Aware Summarisation: One of the most powerful capabilities of an AI research assistant is intelligent summarisation. KnolAI can condense multi‑page articles into structured summaries that highlight key outcomes, populations, endpoints, safety findings, and economic insights. Importantly, these summaries are context‑aware. Rather than stripping nuance, KnolAI preserves scientific meaning, allowing users to quickly understand what matters most while retaining the ability to explore deeper when needed.

Continuous Learning Over Time: Unlike static tools, an AI research assistant improves with use. By learning from past queries, selected studies, and interaction patterns, KnolAI becomes better aligned with how life sciences teams search for and interpret evidence. Over time, this creates a smarter and more efficient research experience supporting faster discovery and more consistent insight generation across teams.

IV. Inside KnolAI by Pienomial

A. Key features and capabilities of the platform: KnolAI combines several advanced capabilities into a single conversational experience:

Natural language evidence discovery across scientific literature

Automated, structured summarisation of complex publications

Contextual understanding of clinical, scientific, and regulatory language

Organised outputs that support downstream analysis and communication

Designed for real‑world life sciences workflows, KnolAI supports deep research tasks while remaining intuitive and easy to use.

B. Example: Using KnolAI to Explore a Therapy Landscape: Consider a team evaluating a new therapy area in oncology. Using KnolAI, they can ask questions about recent trial outcomes, competing mechanisms, reported quality‑of‑life data, or emerging safety signals. KnolAI scans the relevant literature, organises findings, and delivers concise summaries that highlight treatment patterns and unmet needs. What might take days of manual effort can now be achieved in a fraction of the time, enabling faster, more informed discussions.

C. Collaboration and Knowledge Sharing: KnolAI supports collaboration by allowing teams to save searches, revisit insights, and share findings across functions and geographies. Evidence can be organised and exported to support modelling, medical writing, internal reviews, or strategic planning. This shared access to structured insights helps reduce duplication of effort and creates a more consistent evidence foundation across the organisation.

V. Benefits Across Life Sciences Teams

A. Faster Time‑to‑Insight: By accelerating discovery and summarisation, KnolAI significantly reduces the time required to move from question to insight. Teams can respond more quickly to internal requests, emerging evidence, and external stakeholder needs.

B. Stronger, More Confident Decision‑Making: With broader and more complete evidence coverage, teams gain confidence that their decisions are grounded in the full scientific picture. This supports clearer positioning, stronger narratives, and better‑informed strategic choices.

C. More Efficient Use of Expert Time: By reducing manual research tasks, KnolAI allows experts to focus on interpretation, judgment, and strategy where human expertise matters most.

VI. Why AI + Human Expertise is the Future

A. AI as an Enabler, Not a Replacement: AI does not replace scientific or clinical expertise. Instead, it enhances it. While KnolAI accelerates evidence discovery and organisation, human experts remain essential for interpreting results, understanding nuance, and making strategic decisions. This partnership between AI and domain expertise leads to better outcomes than either could achieve alone.

B. Transparency and Trust in AI Outputs: Trust is critical in scientific workflows. KnolAI is designed with transparency in mind, allowing users to see how evidence is sourced and summarised. Outputs maintain scientific integrity and support traceability, ensuring the AI acts as a trusted assistant rather than a black box.

VII. Conclusion

A. From Evidence Overload to Insight: AI research assistants are redefining how life sciences teams engage with evidence. By reducing manual effort, accelerating synthesis, and improving completeness, tools like KnolAI help organisations navigate growing complexity with greater speed and confidence.

B. Meet KnolAI: Your Research Co‑Pilot: KnolAI empowers life sciences teams to move from information overload to actionable insight. With conversational access to scientific literature and intelligent synthesis at its core, KnolAI serves as a powerful research co‑pilot helping teams ask better questions, find better evidence, and make better decisions.