KOL Engagement in the AI Era: How Medical Affairs Uses Evidence Synthesis

Key opinion leader engagement has always been central to Medical Affairs. But the nature of that engagement is changing. KOLs increasingly expect scientific discussions to be grounded in current evidence, relevant to their area of expertise, and focused on meaningful clinical questions rather than broad information sharing.

At the same time, Medical Affairs teams face an expanding evidence landscape. Clinical trials, publications, guidelines, real-world evidence, congress presentations, regulatory developments, and competitor research can make it difficult to build a complete picture before an interaction.

This is where AI expert intelligence can support a more evidence-led approach. Instead of replacing the scientific relationship, AI can help Medical Affairs teams research faster, synthesise evidence, identify knowledge gaps, and prepare more relevant questions for KOL conversations.

The objective is not simply to automate KOL engagement. It is to make scientific engagement more informed, focused, and useful.

Why KOL Engagement Is Changing

Medical Affairs has evolved from a function primarily focused on scientific dissemination into a broader strategic function involved in evidence generation, stakeholder engagement, and translating evidence into clinical practice. Recent Medical Affairs research describes increasing integration of real-world evidence, diverse data sources, digital tools, and cross-functional collaboration. [1]

KOLs are an important part of this ecosystem.

They may contribute perspectives on:

• Unmet clinical needs

• Emerging treatment approaches

• Clinical trial design

• Patient identification

• Evidence gaps

• Treatment patterns

• Scientific controversies

• Future research priorities

Medical Science Liaisons (MSLs) play an important role in facilitating scientific exchange and gathering insights from KOLs and other healthcare professionals. Professional guidance describes scientific exchange, insight gathering, and evidence generation as important elements of the MSL role. [2]

The challenge is that meaningful engagement requires preparation.

An MSL cannot simply know that a KOL specialises in oncology, cardiology, neurology, or another therapeutic area. The interaction becomes more valuable when the team understands the expert's research interests, the current evidence landscape, unresolved scientific questions, and where the KOL's perspective may help address an evidence gap.

What Is AI-Powered KOL Engagement?

KOL engagement AI refers to the use of artificial intelligence to support the research, preparation, personalisation, documentation, and analysis surrounding interactions with scientific experts.

It can help Medical Affairs teams answer questions such as:

• What has this expert recently published?

• Which clinical topics does the expert focus on?

• What evidence is emerging in the therapeutic area?

• Where do published studies disagree?

• Which questions remain unanswered?

• What new evidence has appeared since the last interaction?

• How does the expert's research relate to broader scientific developments?

1. Building a More Complete KOL Profile

Effective engagement starts with understanding the expert.

Traditional KOL mapping may rely on publication databases, conference participation, institutional affiliations, advisory involvement, and internal knowledge.

AI can bring these information streams together.

For example, an AI workflow can help organise evidence around:

• Publications

• Clinical research

• Therapeutic interests

• Trial involvement

• Scientific presentations

• Research collaborations

• Emerging areas of interest

• Recent scientific activity

Moving Beyond Static KOL Lists

A static KOL database can tell a team who the important experts are.

It does not necessarily tell them what those experts are thinking about now.

AI can help turn a static profile into a more dynamic intelligence view.

For example:

Expert → Recent publication → Research topic → Evidence gap → Relevant clinical question

This gives MSLs a more useful starting point for scientific engagement.

2. Evidence Synthesis Before the KOL Meeting

One of the most valuable applications of AI is evidence synthesis.

Before meeting an expert, an MSL may need to review dozens of publications, clinical studies, guidelines, conference abstracts, and other evidence.

A well-designed AI workflow can help organise this material into themes.

For example, it can identify:

• Areas of scientific agreement

• Conflicting findings

• Emerging evidence

• Methodological differences

• Evidence gaps

• Frequently studied outcomes

• Areas where further research is needed

3. Preparing Better Questions for KOLs

The quality of a KOL interaction depends partly on the quality of the questions being asked.

Generic questions can produce generic answers.

For example:

"What do you think about the current treatment landscape?"

may generate a broad discussion.

A more evidence-informed question could explore a specific uncertainty:

"Recent studies have produced different results around treatment response in this patient subgroup. From your clinical experience, what factors might explain the difference?"

The second question gives the expert an opportunity to contribute scientific interpretation.

AI can help identify areas where such questions may be useful.

This is one of the most important ways KOL engagement AI can support Medical Affairs: not by replacing the expert conversation, but by helping teams enter the conversation better prepared.

4. Connecting Published Evidence With Field Insights

Medical Affairs teams work with two important evidence streams.

The first is documented evidence:

• Clinical trials

• Publications

• Guidelines

• Real-world studies

• Regulatory information

The second is field insight:

• KOL perspectives

• MSL observations

• Investigator feedback

• Unmet-needs discussions

• Clinical-practice observations

These streams can complement each other.

Suppose published evidence suggests that a particular treatment challenge is relatively uncommon.

Several KOL conversations, however, repeatedly highlight the same issue in clinical practice.

That discrepancy may warrant further investigation.

AI can help organise and compare these signals.

The purpose is not to treat expert opinion as equivalent to clinical evidence.

Instead, expert insight can help identify where evidence-generation questions deserve further attention.

5. Identifying Evidence Gaps

Evidence synthesis is not only about finding what is already known.

It should also reveal what is not known.

For example, AI-assisted analysis may show that a therapeutic area has extensive evidence around:

• Efficacy

• Short-term safety

• Treatment response

but comparatively limited evidence around:

• Long-term outcomes

• Patient-reported outcomes

• Treatment sequencing

• Specific patient subgroups

• Real-world treatment patterns

6. Supporting Scientific Communication

Medical Affairs teams must communicate complex evidence accurately.

That makes a strong scientific communication platform pharma teams can use particularly valuable when information needs to be organised, reviewed, and adapted for different scientific audiences.

The same underlying evidence may need to support:

• KOL briefings

• Scientific presentations

• Medical information responses

• Advisory board preparation

• Internal evidence reviews

• Congress planning

• Evidence-generation discussions

7. Personalising Engagement Without Losing Scientific Integrity

Personalisation is often discussed in commercial contexts.

For Medical Affairs, however, personalisation should primarily mean scientific relevance.

A KOL's expertise can determine:

• Which evidence is most relevant

• Which questions should be prioritised

• Which scientific controversies deserve discussion

• Which emerging research areas are worth exploring

8. Tracking Scientific Activity Between Engagements

KOL engagement should not be treated as a series of disconnected meetings.

Scientific activity continues between interactions.

An expert may:

• Publish a new study

• Join a clinical trial

• Present new research

• Collaborate on a guideline

• Comment on emerging evidence

• Shift research priorities

AI can help Medical Affairs teams detect relevant changes.

This creates a more continuous intelligence cycle:

Engage → Capture insight → Monitor evidence → Identify change → Prepare → Re-engage

The benefit is not simply better record keeping.

It is greater continuity between scientific interactions.

9. Turning KOL Insights Into Evidence-Generation Questions

One of the strongest applications of evidence synthesis is connecting stakeholder insights to evidence-generation planning.

Suppose several experts identify uncertainty around:

• Treatment sequencing

• A patient subgroup

• Long-term outcomes

• Diagnostic pathways

• Patient experience

Medical Affairs can investigate whether existing evidence addresses those questions.

If not, the insight may inform future research.

Recent work describing Medical Affairs evidence-generation models highlights the importance of combining diverse data sources, real-world evidence, stakeholder collaboration, and patient-centred research. [1]

This creates a feedback loop:

KOL insight → Evidence gap → Research question → Evidence generation → New evidence → Scientific exchange

AI can help connect these stages.

10. Improving the Quality of MSL Preparation

MSLs have limited time.

Before an important interaction, they may need to review:

• The KOL's recent work

• Previous engagement notes

• New publications

• Competitor developments

• Clinical trial results

• Guidelines

• Relevant internal evidence

AI can reduce the manual effort involved in bringing these materials together.

A preparation workflow could produce a structured briefing containing:

Expert Profile

Current research interests and relevant scientific activity.

Evidence Update

Important developments since the previous interaction.

Scientific Questions

Open questions connected to the expert's area of interest.

Evidence Gaps

Topics where the literature remains limited or inconsistent.

Engagement History

Relevant previous scientific discussions and unresolved questions.

Suggested Discussion Areas

Evidence-based topics for exploration during the interaction.

The MSL still determines what is appropriate to discuss.

AI simply helps create a better preparation environment.

The Importance of Evidence Traceability

AI becomes significantly more useful in Medical Affairs when users can verify where information came from.

This is particularly important when synthesising scientific literature.

A summary without sources creates uncertainty.

A summary linked to the underlying publications gives the Medical Affairs professional an opportunity to validate the interpretation.

Evidence traceability can therefore include:

• Source publication

• Publication date

• Study type

• Population

• Intervention

• Outcome

• Relevant finding

• Supporting passage or data

• Context and limitations

The principle is simple:

AI should accelerate evidence review without weakening evidence verification.

Avoiding AI Hallucinations in KOL Preparation

AI introduces its own risks.

A system can produce a plausible but incorrect statement about a study, publication, expert, or clinical finding.

That makes human review essential.

Medical Affairs teams should establish processes for:

• Source verification

• Evidence traceability

• Human review

• Access control

• Approved data sources

• Appropriate use policies

• Documentation

Measuring the Value of AI-Assisted KOL Engagement

Measuring KOL engagement has always been difficult.

Traditional activity metrics may include:

• Number of interactions

• Number of KOLs engaged

• Meeting frequency

• Geographic coverage

Evidence Relevance

Was the interaction based on current and relevant evidence?

Insight Quality

Did the engagement produce a meaningful scientific insight?

Evidence Gaps Identified

Did KOL discussions uncover previously unrecognised questions?

Follow-Up Value

Were important scientific questions investigated after the interaction?

Evidence Generation Impact

Did insights contribute to future evidence-generation planning?

The goal should be to measure scientific value, not simply interaction volume.

AI and the Future of KOL Mapping

KOL identification itself is changing.

Historically, organisations might rely heavily on publication counts, citations, conference participation, and established reputation.

These remain useful indicators.

But modern KOL intelligence can consider a broader set of signals.

For example:

Research activity + scientific influence + therapeutic expertise + emerging-topic activity + network relevance

AI can help analyse these signals at scale.

This may help Medical Affairs identify emerging experts earlier rather than relying exclusively on established names.

That matters in rapidly evolving fields where scientific leadership can shift quickly.

Evidence Synthesis Across the Medical Affairs Organisation

KOL engagement should not exist in isolation.

Insights gathered through scientific exchange can be relevant to:

• Medical strategy

• Evidence generation

• Clinical development

• HEOR

• Regulatory strategy

• Publication planning

• Medical communications

• Patient-centric research

Pienomial and AI-Powered Medical Affairs Intelligence

Pienomial's AI-powered intelligence capabilities are designed to help organisations work with complex information and turn evidence into actionable intelligence.

Its Knol AI product can support AI-assisted research and analysis workflows, helping teams work more efficiently across large information environments.

For Medical Affairs organisations, the broader life sciences solution can support evidence-driven workflows across clinical, scientific, and competitive intelligence activities.

This approach is particularly relevant to KOL engagement because the value of an expert interaction depends partly on the quality of information surrounding that interaction.

The goal is not to automate the relationship.

It is to help Medical Affairs professionals arrive with better evidence, better context, and better scientific questions.

A Practical AI-Enabled KOL Engagement Workflow

A Medical Affairs team can structure the process into seven stages.

Stage 1: Identify

Map relevant experts based on scientific expertise and current activity.

Stage 2: Profile

Review publications, trials, presentations, research interests, and previous engagement.

Stage 3: Synthesise

Bring together the most relevant evidence surrounding the therapeutic question.

Stage 4: Identify Gaps

Highlight conflicting findings, unanswered questions, and areas requiring expert interpretation.

Stage 5: Engage

Use the evidence review to guide a focused scientific discussion.

Stage 6: Capture

Document relevant insights and distinguish expert perspective from established evidence.

Stage 7: Connect

Link important insights to evidence-generation planning, medical strategy, and future scientific engagement.

This creates a repeatable intelligence cycle rather than a collection of isolated KOL meetings.

What Medical Affairs Teams Should Look for in an AI Solution

Not every AI platform is appropriate for scientific engagement.

Teams should consider whether the technology provides:

Evidence Traceability

Can users identify the sources behind AI-generated findings?

Scientific Search

Can the system retrieve relevant clinical and scientific evidence?

Contextual Synthesis

Can it connect evidence across multiple sources rather than summarising documents independently?

KOL Intelligence

Can it help identify and monitor relevant scientific activity?

Security

Can sensitive internal information be managed appropriately?

Governance

Are there controls for responsible AI use?

Human Review

Can experts validate AI-generated findings before they are used?

Collaboration

Can Medical Affairs teams share and build on evidence intelligence?

These capabilities matter because scientific engagement requires a higher standard of evidence quality than ordinary information retrieval.

The Future of KOL Engagement Is Evidence-Led

The future of KOL engagement is unlikely to be defined by simply increasing the number of interactions.

It will be defined by improving their relevance.

Digital transformation is already changing how Medical Affairs generates insights, communicates evidence, engages stakeholders, and measures impact. Recent research describes AI as an enabler of personalised scientific communication and evidence generation, while emphasising that digital tools should strengthen—not replace—high-quality scientific exchange. [3]

AI can contribute to this transformation by helping teams:

• Understand KOL expertise

• Monitor scientific activity

• Synthesise evidence

• Identify gaps

• Prepare focused questions

• Capture insights

• Connect insights to evidence generation

Conclusion

KOL engagement AI is changing how Medical Affairs teams prepare for and learn from scientific interactions.

The strongest use cases are not about replacing MSLs or automating relationships. They are about improving the intelligence surrounding each interaction.

AI can help Medical Affairs teams build dynamic expert profiles, synthesise scientific evidence, identify evidence gaps, monitor new publications, prepare better questions, and connect KOL insights with broader evidence-generation strategies.

This makes evidence synthesis a central capability in modern KOL engagement.

The most effective model is therefore not:

AI replaces scientific engagement.

It is:

AI strengthens scientific preparation → KOL provides expert interpretation → Medical Affairs captures insight → Evidence strategy evolves.

For organisations investing in a scientific communication platform pharma teams can use across complex evidence environments, the priority should be technology that combines speed with traceability, scientific rigor, governance, and human oversight.

Pienomial can support this shift by helping life sciences teams connect research, evidence, and intelligence in a more structured environment.

The future of KOL engagement is not simply more data.

It is better to use evidence before, during, and after every scientific conversation.

Frequently Asked Questions

1. How is AI changing KOL engagement in Medical Affairs?

AI can support KOL identification, expert profiling, scientific activity monitoring, evidence synthesis, meeting preparation, insight capture, and evidence-gap identification. It should complement rather than replace scientific exchange.

2. What is KOL engagement AI?

KOL engagement AI refers to artificial intelligence tools used to support the research and intelligence activities surrounding interactions between Medical Affairs professionals and key opinion leaders.

3. How does evidence synthesis improve KOL engagement?

Evidence synthesis helps MSLs understand current research, identify conflicting findings, recognise evidence gaps, and prepare more focused scientific questions for expert discussions.

4. Can AI replace Medical Science Liaisons?

No. MSLs provide human scientific judgment, relationship management, contextual interpretation, and two-way scientific exchange. AI can reduce research and preparation workload but does not replace these responsibilities.

5. What should a scientific communication platform for pharma provide?

Important capabilities include evidence retrieval, source traceability, scientific synthesis, expert intelligence, collaboration, security, governance, and human review.

6. How can KOL insights support evidence generation?

KOLs can identify unmet needs, evidence gaps, clinical uncertainties, and research priorities. When systematically captured and assessed, these insights can inform future evidence-generation strategies.

References

[1] Shaping the Future of Evidence Generation: Real-World Data to Drive Healthcare Transformation and Patient-Centered Decisions. The article discusses the evolution of Medical Affairs evidence generation, including real-world data, predictive analytics, patient-centred research, and collaboration with external experts.
PubMed Central — Evidence Generation in Medical Affairs

[2] Promoting Best Practices for Medical Science Liaisons: Position Statement from the APPA, IFAPP and MSLS. The position statement describes scientific exchange, evidence generation, and engagement with external experts as important MSL responsibilities.
PubMed Central — MSL Best Practices

[3] Lalwani A, et al. Embracing Digital as a Paradigm Shift in Medical Affairs. Pharmaceut Med. 2026. The article examines AI-enabled stakeholder engagement, evidence generation, personalised communication, and responsible digital transformation in Medical Affairs.
PubMed — Embracing Digital as a Paradigm Shift in Medical Affairs

[4] Measuring the Impact of Medical Science Liaisons: A Global Cross-Sectional Survey of Evaluation Practices and Perceptions. The 2026 study examines how organisations measure MSL value and the challenges associated with evaluating scientific engagement.
PubMed — Measuring MSL Impact

[5] The Evolution of Medical Affairs Central Teams: Shaping Strategic Action and Driving Healthcare Transformation Through Scientific Interaction. The article discusses stakeholder mapping, evidence-generation planning, scientific interaction, and the emerging role of AI and digital tools in Medical Affairs.
PubMed — Evolution of Medical Affairs Central Teams