The ISPOR US 2026 conference in Philadelphia drew together colleagues and industry partners across evidence, value, and access. Across the presentations and sessions, a major theme emerged: we are an industry moving rapidly from AI experimentation and toward AI-enabled infrastructure. Here we share some of the key takeaways.

AI becomes core infrastructure

The strongest signal from ISPOR Philadelphia was that AI is no longer viewed as a side tool for productivity gains. Across HEOR, HTA, and RWE, organizations are beginning to embed AI directly into evidence generation and submission workflows. Discussions focused less on experimentation and more on operationalization, governance, and scalability.

AI is now being explored across the full evidence lifecycle, including systematic literature reviews, economic modeling, patient-reported outcomes, HTA submissions, payer communication, and regulatory documentation. The industry appears to be shifting toward continuously learning evidence systems rather than static, project-based workflows.

Agentic AI moves beyond simple automation

One of the biggest themes was the emergence of agentic AI systems. Instead of using isolated prompts, organizations are experimenting with coordinated AI agents that can generate models, review outputs, create documentation, and prepare evidence packages.

Several workshops demonstrated how AI can move from model concept to full implementation in both R and Excel while maintaining human oversight. The emphasis throughout was not full autonomy, but “human-at-the-helm” governance where AI accelerates and supports execution while experts retain accountability.

This reflects a broader transition from AI-assisted work toward AI-orchestrated workflows.

AI-supported SLRs reach a turning point

AI-assisted systematic literature reviews (SLRs) dominated the conference agenda. However, the conversation has evolved significantly from earlier discussions focused mainly on efficiency gains.

The field is now grappling with questions around reproducibility, transparency, benchmarking, and governance. Multiple sessions highlighted the lack of shared standards for evaluating AI-SLR performance and proposed industry-wide benchmarking frameworks and validation challenges.

ISPOR itself is increasingly positioning itself as a central body for developing good-practice guidance and methodological standards for AI-enabled evidence synthesis, with the anticipated publication of the GenAI in SLR taskforce report.

Regulatory readiness becomes critical

Another major theme was regulatory credibility. Panels focused heavily on FDA, EMA, NICE, and Health Canada guidance regarding AI-assisted evidence generation and real-world data curation.

The industry discussion has shifted from asking whether regulators will engage with AI-generated evidence to determining what documentation, validation, and governance standards will be required for acceptance.

Speakers repeatedly emphasized auditability, traceability, reproducibility, and version control as foundational requirements for regulatory-grade AI workflows.

Real-world data and AI converge

Many sessions positioned AI as the enabling layer needed to unlock the value of modern real-world data. Much of healthcare’s most clinically meaningful information remains trapped in unstructured formats such as clinician notes, pathology reports, and medical charts.

AI methods including NLP and machine learning are increasingly being used to transform this information into structured, research-ready evidence. This was especially prominent in sessions involving medical devices, exploratory evidence planning, and dynamic evidence generation strategies.

AI is increasingly being viewed not simply as an analytics tool, but as foundational infrastructure for modern RWE generation.

Patient voice gains new attention

Several workshops explored how large language models and conversational AI can support patient-centered research. These applications included free-text analysis, conversational patient interviews, social media analysis, and narrative symptom capture.

The interest in AI application in qualitative research represents an important expansion beyond traditional structured analytics. Researchers are now exploring whether AI can preserve the nuance of lived patient experience while enabling scalability.

At the same time, concerns around hallucination risk, construct validity, and bias remain central to these discussions.

HEOR leadership roles are evolving

As AI automates more technical tasks, the role of HEOR and RWE leaders appears to be changing. Multiple sessions suggested that future leadership value will increasingly center on governance, strategic interpretation, stakeholder trust, and organizational coordination.

Rather than replacing experts, AI may elevate the importance of human judgment and scientific oversight. Organizations will need leaders who can balance innovation with credibility in payer and regulatory environments.

This suggests AI adoption is not simply a technology challenge, but an organizational transformation challenge.

Responsible AI emerges as the central principle

Across nearly every session, the same themes repeatedly appeared: transparency, reproducibility, validation, governance, and human oversight.

The HEOR community appears to be converging around a shared understanding that AI adoption will only succeed if scientific credibility and integrity remain intact. The conversation is no longer about replacing traditional rigor, but about scaling evidence generation responsibly.

ISPOR Philadelphia ultimately showed an industry moving rapidly from AI experimentation toward AI-enabled infrastructure. The next phase of HEOR will likely be defined by organizations that can operationalize AI while maintaining trust, methodological rigor, and decision relevance.

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