Artificial intelligence tools in healthcare have moved beyond clinical notetaking and patient-facing chatbots into systems that can shape clinical decisions and tailor pharmaceutical marketing to individual health systems, according to speakers at Asembia's AXS26 Summit in Las Vegas. The shift means AI is no longer just improving efficiency around the edges of care delivery - it is starting to change how providers diagnose, treat, and communicate with patients, and how manufacturers position their products.
Muna Tuna, a partner at Ernst and Young and the EY US Market Access leader, said earlier AI tools like notetakers and chatbots improved workflow but did not fundamentally alter how patients interact with the healthcare system or how payors and providers deliver care. The newer generation of healthcare AI is different. These systems can generate diagnostic hypotheses, integrate clinical practice guidelines into evidence-based decision support tools, and continuously incorporate new input rather than delivering static answers to fixed queries.
AI in clinical decision-making
Providers are using AI to text patients and optimize treatment paradigms, Tuna said, potentially catching subtle signs of disease progression and evaluating appropriate therapeutic options. She emphasized that these tools support active reasoning and adapt as new information becomes available.
"[However,] there will always be a human in the loop in healthcare," she said. AI should never replace the pharmacist or provider, she added, but it can help healthcare professionals personalize and optimize care.
For professionals working in clinical settings, the practical implication is that AI-assisted decision support is becoming a standard part of care planning rather than an experimental add-on. AI for Healthcare training increasingly covers these clinical applications, from diagnostic support to treatment optimization.
Manufacturers and market access
Tuna said manufacturers will see their work transformed as well, particularly in articulating the value of their products to different stakeholders - clinical and economic decision-makers, operational leaders, and healthcare professionals. The challenge is that each health system responds to different value propositions.
"In pharma, there is a saying: 'You see a health system, you've seen one health system.' The things they respond to, the value propositions that resonate for them are all very different," Tuna said. AI tools can surface these varied points of resonance and enable manufacturers to tailor their marketing accordingly. That application is directly relevant to AI for Pharmaceutical Sales Representatives, where understanding how to match product messaging to individual health system priorities is becoming a core skill.
Why this matters for healthcare professionals
The takeaway for clinicians, pharmacists, and healthcare executives is that AI tools are shifting from documentation aids to active participants in clinical reasoning and market strategy. Providers who understand how AI generates diagnostic hypotheses and integrates clinical guidelines will be better positioned to use these tools effectively - and to maintain the human oversight that Tuna stressed remains essential. For those in pharmaceutical sales and market access, the ability to use AI to identify what resonates with each health system will increasingly separate effective outreach from generic pitches.
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