AI Agents Find Their Footing in Healthcare and Pharma

The Prompt Economy is a tug of war lately, with both sides invested in a successful outcome for agentic technology. Pulling on one side are the security-minded tech executives insisting on the right risk management parameters. Pulling on the other side are the practitioners that are creating new use cases by the day.

Healthcare continues to be one of the most fruitful areas. Healthcare IT News reports that Dermatology Partners, a large dermatology group with 80 providers across 41 locations, is using agentic AI to handle a heavy flow of patient phone calls. The practice receives between 2,000 and 4,000 inbound calls a day, with the busiest periods coming on Monday mornings. Leaders said that made it hard to maintain fast service without staffing for peak demand all the time.

They turned to a voice-based AI system that could speak directly with patients, schedule appointments, and work inside the group’s existing practice management platform. The article says the group chose a voice-first approach because many patients are older and may be less comfortable with text-based tools.

According to Healthcare IT News, the AI now does more than book visits. It can also capture simple clinical requests, such as medication refill needs and wound care concerns, and send those items into the electronic health record for staff review. Dermatology Partners says about 25% of AI-routed calls are now resolved through appointment-related tasks, which frees human staff to focus on more complex conversations. The organization is tracking performance each day and hopes to push the AI resolution rate above 50% in the near term. The article presents the effort as a way to extend staff capacity and improve patient access, rather than replace employees.

FIs Take Note

TechForge reports that agentic AI is starting to move deeper into financial services, with the clearest focus on practical business use cases rather than broad claims about transformation. The article says financial firms are interested in agents that can work across research, operations and client-facing workflows, especially in areas where employees must sort through large amounts of information and make decisions quickly. The piece frames this as a step toward using AI in higher-value work, while also warning that finance companies need stronger testing before they rely on these systems in production.

The article points to several specific use cases. TechForge says financial institutions are exploring agentic AI for investment memos, compliance checks and root-cause investigations. It also says these systems could be used in situations where an agent might help shape a portfolio recommendation, which raises the bar for accuracy and oversight. Franklin Templeton is the main company example in the piece. A Franklin Templeton executive says firms want to use AI agents in research, internal operations and customer-facing work, making the article’s central point clear: the appeal of agentic AI in finance is its ability to support real workflows that sit close to revenue, risk and client service.

TechForge says the challenge is proving that these systems can be trusted before they are allowed into sensitive workflows. That is where Sentient’s Arena platform comes in. The article says the platform is designed to test agents under difficult conditions, including incomplete data, unclear instructions and conflicting information, while showing the full reasoning path behind an answer. The piece also notes that Pantera and Founders Fund are part of the early group evaluating the platform, and it points to a related example of Goldman Sachs and Deutsche Bank testing agentic AI for trade surveillance. Together, those examples show where the market is headed: toward agentic AI in research, compliance, surveillance, operations and client support, but only with tighter controls around reliability and explainability.

The Pharma Angle

GEN Edge reports that Nvidia’s GTC 2026 made one point clear: pharma is moving from AI experiments to real operating use cases. The article says Roche is deploying more than 3,500 Nvidia Blackwell GPUs across cloud and on-premises systems in the U.S. and Europe to speed up R&D, diagnostics and manufacturing efficiency. It also says Eli Lilly and Nvidia have committed $1 billion over five years to fund the talent, infrastructure and computing needed to tackle bottlenecks in AI-based drug discovery. GEN Edge presents these moves as a sign that large drugmakers now see AI infrastructure as core to how research gets done, not as a side project.

The use cases in the article are especially strong in drug discovery and life sciences operations. GEN Edge says new AI systems are being built to support biological discovery, including models that predict protein complexes and design protein binders for structure-based drug development.

The article also points to IQVIA’s agentic platform, which has already deployed more than 150 specialized agents to reduce heavy workloads such as clinical trial site selection. Other healthcare-focused uses include patient follow-up for chronic care and post-discharge support, as well as clinical documentation through ambient listening tools. Overall, the article argues that agentic AI in pharma and healthcare is being applied to concrete jobs: finding drug targets faster, improving trial planning, easing documentation burdens and making research and care workflows more productive.

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