Healthcare in focus

Healthcare operations are being transformed as AI streamlines both clinical and administrative workflows.

In surgical robotics, like those produced by Moon Surgical, automated setup and adaptations to surgeon preferences may enable faster, more predictable surgeries and higher operating room utilization. As Joe Mullings, Chairman and Chief Executive Officer of The Mullings Group Companies emphasized, “Physical AI will tremendously reduce not just the cognitive load on the surgeon but the whole care team, which will reduce overall case time.”

Diagnostics are also being reshaped. Vista AI’s software manages a growing number of MRI scans at leading hospitals, noticeably reducing exam times and backlog, and leveling up technologists’ skills. “About 90% of volume with the hospitals we partner with are going through our software,” noted CEO Daniel Hawkins.

In drug discovery, AI may help bring new drugs into the clinic faster by accelerating R&D workflows, improving identification of novel drug candidates, and streamlining administrative tasks required to move assets into the clinic.

AI could shorten R&D workflows that previously took months to days. Liz Schwarzbach, Chief Business Officer of Big Hat Biosciences highlighted, “At Big Hat, we run weekly cycles designing new antibody sequences and making them in the lab. DNA synthesis now delivers blocks in a day or two, instead of a week."

While Jason Silvers, Chief Financial Officer of Generate Biomedicines added, “Our fastest cycle is eight days. When I started, it was six months.”

Healthcare operations are being transformed as AI streamlines both clinical and administrative workflows.

In surgical robotics, like those produced by Moon Surgical, automated setup and adaptations to surgeon preferences may enable faster, more predictable surgeries and higher operating room utilization. As Joe Mullings, Chairman and Chief Executive Officer of The Mullings Group Companies emphasized, “Physical AI will tremendously reduce not just the cognitive load on the surgeon but the whole care team, which will reduce overall case time.”

Diagnostics are also being reshaped. Vista AI’s software manages a growing number of MRI scans at leading hospitals, noticeably reducing exam times and backlog, and leveling up technologists’ skills. “About 90% of volume with the hospitals we partner with are going through our software,” noted CEO Daniel Hawkins.

In drug discovery, AI may help bring new drugs into the clinic faster by accelerating R&D workflows, improving identification of novel drug candidates, and streamlining administrative tasks required to move assets into the clinic.

AI could shorten R&D workflows that previously took months to days. Liz Schwarzbach, Chief Business Officer of Big Hat Biosciences highlighted, “At Big Hat, we run weekly cycles designing new antibody sequences and making them in the lab. DNA synthesis now delivers blocks in a day or two, instead of a week."

While Jason Silvers, Chief Financial Officer of Generate Biomedicines added, “Our fastest cycle is eight days. When I started, it was six months.”

Barriers to care are falling as advanced technologies reach more clinicians and patients, especially in underserved communities.

Physical AI and robotics are helping to elevate the skill level of surgeons, making complex procedures more accessible. “You’ve got democratization of surgery with a robot, but you still have the access issue because they aren’t an inexpensive proposition in today’s environment.” observed Joe Mullings, Chairman and Chief Executive Officer of The Mullings Group Companies.

Remote diagnostics powered by AI are enabling earlier disease detection in rural areas, while new AI tools in drug discovery are unlocking treatments for previously difficult to treat targets.

Together, expanding therapeutic options for patients who need them most. “Expanding access is not just about technology. It’s about bringing advanced care to those who need it most, wherever they are.” Samantha Meadows, Private Markets and Thematics Research Analyst at UBS noted.

Barriers to care are falling as advanced technologies reach more clinicians and patients, especially in underserved communities.

Physical AI and robotics are helping to elevate the skill level of surgeons, making complex procedures more accessible. “You’ve got democratization of surgery with a robot, but you still have the access issue because they aren’t an inexpensive proposition in today’s environment.” observed Joe Mullings, Chairman and Chief Executive Officer of The Mullings Group Companies.

Remote diagnostics powered by AI are enabling earlier disease detection in rural areas, while new AI tools in drug discovery are unlocking treatments for previously difficult to treat targets.

Together, expanding therapeutic options for patients who need them most. “Expanding access is not just about technology. It’s about bringing advanced care to those who need it most, wherever they are.” Samantha Meadows, Private Markets and Thematics Research Analyst at UBS noted.

Despite rapid innovation, regulatory hurdles, data silos, and the need for robust clinical validation and workflow integration remain significant barriers to widespread AI adoption.

Application is slowed by the need for clinicians to learn new workflows. And is also impacted by regulatory pathways for new sensors and devices, with progress contingent on the FDA’s willingness to accept synthetic data and simulation as catalysts for advancing physical AI.

Jason Silvers, Chief Financial Officer at Generate Biomedicines, emphasized that while regulatory agencies have been receptive to AI-generated molecules in drug discovery, rigorous clinical validation and safety remain essential. “Regardless of how they’re made, we still need to complete all pre-Investigational New Drug (IND) studies and demonstrate safety and efficiency,” he explained. Silvers further noted that AI’s potential is vast, but technology alone cannot revolutionize drug discovery or clinical care. “Many companies attempt to solve every problem computationally, but biology is extraordinarily complex; without validating results in vivo and in humans, true breakthroughs are impossible.

For providers, integrating AI into clinical workflows and demonstrating clear ROI are critical for adoption.

Daniel Hawkins, CEO at Vista AI, emphasized, “Integrating AI into clinical workflows and making the ROI case can be difficult, especially with competitive data and large ticket sizes. That said, at Vista we have been able to do exactly that” Ultimately, ease of use and reimbursement/ROI are the key factors for AI utilization.

Despite rapid innovation, regulatory hurdles, data silos, and the need for robust clinical validation and workflow integration remain significant barriers to widespread AI adoption.

Application is slowed by the need for clinicians to learn new workflows. And is also impacted by regulatory pathways for new sensors and devices, with progress contingent on the FDA’s willingness to accept synthetic data and simulation as catalysts for advancing physical AI.

Jason Silvers, Chief Financial Officer at Generate Biomedicines, emphasized that while regulatory agencies have been receptive to AI-generated molecules in drug discovery, rigorous clinical validation and safety remain essential. “Regardless of how they’re made, we still need to complete all pre-Investigational New Drug (IND) studies and demonstrate safety and efficiency,” he explained. Silvers further noted that AI’s potential is vast, but technology alone cannot revolutionize drug discovery or clinical care. “Many companies attempt to solve every problem computationally, but biology is extraordinarily complex; without validating results in vivo and in humans, true breakthroughs are impossible.

For providers, integrating AI into clinical workflows and demonstrating clear ROI are critical for adoption.

Daniel Hawkins, Chief Executive Officer at Vista AI, emphasized, “Integrating AI into clinical workflows and making the ROI case can be difficult, especially with competitive data and large ticket sizes. That said, at Vista we have been able to do exactly that” Ultimately, ease of use and reimbursement/ROI are the key factors for AI utilization.

Physical AI is delivering measurable returns for providers and payers.

Jeffery Alvarez, Chief Strategy Officer of Moon Surgical outlined how AI delivers measurable returns for providers and payers by progressing operational models through physical platforms that reduce overtime, optimize finances, and labor deployment, and increase case output. He explained, “One of biggest impacts in cost is overtime. If you’re able to get through thirteen procedures in a day by using intelligent robotics to streamline labor deployment and everyone’s home by 5:00 PM, that’s of big value.”

Hospitals are seeing millions in downstream revenue from improved workflow efficiency, while drug discovery is benefiting from reduced R&D costs and faster timelines, thanks to partnerships with tech giants that unlock new infrastructure and data advantages.

However, as Simos Kedikoglou, President and Chief Operating Officer at Anumana cautioned, increased detection and intervention rates may drive up healthcare costs, and payers may struggle to keep pace with rising expenses for class-one interventions identified by AI. Thus, highlighting the need for system-level cost control and thoughtful reimbursement strategies that take into consideration documented patient benefit, especially in primary care and community settings.

Physical AI is delivering measurable returns for providers and payers.

Jeffery Alvarez, Chief Strategy Officer of Moon Surgical outlined how AI delivers measurable returns for providers and payers by progressing operational models through physical platforms that reduce overtime, optimize finances, and labor deployment, and increase case output. He explained, “One of biggest impacts in cost is overtime. If you’re able to get through thirteen procedures in a day by using intelligent robotics to streamline labor deployment and everyone’s home by 5:00 PM, that’s of big value.”

Hospitals are seeing millions in downstream revenue from improved workflow efficiency, while drug discovery is benefiting from reduced R&D costs and faster timelines, thanks to partnerships with tech giants that unlock new infrastructure and data advantages.

However, as Simos Kedikoglou, President and Chief Operating Officer at Anumana cautioned, increased detection and intervention rates may drive up healthcare costs, and payers may struggle to keep pace with rising expenses for class-one interventions identified by AI. Thus, highlighting the need for system-level cost control and thoughtful reimbursement strategies that take into consideration documented patient benefit, especially in primary care and community settings.

The promise of AI in healthcare is only as strong as the quality of data and talent behind it.

Panelists from the UBS-ARC sessions emphasized that robust, harmonized datasets are essential for training reliable models and driving meaningful innovation.

Liz Schwarzbach, Chief Business Officer at BigHat Biosciences emphasized, “the real challenge lies in having high-quality, well-annotated data to train your model.” Without it, even the most advanced machine learning algorithms risk producing unreliable results, making relevance and quality critical for every application.

The rapid evolution of AI has made specialized talent in machine learning and data science essential, with organizations facing growing challenges in recruiting and retaining experts needed to turn innovation into practical healthcare solutions.