As artificial intelligence continues to face public scrutiny across multiple sectors, technology companies are increasingly turning to healthcare as a proving ground for the technology’s potential benefits-and as a pathway to improving its tarnished reputation.

The healthcare industry has become a focal point for AI development and deployment, with major tech companies and pharmaceutical giants investing billions in applications ranging from clinical documentation to drug discovery. This strategic push comes at a time when public opinion on AI remains deeply divided, with concerns about job displacement, environmental impact, and data privacy casting long shadows over the technology’s rapid advancement.

According to a recent survey from the American Medical Association, 81% of physicians now use AI tools to stay current with medical research, create discharge instructions, document medical visits, and perform other clinical tasks. This widespread adoption signals a fundamental shift in how healthcare professionals approach their daily workflows, even as broader questions about AI’s role in society remain unresolved.

The scale of investment in healthcare AI reveals the sector’s growing importance as both a technological frontier and a public relations battleground. In January, computer chip manufacturer NVIDIA and pharmaceutical company Eli Lilly announced plans to invest up to $1 billion in creating an AI lab focused on drug discovery. More recently, in October 2025, Lilly announced it would build what it claims is “the most powerful supercomputer owned and operated by a pharmaceutical company,” in collaboration with NVIDIA.

This supercomputer, the world’s first NVIDIA DGX SuperPOD with DGX B300 systems, is powered by more than 1,000 B300 GPUs and delivers over 9,000 petaflops of AI performance-meaning it can process over 9 quintillion mathematical operations every second. The infrastructure will enable scientists to train AI models on millions of experiments to test potential medicines, dramatically expanding the scope and sophistication of drug discovery efforts.

“Lilly’s mission is to make life better for people around the world, and today that requires excellence not just in science but also in technology,” said Diogo Rau, executive vice president and chief information and digital officer at Lilly. “As a 150-year-old medicine company, one of our most powerful assets is decades of data. With purpose-built AI models and AI, we can set a new scientific standard that accelerates innovation to deliver medicines to more patients, faster.”

The strategic nature of these investments extends beyond pure scientific advancement. According to reporting from the Wall Street Journal, AI company Anthropic, the makers of Claude, is actively promoting AI use in healthcare to improve the technology’s reputation and instill confidence in potential investors. This approach reflects a broader industry recognition that healthcare applications-with their clear potential to save lives and improve patient outcomes-may offer the most compelling narrative for winning over skeptical audiences.

Despite the industry’s optimism and substantial investments, public acceptance of AI remains a significant challenge. Recent polling data paints a picture of widespread concern about the technology’s trajectory and implications.

A YouGov/Economist poll found that 71% of Americans believe AI development is moving too fast, while an NBC News survey revealed that 57% of voters think the risks of AI outweigh its benefits, compared to just 34% who hold the opposite view. These concerns stem from multiple sources: potential copyright issues with AI training data, environmental impact from energy-intensive data centers, job displacement fears, and the proliferation of data centers associated with increased utility bills and heavy water use.

Perhaps most tellingly for the healthcare sector specifically, a study from Ohio State University found that public openness to AI use in healthcare fell from 52% approval in 2024 to just 42% in 2026-a 10-percentage-point decline in just two years. This trend suggests that even in an application area with obvious potential benefits, public trust is eroding rather than building.

Additional research from Heartland Forward reinforces this skepticism, finding that 79.4% of respondents stated they do not trust AI to provide accurate information about healthcare. Meanwhile, an Annenberg Public Policy Center survey found that nearly half of Americans (49%) are not comfortable with healthcare providers using AI tools rather than their experience alone when making decisions about patient care.

This disconnect between industry enthusiasm and public wariness creates a significant challenge for companies betting on healthcare AI as a reputation-building strategy.

Despite public skepticism, healthcare professionals are finding concrete benefits in AI applications, particularly in areas that reduce administrative burden and improve workflow efficiency.

Dr. Michael Sjoding, a pulmonary and critical care physician at Michigan Medicine, highlighted the positive impact of “ambient AI scribes” that help summarize medical appointments. “When you go to the doctor, they might ask you now, ‘Hey, can I record this conversation?’ using a program that’s typically on our phone,” Sjoding explained. “That conversation can listen to the entire medical conversation that you’re having, and that tool can then draft the medical notes for the doctor, which the doctor would then hopefully edit and finalize.”

This application addresses one of healthcare’s most persistent challenges: the administrative burden that pulls physicians away from patient care. A survey among 67 health systems found that ambient notes for clinical documentation achieved 100% adoption, with a 53% success rate. The same survey identified reducing caregiver burden and improving satisfaction as the most cited organizational goal for deploying AI, with 72% of respondents prioritizing this objective.

Beyond documentation, AI is being deployed for clinical applications such as early sepsis detection, which 67% of surveyed health systems have implemented, though only 38% reported high success rates. Scientists are also using AI to analyze entire genome sequences, predict patient outcomes, and explore biochemical possibilities that would be impossible to examine manually.

Thomas Fuchs, chief AI officer at Lilly, emphasized the transformative potential: “Our foundation models are spawning new possibilities for our chemists, helping them uncover new motifs and configurations of atoms that were out of reach with traditional methods. AI gives us the means to accelerate progress toward both developing and delivering better, more personalized and targeted medicines.”

While the potential benefits are substantial, healthcare organizations face considerable obstacles in implementing AI systems effectively. A survey of healthcare executives revealed that only 30% of AI pilot projects make it to production, primarily due to security and integration issues.

The most significant barriers to AI adoption were identified as immature tools (77% of respondents), financial concerns (47%), and regulatory uncertainty (40%). These challenges highlight the gap between the theoretical promise of AI and the practical realities of deploying these systems in highly regulated healthcare environments where patient safety and data privacy are paramount.

Additional challenges span equity concerns, accountability questions, data privacy issues, the need for robust digital infrastructures, and workforce skills development. As one research summary noted, “AI has the potential to revolutionize public health practice and research, but accompanying challenges need to be addressed.”

To advance public acceptance of AI in the medical field, experts emphasize that rigorous validation of AI-driven solutions is required, along with ethical frameworks that address concerns about bias, transparency, and human oversight.

Despite current skepticism, there are signs that attitudes may be gradually shifting. The Annenberg Public Policy Center found that most Americans (63%) think AI-generated health information is somewhat (55%) or very (8%) reliable, and approximately 79% of U.S. adults are likely to search online for answers to health questions, indicating strong reliance on digital health information.

Furthermore, 95% of healthcare executives believe that generative AI will be transformative for the sector within three to five years, reflecting strong market expectations. Healthcare organizations are increasingly moving toward internal development of AI tools, with 60% of executives reporting that their AI budgets are growing faster than IT spending.

Kimberly Powell, vice president of healthcare at NVIDIA, framed the broader context: “The AI industrial revolution will have its most profound impact on medicine, transforming how we understand biology. Modern AI factories are becoming the new instrument of science-enabling the shift from trial-and-error discovery to a more intentional design of medicines.”

Lilly’s investment in sustainability alongside its AI infrastructure suggests awareness of broader public concerns. The company has committed to running its supercomputer on 100% renewable electricity within existing facilities and using existing chilled water infrastructure for liquid cooling, in accordance with its goal of achieving carbon neutrality by 2030.

The company’s broader $50 billion commitment to expanding its U.S. manufacturing and R&D footprint-including four new facilities and a proposed $4.5 billion lab in Indiana called the Lilly Medicine Foundry-demonstrates the scale of industry conviction that AI will fundamentally transform pharmaceutical development and manufacturing.

The healthcare sector’s embrace of AI represents both tremendous opportunity and significant risk. If successful, AI applications in drug discovery, clinical documentation, medical imaging, and personalized medicine could accelerate breakthroughs, reduce costs, and improve patient outcomes on a massive scale. These successes could, in turn, improve public perception of AI more broadly.

However, the declining approval ratings for AI in healthcare-from 52% in 2024 to 42% in 2026-suggest that industry enthusiasm alone is insufficient to build public confidence. The gap between professional adoption (81% of physicians using AI tools) and public comfort (only 42% approve of AI in healthcare) indicates a communication challenge that may require more than technological advancement to resolve.

As Fuchs noted, “Lilly is shifting from using AI as a tool to embracing it as a scientific collaborator. By embedding intelligence into every layer of our workflows, we’re opening the door to a new kind of enterprise: one that learns, adapts and improves with every data point.”

Whether this vision of AI as collaborative partner resonates with the public-or deepens concerns about human displacement and loss of control-may determine not only the technology’s reputation but also its practical trajectory in healthcare and beyond. The healthcare industry’s bet on AI as a reputation-building strategy represents a high-stakes experiment with implications that extend far beyond medicine into the broader question of how society will integrate artificial intelligence into critical human services.