Why AI alone won’t cut it in healthcare

The healthcare industry has reached an inflection point at which “AI everywhere” is more than a buzzword. It’s reshaping how care is delivered and experienced. Investments in artificial intelligence, particularly in clinical applications such as automated documentation and diagnostic support, have surged to tens of billions of dollars in recent years as hospitals and providers seek to improve efficiency and quality simultaneously.

Yet, the promise of AI transforming healthcare delivery remains contingent on more than just smarter algorithms; it hinges on tailored workflows, regulatory compliance, and meaningful clinician and patient engagement, a challenge that companies like Longevitix have been addressing by designing end-to-end systems that go beyond standalone AI models.

This nuance matters in an era in which headline announcements dominate but often obscure context. When OpenAI unveiled OpenAI for Healthcare in January 2026, it marked a major step toward integrating large language models into clinical workflows, including HIPAA-compliant support and physician-evaluation frameworks to reduce administrative burden and support documentation and research tasks.

Within days, Anthropic responded with Claude for Healthcare, a similar healthcare AI offering focused on safety, clinical documentation, and interoperability via integrations with CMS databases, ICD‑10 coding, and Fast Healthcare Interoperability Resources (FHIR) standards.

However, behind the headlines lies a central truth: the commoditization of core AI models has made sophisticated capabilities broadly accessible, yet access alone doesn’t guarantee clinical impact at scale. Where OpenAI and Anthropic provide generalized AI models, Longevitix accounts for the entire clinical workflow, offering granular, evidence-based, and configurable solutions that general AI cannot replicate.

Today, a wide range of diagnostic, summarization, and clinical decision-support tools are available beyond proprietary ecosystems. Whether it’s startups offering ambient listening in exam rooms or APIs that summarize patient charts, the underlying models, often built on the same generative AI foundation,s are broadly accessible. This commoditization is remarkable, but commoditized models are not healthcare solutions by themselves.

Healthcare isn’t like other tech-driven industries where deploying a new feature can be measured in weeks. Physicians in private clinics work within regulated workflows that include safety protocols, documentation requirements, reimbursement processes, and consent procedures. These constraints make it difficult to implement generic AI tools without careful integration and customization.

A related challenge is data fragmentation. Clinics face difficulties integrating patient data from medical histories, specialty labs (genetics, epigenetics, microbiome, hormonal panels), cognitive and functional assessments, intake questionnaires, progress notes, imaging, and wearable devices. Without a unified view, AI models risk working with incomplete context, limiting their clinical utility. OpenAI and Anthropic are attempting to address this through interoperability and secure data connectors, but only tailored workflows, such as those Longevitix provides, can reliably use this data across the entire preventive care journey.

Beyond regulated processes, AI’s role in healthcare remains fundamentally social. Physicians are adopting AI tools at a brisk pace, with recent surveys indicating that around two-thirds of U.S. physicians use AI in their practice, primarily for documentation and administrative tasks. Yet, significant reservations remain regarding clinical decision-making, overreliance, and the loss of human touch.

Those concerns are critical because preventive care, especially interventions around sleep, nutrition, exercise, and supplementation, requires sustained human guidance. AI may provide insights and reminders, but only clinicians can hold patients accountable over months, adjust interventions based on retesting, and ensure that evidence-based milestones are met. This blend of technology and human judgment is essential for meaningful outcomes.

This resistance reflects how healthcare outcomes still depend heavily on the physician-patient relationship. Empathy, context, and longitudinal understanding of a person’s life can’t be encoded in a model merely trained on massive datasets. These human aspects are not inefficiencies; they are core components of care that any AI strategy must respect and reinforce.

For companies like Longevitix, the argument isn’t that AI is irrelevant; it’s that AI without deeply integrated, evidence-based clinical workflows cannot meaningfully improve preventive care. Success requires aligning AI with trusted protocols, regulatory compliance, and clinician adoption to ensure that insights translate into sustained lifestyle change and measurable outcomes. This means building systems that align with the daily rhythms of care delivery, adapt to regulatory requirements, and produce measurable improvements in health behaviors and outcomes.

Early evidence suggests clinical AI can predict deterioration and optimize hospital workflows. However, for preventive care in clinics, AI’s success depends on patient engagement, behavioral coaching, and continuous monitoring, tasks that remain human-centric.

OpenAI and Anthropic’s moves underscore the momentum behind clinical AI, yet they highlight the limitations of generalist AI models in delivering fully realized care.

Longevitix is demonstrating that true transformation comes from embedding AI into human workflows, focusing on granular, configurable, evidence-based interventions that augment rather than replace clinicians. In a landscape of commoditized models, the distinction between algorithmic novelty and clinically meaningful impact is what will determine who actually improves patient outcomes.

This article was written in cooperation with Tom White