For years, the promise of artificial intelligence in healthcare centered on what the technology might eventually deliver. Today, that conversation is becoming much more tangible.
AI is increasingly being applied across the healthcare journey, helping clinicians interpret complex information, supporting more personalized care, enabling remote patient monitoring, and reducing the ubiquitous administrative work that can pull healthcare professionals away from patients.
As both a physician and a technologist, I believe this is the lens healthcare leaders should use when evaluating AI: not simply asking what the technology can do, but what it can safely do for patients and the clinicians caring for them.
For hospitals and health systems facing staffing challenges, financial pressures, and growing volumes of health data, AI’s success should not be measured by how many new tools are deployed. It should be whether those tools help clinicians make more informed decisions, spend more meaningful time with patients, and ultimately improve care.
Turning More Healthcare Data Into Better Decisions
Modern healthcare generates an extraordinary amount of data, from electronic health records and medical images to laboratory results and connected devices. Many estimate that healthcare data is as much as one third of all of the world’s data, and that percentage continues to increase.
In my experience and that of my colleagues, more information does not necessarily make a clinician’s job easier. Without the right tools, it can create another layer of complexity, especially when that information is delivered without the proper context. This is where AI provides one of its clearest opportunities to improve care.
AI can help identify patterns, organize information, and surface insights at the right time and in the right context to support clinical decision-making. Applications already span medical imaging triage, visualization, personalized treatment planning, and predictive modeling.
Making complex information more actionable is one of the most promising applications of AI in healthcare. But AI should augment clinical expertise, not replace it. Too many patient-specific nuances remain unstructured or even uncaptured in the medical record. A clinician brings additional context, judgment, and an understanding of the patient that an algorithm or AI model alone cannot.
Making Care More Personal and Proactive
AI can also help make healthcare more personalized and proactive.
Connected devices and remote patient monitoring can provide information about patients outside traditional care settings. AI can analyze these data streams to help identify trends or surface data about specific changes that may warrant a clinician’s attention, while also reducing the “noise” and information overload.
We are already seeing what this can look like in practice. ElephasCare, for example, has developed an AI-powered patient monitoring solution that analyzes sensor data in real time and alerts caregivers when a patient may be at risk. According to ElephasCare, the technology has helped participating hospitals and long-term care facilities reduce falls, pressure ulcers, and emergency room visits by up to 50%. The sensors they use instead of cameras do it all while respecting patient privacy.
This is particularly compelling because it demonstrates how AI can make healthcare more proactive, identifying meaningful changes earlier rather than reacting to problems as they arise.
The opportunity extends to chronic condition management, long-term care, skilled nursing and care delivered in the home. Instead of relying solely on information collected during periodic appointments, care teams can potentially develop a more continuous picture of a patient’s health.
The goal is not for an algorithm to make decisions for a patient. Instead, the design should give healthcare professionals better information, sooner, so they can determine when intervention may be best.
Giving Clinicians More Time for Patients
Some of the most meaningful ways AI could impact patient care may be far less visible.
Healthcare professionals spend substantial time documenting patient visits, navigating electronic health records in search of information, dealing with insurers, and completing other purely administrative tasks. AI can help reduce some of that burden by assisting with clinical documentation, summarizing information, and automating many routine administrative processes.
We are already seeing evidence of that potential: A 2025 JAMA Network Open study found that using an ambient AI scribe was associated with 20.4% less time spent on notes per appointment and 30% less after-hours work time, along with a greater reported sense of engagement with patients.
Having practiced medicine, I know that time is one of the most valuable resources a clinician has. As a dad, I’ve also experienced that same administrative time taking away family time. If AI can reduce time spent navigating systems and completing repetitive tasks, that creates more capacity to listen to patients, answer questions, and think through complex cases while also improving work-life balance.
That means more than just a productivity improvement. It can improve patient care and clinicians’ family life at the same time.
Bringing Intelligence to the Point of Care
The impact of AI will also depend on making these capabilities available across the range of settings in which patients receive care.
Healthcare extends across hospitals, outpatient centers, long-term care facilities, skilled nursing facilities, and patients’ homes; each environment has different requirements for connectivity, computing power, security, and speed.
One thing has become increasingly clear to me: where computing happens matters. An AI application still needs to work securely, reliably, and quickly in the real-world environment where a clinician or patient needs it.
Some workloads may be best suited to cloud or data center environments, while others can benefit from processing patient data closer to where it is generated. When speed matters, processing should happen as close to the patient as possible.
This means healthcare organizations need secure, reliable infrastructure that can support AI across the full continuum of care, from data centers and cloud environments to edge computing and endpoints. Patients may never see that infrastructure, and in many ways, that is the point. Technology should work in the background so clinicians can focus on the care happening in the foreground.
Trust Must Come with Innovation
As AI becomes more integrated into healthcare, trust remains a foundational imperative.
Healthcare organizations manage deeply sensitive information. Leaders need to understand what data an AI system uses, where that data is processed, who can access it, and how it is protected.
Cybersecurity is therefore inseparable from AI adoption. The U.S. Department of Health and Human Services’ Healthcare and Public Health Cybersecurity Performance Goals highlight practices such as vulnerability management and endpoint protection as priorities for strengthening healthcare organizations’ cyber resilience. The stakes have never been higher. In the first half of 2026 alone, 397 large healthcare data breaches were reported to the HHS Office for Civil Rights, affecting 33.77 million individuals. As healthcare becomes increasingly connected and data-driven, protecting the systems and information that underpin care must evolve alongside innovation.
Earning trust in healthcare, with or without AI, requires more than just protecting data. Organizations also need to validate technology tools for their intended use before integrating them into care, clearly define when clinician review and intervention are required, and continuously monitor how these systems perform over time. AI models and the environments in which they operate can evolve, so responsible adoption requires ongoing oversight to ensure the technology continues to perform as intended.
I do not believe that healthcare organizations should view innovation and trust as competing priorities. In healthcare, trust is what makes innovation possible.
Human oversight must remain central. AI can surface information, recognize patterns, and reduce repetitive work, but clinical decisions require context, empathy, and judgment. Those remain distinctly human responsibilities.
Keeping Patients at the Center
As someone who has spent my career at the intersection of medicine and technology, I believe the most important question about healthcare AI is also the simplest: Did it make care better for the patient?
Did it help a physician identify important information sooner? Did it help recognize a change in a patient’s condition? Did it make care easier to navigate? Did it give a clinician more time to listen?
My work with healthcare organizations reinforces the importance of starting with real-world problems and then determining where AI can make a meaningful difference. We should not introduce AI simply because the technology exists and is heavily hyped. Rather, we should apply it where it genuinely solves a problem for a clinician, caregiver, or patient.
The future of healthcare AI should not be about putting more technology between clinicians and patients. It should be about using technology to remove barriers between them.
If we get that right, patients may not always notice the AI working behind the scenes. They may simply notice better care.
