AI in Health Care: What do we expect in 2026? We're working on the annual "Predictions" issue of our practice management weekly Part B News. One of our areas of interest in AI in health care. We're inviting experts in the field to speculate on: What areas of health care will see use of AI increase — and whether it will expand beyond the administrative tasks in which they've become ubiquitous into clinical areas; What new related products are worth keeping an eye on; What AI "danger zones," if any — that is, operational categories in which AI is likely to be misapplied -- we're likely to hear about in 2026. Any other thoughts on the role of AI in health care in the short term would also be welcome.

Greetings, I'm Dr. Jon Stewart Hao Dy, a board-certified adult neurologist from the Philippines. I use AI in my everyday work as a clinician, educator, administrator and researcher. These are the areas of healthcare that I think and predict AI use will increase in: (1) Diagnosis of an individual's health condition: patients are increasingly using AI to explore more about their conditions based on the symptoms that they are experiencing. They do these even before they seek formal consultation with a healthcare provider (2) Laboratory and Imaging Test Interpretation: testing centers are using AI to automatically generate a personalized patient profile that will integrate their clinical and laboratory data in order to give a patient and their healthcare providers a complete and specific "personalized health profile" (3) Treatment algorithms and decision making: clinicians have enlisted the use of AI to develop logical and plausible treatment algorithms that will enable its validation, standardization and use across different patient populations from around the world. It's not exactly new but the increasing development of large language models (LLMs) in medicine in order to automate and streamline clinical diagnosis, radiographic/histopathologic interpretations and treatment decision making is becoming commonplace and I have personally reviewed scientific articles that use these LLMs to aide in their decisions (eg. Use of RapidAI software in assessing eligibility of a stroke patient to undergo thrombolysis). The danger zone with AI is always the overreliance of an individual on AI itself. AI doesn't just stand for artificial intelligence. I think it also stands for assistive intelligence. Yes, it has been extremely helpful in mundane and clerical tasks, exhaustive literature search, summarizing difficult concepts or lengthy papers and the like but AI was created by humans, who by our very nature are error-prone. AI cannot be the sole basis for one to rely on. I primarily use AI in my everyday work but I use it to assist me. I dictate how I use it and how I leverage it. Finally, I think AI in healthcare will be extremely valuable but it can NEVER replace the development of critical thinking and analysis needed to hone one's clinical acumen. Likewise, it cannot replace the humanity that AI cannot provide in everyday patient encounter. Thank you. Website Link to My Credentials: https://www.mymsteam.com/writers/685c6928ae4ebe421959cdfb

AI is finally moving from paperwork to people. In 2026 we'll see AI show up in the day to day moments patients actually struggle with, not just in billing or note taking. The big shift is lightweight clinical support, things like nutrition guidance, symptom triage, medication reminders, and early risk detection. These are areas where you don't need full diagnosis, you need good coaching and consistent follow through. We'll also see more products that blend models with sensors. Sleep, nutrition, activity, and glucose data will feed small predictive tools that help patients course correct earlier. The winners will be the ones that stay simple and actionable for the patient, not the ones that add complexity. The danger zone is over claiming clinical accuracy. It is easy for teams to stretch what models can do, especially when the product touches sensitive decisions like medication changes or diagnosing conditions. If you skip clinical guardrails or human review in the wrong places, the risk climbs fast. Short term, AI will play a bigger role in prevention and behavior change. Helping people eat better, sleep better, move more, and understand their own patterns is where AI can create real impact without pretending to replace clinicians.

1 / The administrative sector has already adopted AI technology through functions like dictation, coding, and scheduling, but healthcare providers are now focused on its potential in direct patient care. The immediate value of AI lies in decision-support systems that help identify risks and enforce medical standards while reducing human error, rather than replacing doctors. For example, AI tools used in pre-screening for mental health and dermatology need qualified professionals to validate the generated results. While AI improves healthcare access, organizations must establish strong governance protocols to use it responsibly. 2 / The next wave of valuable tools will integrate directly into clinical workflows, such as AI-based scan reporting and automated symptom triage systems that connect with EMRs. These tools must provide tangible benefits like time savings without sacrificing patient safety. One of our clients piloted an AI transcription tool that compared medical records with coding standards, and it significantly reduced errors, largely because they had well-defined procedures and properly trained staff in place. 3 / The greatest risks emerge when AI is used outside of structured processes. Our organization has cautioned against unmonitored chatbot symptom checkers, which can be unsafe, especially in mental health and pediatric care. Similarly, using AI-generated content for marketing or patient education requires human oversight to ensure compliance. All medical decisions and consent-related actions must involve human verification and maintain full documentation trails. Current AI applications are designed to support--not automate--healthcare operations. Clinics that see success with AI have done so by embedding its use into their operating procedures, verifying outputs, and incorporating staff feedback. No matter how advanced the tools become, healthcare professionals must still take full responsibility for patient care.

I've been building genomic data platforms for 15+ years and running Lifebit through the AI revolution in precision medicine, so I'm watching 2026 closely from the data infrastructure side that makes clinical AI actually work. **Clinical AI will explode in drug safety surveillance and pharmacovigilance.** We're already seeing pharma companies deploy real-time AI monitoring across federated health databases--our R.E.A.L. platform processes adverse event signals from millions of patient records without moving sensitive data. By 2026, expect AI to catch drug interactions and safety issues in weeks instead of years. The FDA's updated guidance on continuously-learning AI systems (mentioned in our policy review) will finally let these models improve without full re-approval each time. **The massive danger zone nobody's talking about: AI trained on non-diverse genomic datasets making clinical recommendations.** Over 80% of genetic research data comes from European ancestry populations. When an AI recommends a cancer treatment based on genomic markers, it might work brilliantly for some patients and fail catastrophically for others because the algorithm never learned their population's genetic variants. We'll see harm cases in 2026 where this bias becomes painfully visible--particularly in pharmacogenomics where drug metabolism varies significantly across ancestries. **Products to watch: federated AI platforms that analyze data where it lives rather than centralizing it.** Traditional AI needs data pooled in one place, which is increasingly illegal under GDPR and impossible for cross-border genomic research. The market estimated at $151B in 2024 hitting $469B by 2034 will be powered by infrastructure that brings analysis to distributed data while respecting privacy laws. Anyone building clinical AI without solving the data access problem first is building on sand.

I've launched products for tech companies from robotics to defense systems, and the pattern I'm seeing translates directly to healthcare: **AI will explode in patient-facing brand experiences and diagnostic product launches in 2026.** Medical device companies are starting to use the same interactive AI we built for Robosen's transformer robots--imagine a patient downloading an app that uses AI to guide them through physical therapy exercises with their new joint implant, tracking ROM and providing real-time form corrections just like our Buzz Lightyear robot responded to voice commands and environment. **The product category to watch is AI-powered packaging and unboxing experiences for prescription medical devices.** We created packaging for high-end collectibles that used QR codes linking to exclusive content and step-by-step reveals--now I'm seeing pharma companies pilot similar approaches where the medication box itself becomes an interactive tutorial. The patient scans their insulin pen packaging and gets an AI avatar demonstrating injection technique customized to their specific tremor patterns or vision limitations captured through their phone camera. **The danger zone nobody's talking about: AI-designed patient onboarding flows that optimize for engagement instead of comprehension.** When we redesigned Element U.S. Space & Defense's website, we obsessed over making complex technical specs accessible to multiple personas--but we had human experts validating everything. I'm seeing healthcare apps now that use AI to make medication instructions "more engaging" by shortening them or gamifying compliance, which terrifies me. An AI might test that patients interact more with a 30-second video than reading a warning label, but interaction doesn't mean they understood the contraindication. **Clinical AI will stay narrow in 2026 but go deep in imaging analysis for specific conditions.** The companies winning will be those who treat AI like we treated our Optimus Prime launch--premium positioning, obsessive attention to the "unboxing" experience of first use, and understanding that trust is built through transparent demonstration of capability, not hype.

I run a men's health clinic in Rhode Island, and I've been watching AI tools pop up in our space for the past two years. Here's what I'm seeing from the specialty practice side: **AI will push harder into patient triage for sexual health--and that's where we'll see problems.** I'm already getting guys who used symptom-checker apps that told them their erectile dysfunction was psychological, when our in-office testing revealed it was vascular or hormonal. In 2026, expect more telehealth platforms using AI to route ED and low testosterone cases directly to generic prescriptions without proper workup. We've caught early diabetes, undiagnosed hypertension, and pituitary issues during testosterone evaluations that an algorithm would have missed entirely. The danger is insurance companies will start requiring AI pre-screening to "reduce costs," which will delay men getting the diagnostic testing they actually need. **One product I'm tracking: AI-improved ultrasound interpretation for penile Doppler studies.** We use Doppler imaging to assess blood flow for ED cases, and reading those scans requires experience to catch subtle vascular abnormalities. If AI can flag borderline findings that a busy radiologist might rush past, that's valuable--but only if it's built on datasets that include our patient population, not just academic research cohorts. Most men's health AI tools I've seen are trained on data that doesn't reflect the 45-65 age range we treat daily. **The real 2026 story will be AI trying to replace the follow-up visit.** Testosterone therapy requires monitoring--hematocrit levels, PSA trends, symptom response. I'm seeing platforms that want to automate those check-ins with chatbots and auto-refill protocols. We've had patients whose hematocrit climbed dangerously high between visits, and catching that early prevented stroke risk. An algorithm trained on "average" responses won't catch the guy who's a high aromatizer or has sleep apnea amplifying his risk profile.

I ran Premise Data, where we had contributors in 140+ countries feeding us real-time ground truth on everything from disease outbreaks to supply chain disruptions. What I learned: AI is only as good as the data it's trained on, and in healthcare, garbage data creates dangerous confidence. **The expansion everyone's missing: AI moving into supply chain prediction for medical devices and pharmaceuticals.** We tracked medicine availability across developing nations in real-time using crowdsourced data. In 2026, expect AI systems that predict drug shortages, counterfeit medication flows, and equipment failures before they hit hospitals. Companies like Kinaxis are already building these models for pharma supply chains. The danger? Hospitals will over-rely on predictions without ground-truthing--I've seen governments make policy decisions based on models that were 6 months out of date. **The real danger zone is AI being deployed without transparency audits.** At Accela, we processed millions of government transactions and learned that when systems make decisions affecting people's lives, you need a human who can explain *why*. Healthcare AI in 2026 will face its "show me the algorithm" moment--especially when insurance denials or treatment recommendations come from black-box models. The providers getting sued won't be the ones using AI; they'll be the ones who can't explain what their AI actually did. **Product to watch: Olive AI's automation platform** (now part of Waystar after acquisition). They're moving beyond basic claims processing into clinical workflow optimization--figuring out which patients need follow-up calls, which prior authorizations will get denied, which lab orders are redundant. That's where administrative AI becomes clinically relevant without pretending to be a doctor.

By 2026, AI won't just be for office work anymore. I expect voice assistants will start handling patient intake or early triage. I've seen this go wrong when companies treat these bots like a perfect solution, especially in healthcare. If you're not checking them, assumed accuracy leads to missed errors. You need a feedback system to spot and fix problems fast. That's how you make AI a helpful tool instead of a liability.

By 2026, AI will move past just triaging symptoms. It will start predicting your health risks from your watch, your DNA, and other markers. I've seen this pattern: when AI pulls together all that data, doctors catch problems before you even feel sick. But it only works if a doctor is there to catch the AI's rare mistakes. So we need to build systems that keep doctors in charge, making AI a tool for their judgment, not a replacement for it.

I use AI image analysis to help visualize surgeries and it works pretty well. I bet we'll have smarter planning tools by 2026. But here's the thing. We can't let an algorithm call the shots on cosmetic work. The patient's goals are what matter. AI is a tool, not the decision maker. That part has to stay with the surgeon.

As a practicing ENT specialist, I see AI moving from the background to the clinical front line in 2026, especially in fields where pattern recognition can truly support decision-making. In my own world of sinus and airway care, I expect stronger imaging tools that help physicians read CT scans with more precision and flag subtle structural issues earlier. That kind of assistance improves accuracy without replacing the judgment that comes from years of examining real patients. I'm also watching AI-driven symptom-tracking tools. When patients can reliably log congestion, sleep quality, or post-procedure recovery trends, it gives doctors cleaner data to work with. I expect those platforms to grow quickly because patients already use similar tools in daily life. The area that makes me cautious is automated triage. It is easy to over-trust a system that sorts patients based on incomplete information. ENT problems often present in ways that look similar on the surface, and an algorithm that is not tuned to the nuances can delay proper care. Overall, AI will become more helpful in the short term when it is designed to support the physician's understanding rather than shortcut it. That balance is where the real progress will come from.

As someone who spends a large part of the day reviewing radiographs, I see the most natural growth area for AI in imaging support. The human eye can get fatigued, especially when moving quickly from one patient to another. Tools that can help identify patterns of decay or bone changes can reduce the chance of overlooking something that deserves a closer look. This is not about letting a computer decide for us. It's about gaining a bit more confidence and clarity. I also expect to see more tools aimed at helping practices stay organized. Dentistry involves an enormous amount of follow-up work, from coordinating cases to managing patient expectations. Anything that helps keep communication timely and clear will be valuable. My team and I spend a lot of time making sure patients understand the next steps, so this is an area I watch closely. One area I am cautious about is automatic treatment presentation. Dentistry involves trust. If a patient feels like decisions are driven by a system rather than a person, it can create unnecessary doubt. Treatment planning must always come from a thorough conversation, not from a generated recommendation. In the near future, I see the most promising role of AI as a supportive layer. It should help clinicians stay focused on the patient in front of them while quietly taking care of tasks that can pull attention away. When used thoughtfully, it can help make the care experience smoother and more personalized.

Looking ahead to 2026, I expect AI will continue expanding into clinical decision-making areas, particularly in utilization management and prior authorization processes. From my experience building UM programs across multiple medical specialties, I have witnessed the evolution from traditional peer-to-peer clinical reviews to AI-automated processes. While this automation brings efficiency gains, it also introduces a significant danger zone that healthcare organizations must monitor closely. The primary risk is that AI-driven automation may reduce the nuanced personalization that is essential to quality medical decision-making. As AI takes on more complex clinical tasks, healthcare leaders need to establish clear guardrails to ensure that efficiency does not come at the expense of individualized patient care. The organizations that succeed in 2026 will be those that find the right balance between AI-driven scalability and human-driven clinical judgment. This balance will be particularly important as payers and providers face increasing pressure to reduce administrative burden while maintaining care quality.

By 2026, we'll likely see AI in health care doing much more than basic paperwork. It will discreetly assist physicians in their day-to-day work by assisting them in identifying early indicators of health issues, better understanding patient trends, and providing more individualized care. We also expect wearable-based health tools and improved patient-record systems to become more common and useful. The risk is in handling patient data and in depending too much on AI without using sound human judgment. In the short term, AI will mainly make work faster and reduce routine tasks, while the real decision-making will still stay with doctors.

The area of health care that will massively increase its reliance on AI is that of medical prognosis. Today, trained human eyes look at X-rays and blood samples. They cross-examine what they see with what their patients' tell them, and do their best to give accurate prognoses. Despite their best efforts, medical professionals often misdiagnose the patient's conditions, which in turn can be very dangerous to their health. A number of tools are already being tested in hospitals, specifically around X-ray interpretation, and I've personally heard they are very good. And while I expect that medical professionals will start to worry about their jobs, I don't think that the human element will be removed completelly. I don't imagine any ai company wanting to be fully responsible for patient diagnosis as they would risk massive lawsuits. What is more likelly to happen is that these tools will massivelly increase the work output and correct interpretation of human-made diagnoses. In other words, they will make medical professionals more capable to do their work.

AI will expand into personalizing physical therapy and rehabilitation plans based on patient data and recovery needs. However, the operational danger lies in training programs that create a dependency that hinders the development of hands-on physical exam skills in trainees. To maintain quality, trainees must use AI only to generate a list of possible diagnoses, but they must be required to justify their final management plan using a traditional, hands-on physical assessment.

In 2026, the steepest AI growth in healthcare will still be on the "boring" side: revenue cycle, scheduling, documentation support and prior auth automation, because that is where the ROI and regulatory pressure are clearest. In the clinics my team sees, the next wave is triage assistants and imaging pre-reads that help clinicians prioritise rather than replace their judgment, especially in radiology and dermatology. On the product side, watch for AI-native compliance and audit platforms that monitor claims, access logs and documentation in real time - CMS' evolving audit rules more or less force that shift. The danger zones are opaque models making clinical suggestions without clear provenance, and small practices leaning on generic chatbots for medical advice or documentation without guardrails, which is a recipe for both safety incidents and compliance nightmares.

AI is going to keep gaining ground in 2026, but I see the real shift happening in the day-to-day realities of care rather than in big headline moments. In home care, the biggest lift will come from smarter tools that help us anticipate needs earlier. I expect AI-powered fall-risk monitoring, medication-adherence trackers, and cognitive change alerts to move from the experimental to the practical. These are tools that support caregivers and that distinction matters in senior care where trust is everything. I'm also watching the growth of AI-driven scheduling and care-planning software. When used well, it helps match clients with the right caregivers faster and reduces the gaps that can create stress for families. Products that combine health data with real-time observations from caregivers will be especially valuable because they give a fuller picture of what a senior is experiencing at home. The danger comes when systems rely on AI to make final judgments about a person's health or behavior. Seniors are nuanced. Their stories, habits, and small day-to-day changes cannot be reduced to predictions alone. The next year should be about pairing technology with professional insight so families get support that is efficient and still deeply personal.

As a urologist and medical expert, here's what I expect from AI in health care in 2026: AI will move beyond administrative tasks and become more clinically useful, especially in triage, imaging interpretation, and early detection of conditions like kidney stones, prostate cancer, and infections. We'll also see more patient-facing tools that improve follow-up, adherence, and post-procedure monitoring. Products worth watching include multimodal clinical assistants that combine labs, imaging, and notes into a single decision-support tool, as well as AI-guided point-of-care ultrasound for frontline evaluations. The main "danger zones" will be over-trusting unvalidated diagnostic tools, biased risk-prediction models, and AI-generated documentation that introduces errors clinicians must later correct. In the short term, AI will augment, not replace, clinical judgment, and the winners will be tools that truly reduce workload while maintaining safety and accuracy. Dr. Martina Ambardjieva, MD, Urologist, Teaching surgery assistant Medical expert for Invigor Medical https://invigormedical.com

In 2026, I expect AI in healthcare to move from being a process helper to becoming a true clinical intelligence partner. This won't be about replacing clinicians. It will be about tightening decision-making, reducing errors and accelerating workflows that traditionally slow the system down. The real shift will be AI moving closer to the point of care quietly informing decisions, surfacing risks earlier and removing friction from day-to-day clinical work, rather than trying to automate human judgment. Beyond administrative use cases I see major growth in clinical triage and decision support where AI helps surface patterns that clinicians simply don't have time to connect in real time. Diagnostics will increasingly be augmented, especially in radiology, pathology, dermatology and cardiology with AI acting as a second reader to improve accuracy and reduce fatigue-related misses. Population health and preventive care will mature with AI helping teams identify at-risk groups earlier and intervene before complexity and cost escalate. We'll also see more intelligent care navigation, giving patients clearer, more context-aware guidance before and after appointments. The most important products to watch will be those that integrate directly into clinical workflows. Predictive risk engines embedded inside EHRs, AI-assisted chart summarization tools, medication management and adherence intelligence and real-time operational systems for staffing and throughput will outperform standalone applications. The winners will not be the flashiest tools but the ones that quietly remove friction inside the systems clinicians already trust. That said there are real danger zones. Over-reliance on probabilistic outputs is a major risk, especially when "high confidence" is mistaken for clinical certainty. Bias in training data remains a serious concern, particularly for underserved populations where inequities can be amplified rather than reduced. And the biggest operational failures won't come from AI models themselves but from poor workflow integration creating alert fatigue, mistrust and low adoption. AI should never make healthcare less human. Its role is to give clinicians back their time, their clarity and their capacity to care. The organizations that will succeed are the ones that treat AI as a clinical teammate, not a software tool always supporting, never overshadowing and focused on raising the standard of care.