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Health care technology trends 2025: AI benefits, wearable use cases and telehealth expansion

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Description & tech

Deep-learning computer-vision models analyze head CT/CTA to detect large-vessel occlusions and auto-notify stroke teams on mobile devices.

Benefits

Multiple centers report faster “door-to-diagnosis” and transfer times, which are directly tied to improved outcomes in thrombectomy candidates (Hassan et al., 2022; Viz.ai clinical summaries, 2025).

Challenges

Integration cost, radiology workflow fit, false positives, and generalizability across scanners and sites. Human confirmation remains essential.

Personal note

In urgent care I don’t run stroke codes, but I see the downstream effects when transfers are smoother—families spend less time waiting for answers.

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2) Ambient AI for Clinical Documentation

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2) Ambient AI for Clinical Documentation

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Description & tech

Ambient AI “listens” to a clinician-patient conversation, uses speech-to-text plus medical NLU to generate SOAP notes that post to the EHR after review.

Benefits

Multisite studies show reduced time in notes and lower documentation burden within weeks of adoption (Stults et al., 2025; You et al., 2025).

Challenges

Accuracy in noisy rooms, specialty variation, HIPAA-grade security, and risk of over-reliance. Final sign-off must stay with the clinician.

Personal note

As someone who juggles intake, vitals, labs, and authorizations, ambient notes let our providers face patients more and the computer less.

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3) Wearables for Atrial Fibrillation Detection

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3) Wearables for Atrial Fibrillation Detection

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Description & tech

Consumer smartwatches use optical sensors and on-device algorithms to flag irregular pulse that may indicate AF. Positive notifications trigger confirmatory ECG or clinic follow-up.

Benefits

The Apple Heart Study enrolled >400,000 participants; notifications prompted meaningful follow-up and helped identify AF in the wild (Perez et al., 2019; Stanford Medicine, 2019).

Challenges

False positives, unequal device access, and unclear population-level benefit for asymptomatic screening.

Personal note

I’ve counseled patients who arrived because “the watch warned me.” The key is translating an alert into appropriate, timely evaluation.

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The Apple Heart Study app uses data from Apple Watch to identify irregular heart rhythms, including those from potentially serious heart conditions such as atrial fibrillation. Apple is conducting this research study in collaboration with Stanford Medicine to improve the technology used to detect and analyze irregular heart rhythms, like atrial fibrillation - a leading cause of stroke.

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4) Future Trends and Ethical Considerations

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4) Future Trends and Ethical Considerations

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On the near horizon, large multimodal models (LMMs) that take imaging, text, and signals together will power “reasoning-over-the-chart” assistants: summarizing prior notes, drafting orders, and cross-checking guideline eligibility in real time. Edge AI will move more inference onto devices—ambulances, point-of-care ultrasound, and wearables—reducing latency and protecting data locality. Federated learning will help institutions train models without directly sharing raw data.

Ethically, the center of gravity is governance and accountability. WHO’s 2025 guidance for generative AI in health calls for human oversight, safety and efficacy evaluation, transparency, data protection, and equity as first-class requirements (WHO, 2025). For documentation tools, informed patient notice matters: people should know when AI is recording and drafting notes, and consent flows must be clear. For diagnostic AI, external validation across diverse populations is non-negotiable—recent sepsis-prediction evaluations revealed performance gaps when models left their home institutions (Wong et al., 2021; Ostermayer et al., 2024). We also need post-deployment monitoring to catch model drift.

From my vantage point, the “right” deployment is team-centered: AI drafts and triages; clinicians decide and explain. That means: audit trails, override options, and patient-facing summaries in plain language. It also means widening access: if only well-resourced systems can afford reliable AI, we risk baking in new disparities. The promise is real, but the bar should be clinical benefit demonstrated in peer-reviewed studies, not slide decks.

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AI is shifting how work happens more than it is eliminating roles. In my clinic, ambient documentation takes hours off a week for physicians and advanced practice providers; that time reappears as eye contact, shared decision-making, and patient education. For imaging AI that accelerates stroke workflows, society’s upside is profound: fewer disability-years, faster returns to daily life, and lower inpatient costs (Hassan et al., 2022).

But the risks are societal too. Privacy is not just HIPAA compliance; it includes trust in how voice data or home-collected signals are processed and retained. Equity is a constant lens: if training data under-represent certain groups, error rates can fall unevenly, and benefits can cluster in well-insured populations. The sepsis-model story is a caution: widely deployed, poorly validated algorithms can quietly reshape care without delivering promised gains (Wong et al., 2021).

For me, the metric is dignity. AI that lightens cognitive load so clinicians can listen better serves dignity. AI that overwhelms staff with noisy alerts or that patients don’t understand undermines it. The path forward is transparent evaluation, patient-readable explanations, and procurement policies that require external evidence and equity checks. If we get those right, AI can expand access, shorten time-to-care, and lower the temperature in overwhelmed clinics like mine—without sacrificing the human core of medicine (WHO, 2025).

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I chose healthcare because it is where I work and study every day. As a full-time medical assistant in urgent care and a health-science student, I live the realities of triage, documentation, and patient counseling. AI is no longer hypothetical in these rooms; it is the dictation tool capturing our assessments, the triage alert pinging from the EHR, and the cloud service that flags a possible stroke on a head CT before the specialist has even parked.

On this Padlet, I’ll cover three concrete applications I interact with or could deploy in my setting:

(1) AI-assisted stroke triage from CT angiography.

(2) ambient clinical documentation that drafts the note from a live conversation.

(3) wearable detection of atrial fibrillation that can prompt earlier care.

For each, I outline the core technology, the tangible benefits, and the operational and ethical trade-offs.

What pulls me to this topic is the tension between speed and stewardship. In clinic, shaving minutes off a workflow changes a night for a family; at the same time, we’ve all seen algorithmic alerts that misfire, or promises that outpace evidence (Wong et al., 2021; Ostermayer et al., 2024). My goal here is not to cheerlead, but to map where AI demonstrably improves outcomes, where it reliably saves clinician time, and where governance and human oversight must tighten (WHO, 2025). By the end, I hope to show a practical, ethics-aware route for using AI to reduce documentation burden, get patients treated faster, and widen—not narrow—access to care.

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I started with a wide net—systematic searches of JAMA Network Open and WHO guidance—then narrowed to applications I see or could implement in urgent care: imaging triage, ambient notes, and wearables. The largest challenge was separating marketing claims from peer-reviewed outcomes; I addressed this by prioritizing multicenter studies and external validations. I also checked where evidence is mixed (e.g., sepsis models) to keep my conclusions honest. The biggest shift in my thinking is how governance and clinic workflow determine value as much as model accuracy. I now see AI as a teammate that drafts and triages while humans decide, explain, and stay accountable.

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World Health Organization. (2025). Ethics and governance of artificial intelligence for health: Guidance on large multimodal models. https://www.who.int/publications/i/item/9789240084759 — Rationale: Current global guidance for generative AI in health and governance anchors the ethics section.

World Health Organization. (2021). Ethics and governance of artificial intelligence for health. https://www.who.int/publications/i/item/9789240029200 — Rationale: Baseline principles that many national frameworks still reference.

Hassan, A. E., et al. (2022). Artificial Intelligence–Parallel Stroke Workflow Tool Is Associated With Reduced Time to Treatment. Stroke: Vascular and Interventional Neurology, SVIN. https://doi.org/10.1161/SVIN.121.000224 — Rationale: Peer-reviewed evidence that AI triage shortens treatment timelines.

Stults, C. D., et al. (2025). An ambient artificial intelligence documentation platform and clinician work time. JAMA Network Open, 8(…).* https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2833433 — Rationale: Multisite quality-improvement data on ambient AI’s impact on documentation burden. jamanetwork.com

You, J. G., et al. (2025). Ambient documentation technology and clinician burnout. JAMA Network Open. https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2837847 — Rationale: Survey-based outcomes on adoption and burnout change. jamanetwork.com

Wong, A., et al. (2021). External validation of a widely implemented sepsis prediction model. JAMA Internal Medicine, 181(8), 1065–1070. https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2781307 — Rationale: Cautionary evidence on generalizability of proprietary prediction models. jamanetwork.com

Ostermayer, D. G., et al. (2024). External validation of the Epic sepsis predictive model in two health systems. JAMIA Open, 7(4), ooae133. https://academic.oup.com/jamiaopen/article/7/4/ooae133/7900014 — Rationale: More recent external validation highlighting variability in performance.

Perez, M. V., et al. (2019). Large-scale assessment of a smartwatch to identify atrial fibrillation. New England Journal of Medicine, 381, 1909–1917. https://www.nejm.org/doi/full/10.1056/NEJMoa1901183 — Rationale: Landmark wearable AI study supporting Example 3.

Viz.ai. (2023). The clinical impact of Viz.ai [Video]. YouTube. https://www.youtube.com/watch?v=aWMOepUWhqo — Rationale: Clear, short explainer to embed under Example 1.

East Kent Hospitals NHS. (2023). How AI is helping to speed up stroke diagnosis [Video]. YouTube. https://www.youtube.com/watch?v=yaJ6HgGNzf4 — Rationale: Non-vendor case film for clinical context.

Abridge. (2023). Shiv Rao, CEO demos Abridge [Video]. YouTube. https://www.youtube.com/watch?v=qXPBXhUrjMc — Rationale: Concrete demo for Example 2 embedding.

Abridge. (2024). Enhancing nursing workflows with ambient AI [Video]. YouTube. https://www.youtube.com/watch?v=X5iOciKlYEY — Rationale: Nursing-specific angle on ambient AI.

Stanford Medicine. (2019). Apple Heart Study [Web page with video resources]. https://med.stanford.edu/appleheartstudy.html — Rationale: Authoritative landing page to link under Example 3.