How AI Is Being Used in Healthcare Beyond the Hype

At Bask Health, AI sits within our virtual clinic infrastructure through Basky AI, our built-in AI assistant for telehealth operations, and our questionnaire and patient portal builder, which uses intelligent routing to help the right patient reach the right care pathway. Here is a grounded look at how AI is actually being used in healthcare today, where the evidence is strongest, and where the limits still are.

Quick Answer: How Is AI Being Used in Healthcare Today?

• Clinical documentation is the single largest real-world deployment, with ambient AI tools drafting structured notes from live visits and saving clinicians multiple hours per week.
• Diagnostic imaging has the strongest regulatory track record, with more than 950 FDA-cleared AI radiology tools now on the market.
• Drug discovery is where AI is compressing timelines that once took decades, with several AI-discovered compounds now in clinical trials.
• Predictive analytics flags patients at risk for sepsis, readmission, and deterioration before a crisis happens.
• Administrative automation handles prior authorization, billing codes, and scheduling, the tasks consuming up to 70% of healthcare workers' time.
• Telehealth and virtual care operations use AI for intake routing, patient engagement, and operational analytics.
• What AI is not doing: replacing clinical judgment, working reliably without structured data, or operating without governance and human oversight.

The Category That Actually Broke Through: Ambient Documentation

Why Documentation Is Where AI Won First

Healthcare has experimented with AI across imaging, drug discovery, and predictive analytics for years. Most of those efforts remain in pilot stages or limited deployments. One category broke through at a pace that caught even optimists off guard: ambient AI documentation. Tools like Microsoft Nuance DAX and Abridge listen to a clinical encounter and generate a structured clinical note for the provider to review and sign off on, replacing the hours of manual charting that have contributed directly to clinician burnout.

A rapid review of real-world evidence on AI digital scribes, published in PMC in 2025, found that physicians currently spend up to twice as much time on electronic health record tasks as on direct patient care, and that ambient AI tools consistently reduced documentation burden across the clinical settings studied. The same review noted measurable improvements in clinician satisfaction and time available for patient interaction.

What Is Coming Next in Documentation AI

According to UPMC Enterprises' 2026 healthcare innovation analysis, ambient scribing is one of the use cases where investment is coalescing fastest, precisely because the return on investment is clear and the clinical risk is low. The progression being watched most closely is the integration of ambient documentation with clinical decision support, moving from assisting with notes to surfacing relevant guidelines and risk flags in real time during the encounter itself.

Diagnostic Imaging: The Strongest Regulatory Track Record

AI in Radiology, Pathology, and Dermatology

The landmark 2021 peer-reviewed analysis in the Future Healthcare Journal by researchers from Microsoft Research and University College London identified diagnostic imaging as the leading AI application in healthcare, noting that more than half of all AI medical devices approved in the USA and Europe from 2015 to 2020 were cleared for radiological use. That trend has accelerated: by 2026, more than 950 FDA-cleared AI tools exist for radiology alone.

What These Tools Actually Do

AI imaging tools do not diagnose independently. They flag, prioritize, and assist. A convolutional neural network trained on chest X-ray images can identify findings consistent with pneumonia and surface them for radiologist review. An AI algorithm for diabetic retinopathy screening can analyze retinal images at a scale no human team could match, making population-level screening economically viable in settings where it previously was not. In radiotherapy planning, AI tools have reduced the preparation time for head and neck and prostate cancer image segmentation by up to 90%, translating directly into shorter waiting times for treatment.

The Pattern That Holds Across Imaging

In each of these examples, AI performs the high-volume pattern-recognition task so the clinician can focus on the exceptions, edge cases, and judgment calls. That division of labor is where AI in healthcare consistently works best.

Drug Discovery: Compressing Timelines That Used to Take Decades

Where AI Is in the Drug Development Pipeline

Pharmaceutical companies are using AI to identify promising drug compounds, predict molecular interactions, and optimize clinical trial design. Companies including Atomwise and BenevolentAI have AI-discovered compounds currently in human clinical trials. The potential here is to compress drug discovery timelines, which traditionally run 10 to 15 years, with projections suggesting AI could reduce that window by 30 to 50% for certain compound classes.

AlphaFold as a Turning Point

DeepMind's AlphaFold protein structure prediction tool, which predicts the three-dimensional shape of proteins from their amino acid sequence, has been widely described as a genuine scientific breakthrough, opening new avenues for understanding disease mechanisms and targeting drug development in ways that were computationally impossible before. It is a concrete example of AI doing something in healthcare that is not just faster than a human doing the same task, but fundamentally different in kind.

Predictive Analytics: Catching Problems Before They Become Crises

Sepsis, Readmissions, and Deterioration Flags

Hospitals are deploying AI models that analyze continuous streams of patient data from electronic health records, vital signs monitors, and lab results to flag patients at elevated risk for sepsis, unexpected deterioration, or readmission within 30 days of discharge. The clinical value is in the early warning: an intervention triggered by a risk flag 12 hours before a patient becomes critically ill is categorically different from one triggered after the crisis begins.

What Makes This Work (and What Breaks It)

Predictive analytics AI is only as good as the data it is trained on and runs on. A model trained on data from one hospital system often performs worse when deployed at another with different patient demographics, documentation practices, or care protocols. This is one of the most consistently cited implementation challenges in the literature: a model that looks impressive in validation can underperform in a new real-world context without careful monitoring and recalibration.

Administrative AI: The Least Glamorous, Most Impactful Category

Prior Authorization, Billing Codes, and Scheduling

Healthcare workers currently spend up to 70% of their time on administrative tasks, according to multiple industry analyses. AI automation applied to prior authorization requests, clinical coding, and scheduling optimization is where some of the fastest and most measurable ROI in healthcare AI has appeared, precisely because these tasks are rule-based, high-volume, and do not require the kind of clinical judgment that makes AI deployment more cautious.

Why This Category Matters for Telehealth Brands

For direct-to-consumer telehealth brands, administrative AI touches the parts of the operation that patients never see but feel directly: how quickly an intake is processed, whether a prior authorization delays a prescription, and how accurately a billing code is assigned. Getting these right at scale without proportionally growing administrative headcount is one of the clearest operational advantages AI currently offers telehealth businesses.

AI in Telehealth: How It Shows Up in Virtual Care Operations

Intelligent Intake Routing

AI-supported questionnaires do not just collect information; they route patients based on what they report. A patient flagging a contraindication gets routed differently from one with a routine renewal request. A response pattern suggesting higher clinical risk surfaces to a provider's attention before the visit rather than during it. This is operational AI that directly affects clinical quality without requiring a separate AI product on top of the telehealth platform.

Patient Engagement and Follow-Up

AI-assisted patient communication handles routine follow-up messages, appointment reminders, and medication adherence prompts at a scale no human team could sustain across thousands of active patients. The clinical value here is in the continuity: patients who receive consistent follow-up after a telehealth visit are more likely to refill prescriptions, complete recommended labs, and return for follow-up care.

Operational Analytics for Healthcare Brands

AI tools that analyze intake completion rates, identify where patients drop out of a care pathway, and surface which care categories are underperforming give telehealth brands the operational visibility that used to require a dedicated data team. Bask Health's patient management tools, EMR, and e-prescribing system are built to provide this kind of structured data, making it available to the AI layer without requiring a separate data integration project.

What AI in Healthcare Is Not Doing

Not Replacing Clinical Judgment

Every category above involves AI assisting, flagging, routing, or drafting, with a clinician reviewing, deciding, and signing off. The peer-reviewed literature is consistent on this point, and the regulatory environment reinforces it. FDA-cleared AI tools are cleared as decision support, not autonomous diagnostic or treatment systems. The Microsoft Research and UCL authors of the top-ranking research on this topic put it precisely: AI in healthcare today resembles a signal translator, surfacing patterns from data, not a reasoning engine that can draw on clinical intuition and experience the way a physician does.

Not Working Without Governance

The organizations getting measurable results from healthcare AI in 2026 are the ones that treated workflow integration, monitoring, and governance as the product, not afterthoughts. UPMC Enterprises put it plainly in their 2026 analysis: the winners will be those who prove impact with governed, privacy-preserved data, who deploy AI with safety nets and continuous monitoring, and who treat workflow integration as the product itself.

Not Overcoming Bad Data

AI amplifies the quality of the data it runs on. A healthcare organization with fragmented, inconsistent clinical data does not achieve better outcomes when AI is layered on top of it. It gets faster fragmentation. This is the single most important prerequisite for any serious AI deployment in healthcare, and it is the reason that a connected clinical platform, where intake, documentation, prescribing, and fulfillment share the same structured data, is the right foundation for AI, not an alternative to it.

How Bask Health Approaches AI in Telehealth

Bask Health's approach to AI is grounded in exactly this principle: AI is most useful when it runs on clean, structured, connected data. Basky AI handles operational tasks, including questionnaire generation and validation, patient and business data summaries, and routine operational management, because these tasks rely on the structured data the platform already generates. The security and compliance framework that protects patient data across the rest of the platform also applies to the AI layer.

Conclusion

How is AI being used in healthcare? In 2026, the honest answer is: in the places where tasks are high-volume, rule-bound, and clearly defined, not in the places where clinical judgment, contextual wisdom, and patient relationships actually matter. Ambient documentation, diagnostic imaging, administrative automation, and intelligent routing are where the real-world results are accumulating. The hype is about replacing clinicians. The reality is about giving them their time back.

If you want to build a telehealth business where AI is embedded into the workflow rather than bolted on top of it, you can explore Bask Health's plans or talk to our team about what that looks like in practice.

References

1. Authors. (2021). Article available via PubMed Central. PubMed Central (PMC). https://pmc.ncbi.nlm.nih.gov/articles/PMC8285156/
2. Authors. (2025). Article available via PubMed Central. PubMed Central (PMC). https://pmc.ncbi.nlm.nih.gov/articles/PMC12513689/