Voice AI will dominate healthcare

Healthcare has long carried the label of being twenty years behind in technology.

But one area is breaking that cycle: voice AI.

What began as simple transcription is now evolving into a core layer of clinical workflows.

A leading voice in healthcare AI, Dr. Youssef Aboufandi, combines clinical experience with market analysis to identify early signals and translate them into insights on where the field is heading. Many of his published predictions have already proved accurate, giving rise to HAIF - Health AI Foresight.

With this vantage point, voice AI stands out not as a passing tool but as one of the most transformative shifts in modern medicine. This article explores how voice AI is being used today in healthcare, what is emerging, and where Dr Aboufandi believes it is heading next.

The Current Landscape

AI receptionist

Voice AI is already being used in clinics as AI receptionists, often linked to triage systems that guide whether a patient needs urgent care. This solves a long-standing issue: patients stuck on hold or forced to leave voicemails with no timely response.

But in countries like the United Kingdom, where the population is highly multicultural, dialects and accents create a major challenge, as I’ve seen firsthand in London hospitals. Research shows that speech recognition systems are often less accurate with certain accents, making fairness a practical barrier to adoption.

This is why speech detection and multilingual support are critical.

If it cannot understand the person on the other end of the line — their accent, their dialect, or their language — then it fails at the very first point of care.

Patients may not always default to English when unwell, and a receptionist powered by voice AI must be able to switch languages seamlessly.

AI pre-visit intake 

Before an appointment, patients are often asked to explain why they are attending and to describe their condition.

This can happen through online forms, but some health systems are beginning to use voice AI to make the process smoother.

AI pre-visit intake is most valuable when voice systems move beyond simply recording symptoms and start guiding the conversation like a clinician.

By prompting patients with follow-up questions shaped by probabilities, red flags, and prior history, they can turn free speech into structured, clinically meaningful data. Without that intelligence, the process risks adding noise instead of clarity.

AI scribe

The most visible use case remains the ambient scribe.

It listens during the consultation, capturing medical terminology and shorthand. Here, two factors are crucial: accuracy and latency. If either falls short, the tool becomes a burden rather than an aid. 

Studies show that clinicians spend a substantial portion of their workday, roughly one-third to one-half, on documentation and other EHR-related activities.  For many of us, that means hours of “pajama time” in the evenings, finishing notes after shifts.

AI scribes that are accurate and fast can give that time back — improving both clinician wellbeing and patient focus.

Some solutions now combine AI receptionists, pre-visit intake, and ambient scribing into a single platform that supports the patient journey end to end, even extending into monitoring after discharge and between visits.

What’s emerging? Talking to the EHR

Electronic health records have long been seen as graveyards for clinical data — full of information but difficult and time-consuming for clinicians to navigate.

That is beginning to change. 

The first voice-based electronic health record has already been rolled out, featuring an embedded clinical AI agent that enables clinicians to request labs, medications, imaging, or draft notes simply by speaking.

I expect others to follow suit as the benefits become evident.

The possibilities are clear:

• Handovers where the system has already logged and structured events from the day, so clinicians leave on time and colleagues receive a complete, data-rich summary.

• Ward rounds where observations are logged automatically and orders are placed immediately at the bedside, enabling faster care, earlier discharges, and better use of capacity.

• Emergency care where clinicians dictate observations on arrival, relevant past records are retrieved instantly, and the EHR is updated in real time to support life-saving decisions.

• Surgery where every instruction is captured as it happens, producing a full operation note and triggering follow-up tasks the moment the procedure ends.

In this model, the EHR shifts from a static database into an active, voice-enabled partner in care.

What’s next? Ambient companion wearables

I believe we’re entering the next phase of healthcare AI: the rise of ambient companion wearables. 

AI scribes have seen rapid adoption so far, but connecting that capability to a physical device changes the equation. Imagine a tool that sits with your doctor — worn around the neck or clipped as a pin — not only recording conversations but also retrieving information, updating the EHR, and executing voice commands in real-time.

The potential expands further when voice is paired with vision. While speech recognition captures the dialogue, cameras can add context: logging a patient's appearance and establishing a physical baseline during the visit. Over time, these signals could reveal subtle shifts — slower walking speed, tremors in speech, wound progression, or early signs of cognitive decline — all logged seamlessly into the record.

This future is closer than it may seem. OpenAI’s acquisition of Jony Ive’s startup io points to a compact, screenless device expected in 2026, designed to both see and hear.

While not built for medicine, the rise of such devices will carry strong healthcare applications — with smart glasses likely to emerge as another form of Ambient Companion Wearable.

Yet the integration of vision introduces new complexities — patient consent, data security, and clinical trust. Until those challenges are addressed, adoption will remain cautious.

The trajectory is clear: the next wave of healthcare AI will not just listen. It will become a supportive presence that listens, sees, and acts.

Ambient companion devices represent more than documentation support.

They move into continuous, contextual understanding of the patient, acting like an AI attending clinician at the bedside and easing both the administrative and cognitive load on healthcare professionals.

The human connection

Healthcare is embracing AI, but one constant will remain: the human element. The connection between people and machines depends on whether the technology makes sense of human speech.

If words are misheard, frustration grows quickly. If accents are not recognised, inclusivity and fairness are compromised. If languages are limited, patients and clinicians who are not fluent in English are excluded. And if medical terminology is misunderstood, clinical safety is at risk.

This is why I see speech detection, multilingual support, and medical-specific models not as minor technical details, but as the very foundation of voice AI in healthcare. In a workforce as diverse as the NHS, where accents and languages come from every corner of the world, accuracy and fairness are essential. Without them, voice AI cannot deliver on its promise.

Voice AI will dominate healthcare because it brings technology closer to how humans naturally communicate: through speech. When done right, it does more than save time. It makes care safer, more inclusive, and ultimately more human.