Male vs Female Voice Choices for Healthcare AI Bots: What the Research Suggests

When a healthcare organization deploys voice AI for scheduling or patient support, the first debates are often technical:

Will it understand accents? Can it integrate with our EHR? How can something non-deterministic follow deterministic scheduling rules?

But one of the most consequential decisions is also one of the easiest to underestimate:

What voice should the AI use?

In practice, the "male vs female voice" choice is about perceived gender cues (pitch, timbre, speaking style, name/persona). Those cues affect how people judge warmth and empathy, competence and trust, how much they disclose, and whether they will comply with instructions.

In healthcare, those shifts can affect containment, average handle time, escalation rates, and ultimately the patient experience.

What "Male vs Female Voice" Really Means in AI

Most voice AI systems don't have a "gender" built—in, per se. Meaning, the AI works exactly the same regardless if the voice is perceived as male or female. That perception comes from a bundle of cues:

• Acoustic cues of pitch, resonance, and prosody
• Word choice, such as "Sure!" instead of "Certainly."
• Error-recovery style of apologetic versus direct
• Persona cues, such as any name given, "assistant" framing, or voice style

Perhaps unsurprisingly for a large-language-model AI, punctuation matters. That period at the end of "Certainly." means as much as the exclamation point at the end of "Sure!". Say them both out-loud, and you'll hear yourself make that point. The important thing is knowing which one your AI voice is going to use with your patients for common interactions.

What the Research Suggests in Healthcare Contexts

1) Perceived Trust Can Change with Voice Gender Cues

In an Applied Ergonomics study on voice assistants delivering medication advice, participants rated agents perceived as female as more trustworthy, and participants relied heavily on the assistant's advice while making decisions. [1]

Implication for patient access: if the AI bot is giving scheduling related directions ("arrive 20 minutes early," "bring imaging," "don't eat after midnight"), small shifts in perceived trust can change compliance and downstream rework.

2) Warmth and Competence Perceptions Can Shift Too

A study of AI voice assistants in postpartum follow-up phone calls found female-voiced assistants were rated significantly higher for:

• warmth (female voice mean ~5.78 versus male ~5.49)
• competence (female voice mean ~5.90 versus male ~5.77) [3]

The authors discuss how context and participant demographics shape these results, an important reminder that voice effects are not universal.

Implication for patient access: in sensitive scenarios such as postpartum, behavioral health, and chronic disease coaching, a voice that elicits warmth can reduce friction. But in purely transactional scheduling, that same warmth might increase conversation length unless the dialog is designed to stay focused.

3) Voice Gender Can Affect User Behavior

A Johns Hopkins report on voice assistant gender design describes research where male participants interrupted voice assistants almost twice as often as female participants did, and where gender-neutral voice design reduced interruptions. [2]

Implication for patient access: interruptions can increase repair turns ("Sorry, I didn't catch that…"), repetition frustration, call duration, and ultimately escalation to your staff or contact center agents.

If your scheduling voice AI must handle high volumes, these effects can have a measurable cost.

Why This Matters Operationally in Patient Scheduling

Healthcare voice AI is usually expected to deliver three outcomes simultaneously:

1. Speed with short, successful calls
2. Confidence for patients to trust the result of their actions
3. Fairness with consistent, respectful interactions across patients

Voice choice influences all three. A practical way to think about it:

If a voice increases perceived warmth, you may see higher engagement and more effective disclosure, but also potentially longer (and therefore more costly) calls.

If a voice is perceived as being more competent, you may see higher completion rates and fewer escalations.

If a voice triggers biased behavior (interruptions, disrespect), you may see higher handle times and lower patient satisfaction.

Designing for Choice and Neutrality

UNESCO has argued that making voice assistants "female by default" can reinforce harmful stereotypes and has recommended ending "female-by-default" patterns and exploring neutral, non-human, or user-selectable voices. [4]

Practically speaking, a truly "neutral" voice is difficult to accomplish with modern voice AI technologies. And healthcare is particularly high-stakes, because the bot is often positioned as "help" or "support." You don't want "help" to always sound like a subservient persona, but you also don't want the system to unintentionally invite bias.

Practical Recommendations for Healthcare Voice Bots

1) Keep the Persona Professional, Not "Cute"

Most scheduling workflows benefit from 1) calm, concise, respectful phrasing 2) confident confirmations ("You're scheduled for…" versus "It looks like…") and 3) neutral names ("MDfit Scheduling Assistant") or relevant mascots, when branding supports it ("Buzzy the Pediatric Bear").

2) Design Error Recovery to Reduce Interruptions and Repair Loops

If your AI bot is often interrupted, look for three areas to improve: 1) turn-taking cues ("I'm going to ask two quick questions…") 2) Barge-in handling (pause, summarize, confirm) 3) Brief confirmations that keep momentum ("Got it." "Thanks.")

3) Measure What Matters

Run an A/B test where the only difference is the voice choice, and compare: completion rate, escalation rate, average handle time, patient satisfaction (CSAT), abandonment, repeat contact within 7 days, and any disparity across patient subgroups.

4) Treat Voice as a Governance Decision

Voice choices can be both "branding" and part of your access pathway. Make it ownable with one decision maker (between Patient Access and Marketing, for example). Define clear success metrics, like those listed in #4 above. And periodically review your strategy. Models and patient expectations will continue to evolve over the next several years.

The Bottom Line

Healthcare voice AI isn't just about speech-to-text accuracy, multi-language support, or fancy conversation design. The sound of the system changes what patients do, and how successful patients are using the system.

Don't overlook this important decision when looking for all the benefits voice AI brings to speed, access, and scalability.