Rohit Mahajan

AI voice agents just took a big step toward sounding more “human.” 

WellSaid Labs Inc., a developer of lifelike artificial intelligence voice technology, recently unveiled the next generation of its enterprise platform, introducing major upgrades to its audio studio that make producing natural, customizable speech faster and more intuitive.

WellSaid’s core technology synthesizes voices that sound natural and human-like using a proprietary AI model named Caruso. It was trained on exclusive licensed audio from professional voice actors, not on public sources. It uses its technology to produce natural speech in various styles that can account for accent, timbre, pronunciation, and other expressive elements.

“Today, enterprises require an AI voice solution that ships faster, sounds better, and enables the ability to scale quickly while meeting compliance standards,” said Chief Technology Officer Chris Johnson.

To bring high-quality audio to customers, WellSaid made audio up to 96 kilohertz the new standard, which produces natural clarity and more readily captures intonation and stress across synthesized voices.

That includes the capacity for designing phonetic spellings for acronyms, brand names, and borrowed terms. The platform also provides smart suggestions on potential phonetic spelling to assist users with getting pronunciation right.

In addition, according to a company press release, in order to bring high-quality audio to customers, WellSaid made audio up to 96 kilohertz the new standard, “which produces natural clarity and more readily captures intonation and stress across synthesized voices.”

That includes the capacity for designing phonetic spellings for acronyms, brand names, and borrowed terms. The platform also provides smart suggestions on potential phonetic spellings to assist users in getting pronunciations right.

The company also added 36 new voices covering global languages, including Arabic, Turkish, Persian, and 18 dialects, to enable localization of content. 

Voice Agents: A Burgeoning Industry

AI-powered voice generation has seen rapid growth as lifelike and expressive custom speech becomes increasingly sought after across industries. The market was valued at $3.5 billion in 2023 and is projected to reach $21.8 billion by 2030, according to market analysis firm Grand View Research. Companies such as Eleven Labs Inc. and Hume AI Inc. have raised significant capital this year, $180 million and $50 million, respectively, for voice cloning and generation technologies.

AI-generated voices now power intelligent voice agents that can understand natural speech, respond conversationally, and take action on users’ behalf. This trend has fueled a surge in voice-enabled technologies that sound strikingly human across devices, over the phone, and in customer interactions.

According to a recent Gartner Report, large enterprises are the primary adopters, accounting for over 70% of the market share in 2024, as they seek to improve operational efficiency and customer engagement. However, small and medium-sized enterprises (SMEs) are expected to see a faster growth rate due to the increasing affordability of Agentic AI solutions.

With human-like AI voices, companies can handle routine inquiries around the clock, schedule appointments, and personalize outreach. Voice-capable AI agents can also contact customers directly with updates and tailor interactions to individual preferences and histories.

The future market for AI voice agents is poised for explosive growth, driven by advanced conversational AI that provides personalized, human-like service across major industries. The market is rapidly maturing, moving beyond simple commands to offer autonomous and integrated solutions that reduce operational costs and enhance customer experience.

Please join us for our next informative Webinar, Designing, Deploying, and Scaling Voice Agents in the Healthcare Ecosystem, to be held on Wednesday, November 5, 2025. Click here to register.

You can read much more about how AI is redefining healthcare delivery and drug discovery in my first book, Quantum Care: A Deep Dive into AI for Health Delivery and Research. It’s a comprehensive look at how AI and machine learning are being used to improve healthcare delivery at every touchpoint. My second book, which will be released on November 17, Generative AI: Unlocking the Next Chapter in Healthcare, will focus on GAI and the impact of Agent AI on healthcare. Ordering details will be provided soon.

Rohit Mahajan

A recent study published by the World Economic Forum (WEF) says that to overcome challenges hindering meaningful adoption of AI and to realize AI’s full potential in healthcare, one of the six pivotal shifts for public and private leaders proposed by the report is equipping health leaders and clinicians with the necessary AI literacy to make informed strategic decisions that align with their vision.

A lack of fundamental knowledge about AI among leaders and decision-makers sparks societal concerns and hinders progress with AI. A comprehensive upskilling of leaders helps them grasp AI’s strategic purposes and foundational digital principles more effectively. Thus, enabling informed decision-making, effectively interrogating AI solutions, and building trust across the healthcare ecosystem. An enhanced AI literacy can also help health workers and patients trust and adopt AI tools, showing that the technology is here to complement, not replace, healthcare professionals.

The rapid transformation of healthcare and the life sciences by AI, encompasses clinical decision-making, drug discovery, and patient engagement, requires more than just technology; it necessitates an AI-literate workforce across executive leadership, technical teams, clinicians, researchers, and business functions.

In the following section, I discuss why introducing AI literacy among the clinical and non-clinical workforce matters:

 AI Literacy for Clinical Workforce

Clinical workforce, including doctors, nurses, and technicians, requires AI literacy to ensure patient safety, deliver high-quality care, and adapt to the evolving medical technology. Key reasons include:

• Enhancing diagnostic accuracy: AI algorithms can analyze vast medical imaging datasets, helping clinicians detect subtle patterns that may be imperceptible to the human eye. AI-literate clinicians can interpret and utilize these AI-generated insights more effectively to enhance diagnostic precision and efficiency.
• Strengthening clinical decision-making: AI-powered clinical decision support systems provide evidence-based recommendations and real-time alerts for patients at risk. An understanding of how these systems arrive at their conclusions allows clinicians to make informed decisions and better personalize treatment plans.
• Reducing burnout: AI tools automate time-consuming administrative tasks, such as documentation, transcription, and EHR management. AI-driven automations free up clinicians to focus on direct patient care, potentially reducing burnout and improving job satisfaction.
• Maintaining ethical standards: Clinical staff must recognize the potential for bias in AI algorithms, which can perpetuate or even amplify existing health disparities. AI literacy enables them to assess tools critically, protect patient privacy, and ensure fair, equitable care for all populations.
• Ensuring safety and accountability: Over-reliance on AI without understanding its limitations can lead to diagnostic errors and patient harm. An AI-literate workforce is better equipped to oversee AI applications, check for system failures or “hallucinations,” and ensure accountability for their use.

 AI literacy for non-clinical workforce

Non-clinical workforce, including administrative assistants, IT personnel, and managers, can use AI to automate routine processes, manage resources efficiently, and securely handle sensitive data in the following manners:

• Streamlining operations: AI-driven systems can efficiently manage scheduling, process claims, and handle billing, thereby reducing manual workloads and minimizing errors. Non-clinical workers must understand how these systems work to implement and effectively oversee them.
• Enhancing financial performance: Utilize AI for tasks such as revenue cycle management and fraud detection. A literate team can leverage AI analytics to identify inefficiencies, control costs, and maximize revenue, ultimately contributing to the organization’s financial health.
• Enhancing data security: AI systems manage and process vast amounts of sensitive patient information. Non-clinical staff with AI literacy can understand the security risks and are equipped to protect data through access controls, encryption, and compliance with regulations such as HIPAA, SoC2, FHIR, and more.
• Optimizing resource allocation: AI can help forecast patient demand and staffing needs by analyzing historical data and trends. Administrative and managerial staff should be AI literate to effectively use these predictive tools for scheduling and resource management, thereby preventing both under- and over-staffing.
• Enabling interdisciplinary collaboration: The successful implementation of AI requires collaboration between technical developers and health system staff. AI-literate non-clinical employees can better communicate organizational needs and clinical insights to technical teams, ensuring that AI solutions are user-friendly and clinically relevant.

Why AI Literacy Matters in Healthcare

For AI to fulfill its promise as a transformative partner in healthcare, clinical and non-clinical professionals must go beyond simply using the technology without really understanding it. Actual adoption requires a workforce that recognizes both the strengths and the limitations of AI, and knows how to apply it responsibly, ethically, and effectively.

Building AI literacy across healthcare organizations is not just a technical imperative; it’s a cultural one. When executives, clinicians, researchers, technical teams, and business leaders share a common understanding of AI and the business goals of using it, the results go beyond improved workflows. They foster innovation, enhance patient experiences, and ensure that AI is embedded in a way that is trustworthy and sustainable.

An AI-literate workforce enables:

• A shared foundation of understanding across roles and disciplines.
• Responsible integration of AI into areas such as genomics, clinical workflows, and business operations.
• Empowered technical teams who can leverage modern AI coding tools for rapid innovation.
• A culture of experimentation through collaborative forums like hackathons and challenges.
• Continuous knowledge-sharing that keeps pace with AI’s rapid evolution.

AI literacy is not about teaching everyone to code; it is about equipping healthcare professionals to ask the right questions, make informed decisions, and embrace AI as a partner in improving outcomes. In this way, literacy becomes the foundation for trust, adoption, and transformation.

Agentic AI and, more specifically, AI Voice agents are having a transformative impact on healthcare. From bringing real-time reasoning to clinical and operational workflows to reducing physician burnout, AI Agents are changing healthcare for the better and improving patient experiences.

Why now?

• Conversational models are becoming more affordable and widely available (e.g., OpenAI cut real-time API costs by up to 87.5% in late 2024).
• Enterprises are rapidly deploying them – 25% of enterprises will use AI agents by end of 2025, growing to 50% by 2027 (Deloitte).
• In healthcare, voice AI is moving from “nice-to-have” to foundational for digital transformation (Deepgram State of Voice AI, 2025).

There are five real-world problems faced by the healthcare industry that AI agents can – or already are – solving.

Administrative Overload and Staff Burnout

Healthcare professionals, particularly physicians, often face significant stress and burnout due to the heavy administrative burden, spending a substantial portion of their time on tasks such as paperwork, documentation, scheduling, and billing. AI agents can automate and streamline many of these tasks, freeing up valuable time for medical staff to focus on direct patient care and reducing the cognitive load associated with repetitive administrative duties. For instance, AI-powered systems can automatically transcribe patient conversations into EHRs in real time, generate discharge summaries and clinical notes, handle appointment scheduling,      reminders, and billing queries, and even assist with insurance pre-authorizations and claims processing.

Diagnostic Inaccuracies and Delays

Traditional diagnostic methods can be time-consuming and prone to human error or variations in expertise. AI agents, particularly those trained on vast datasets of medical images and patient information, can enhance diagnostic accuracy and speed. For example, AI systems can analyze X-rays, MRIs, and CT scans to detect subtle abnormalities that might be missed by human observers, leading to earlier disease detection and more effective treatments. They can also act as powerful tools for identifying diseases like cancer and diabetic retinopathy and also deliver AI-driven recommendations to care teams.

Voice agents, combined with GenAI and multimodal models, enable real-time feedback loops—helping physicians act faster and with greater accuracy.

Gaps in Care Coordination and Patient Engagement

Traditional care models often suffer from fragmented communication and a lack of consistent follow-up, which can negatively impact patient outcomes. AI agents can enhance care coordination by acting as digital companions that provide continuous patient monitoring, engage in personalized communication (like medication reminders) and symptom tracking, and alert care teams to potential issues or deviations from treatment plans, and also offer multilingual, natural language engagement.      This helps bridge the gap between in-person appointments and empowers patients to be more proactive in managing their health.

Inefficient Hospital Operations and Resource Allocation

Hospitals frequently struggle with operational inefficiencies, such as overcrowding, suboptimal resource allocation (like managing ICU beds or surgical suites), and supply chain management challenges. AI agents can help address these issues by analyzing real-time data to predict patient flow, optimize staff scheduling based on patient needs and credentials, manage inventory of medical supplies and automate equipment monitoring,      assist with predictive maintenance for equipment, reduce call center load by handling navigation and FAQs, and provide real-time multilingual support for patient navigation.      This leads to smoother operations, reduced wait times, and a more efficient use of resources.

Challenges in Drug Discovery and Personalized Medicine

The process of discovering and developing new drugs is typically lengthy and costly. AI agents can accelerate this process by analyzing vast datasets of chemical structures and clinical trial information to identify potential drug candidates and predict their effectiveness. Furthermore, by analyzing patient genetic and lifestyle data, AI agents can support personalized medicine by helping physicians tailor treatment plans, predict how individuals will respond to medications (pharmacogenomics), and even recommend alternative ways to use existing drugs. AI agents also support clinical trial recruitment, protocol optimization, and compliance monitoring.

Pharma companies are piloting autonomous voice agents for prior authorization, drug substitution, and patient adherence monitoring—saving both time and cost in treatment delivery

Bottom Line: Voice Agents Are Shaping the Future of Healthcare

Next-generation voice assistants are set to revolutionize healthcare by providing intuitive, real-time, and emotionally aware interactions. From virtual nurses and mental health support to chronic disease management and emergency response, these AI-powered assistants are enhancing patient experiences and improving clinical efficiency.

• Voice agents can streamline patient interactions, reduce call center load, and provide 24/7 engagement in healthcare and pharma.
• The underlying advances in natural language understanding and generative AI have made voice agents more intelligent and less reliant on scripted flows.
• Enterprises are rapidly deploying voice agents across functions like customer support, scheduling, and complex operations to drive operational efficiency and enhance user experience.
• Voice assistants are no longer standalone technologies; they are integrated with computer vision, biometric data, and IoT devices to provide a holistic experience.
• In smart hospitals, for example, a voice assistant could monitor a patient’s vitals, provide medication reminders, and alert doctors if any anomalies are detected.

Healthcare is under pressure to do more with less. Agentic voice agents offer a proven way to deliver better care, lower costs, and more satisfied patients—while helping clinicians reclaim their time.

Rohit Mahajan

Wearables and the Internet of Things (IoT) have been integral to the way that Generative AI is transforming healthcare. Apple’s latest contribution to this revolution comes in the form of a model that can detect pregnancy with 92% accuracy by analyzing behavioral data from iPhones and Apple Watches. 

The findings are detailed in a study titled “Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions.” This study introduces the Wearable Behavior Model (WBM), which relies on higher-level health metrics—like sleep quality, mobility, and heart rate variability—rather than just raw sensor data, which can often be noisy and hard to interpret.

The WBM was trained using over 2.5 billion hours of wearable data and outperformed older models that relied on low-level sensor inputs. Researchers built a pregnancy dataset from 430 pregnancies, using Apple Health, HealthKit, and photoplethysmography (PPG) data. They labelled the nine months leading to childbirth and one month after as “positive” weeks—representing active pregnancy or postpartum recovery—while other weeks were marked “negative.”

The research comes from the Apple Heart and Movement Study, which collected over 15 billion data points from more than 162,000 participants. The data came through the everyday use of the Apple Watch and iPhone. For the pregnancy research, the model analyzed information from 430 reported pregnancies and more than 25,000 non-pregnant participants. The AI looked at more than heart rate and temperature. It also examined movement patterns, sleep habits, and exercise routines.

One of the key advancements of WBM is its use of expert-designed algorithms to convert raw sensor readings into meaningful behavioral metrics. These metrics are not only clearer but also better aligned with real health states. 

The model allows for more precise tracking and prediction of health changes over time, marking a significant step forward in wearable-based health monitoring and AI-powered diagnostics.

Pregnancy was just one of several health conditions the WBM model learned to identify. The researchers also tested the model on other health issues with strong results. It predicted diabetes with 82% accuracy, infection with 76% accuracy, and injury with 69% accuracy. These findings suggest that AI-powered wearables may soon do much more than count steps or track sleep. They could help detect serious health changes before symptoms even appear.

Even with these promising results, trust remains a major barrier in women’s health technology. Privacy concerns are growing, especially when it comes to sensitive data like menstrual cycles or pregnancy. In 2023, the Federal Trade Commission fined the popular app Premom for sharing user data without consent.  

A recent FTC study confirmed growing skepticism. Women are less likely to trust apps that collect reproductive health information, especially when the companies do not make their data practices clear. Even if the Apple Watch can detect early signs of pregnancy, would users want it to? This is a key question, one that echoes privacy and ethical concerns across the board as generative AI becomes more ubiquitous in healthcare. Because of such concerns, Apple has not announced any plans to turn the research findings into a consumer feature. But this research shows where Apple’s focus may be headed. With support from public health officials calling for widespread use of wearables, Apple could play a key role in shaping the future of personalized healthcare.

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Please join us for our next informative Webinar: Designing Voice Agents That Work – Real-World Use Cases, Tools, and Strategies to be held on August 27. Click here to register.

You can read much more about how AI is redefining healthcare delivery and drug discovery in my first book, Quantum Care: A Deep Dive into AI for Health Delivery and Research. It’s a comprehensive look at how AI and machine learning are being used to improve healthcare delivery at every touchpoint. My soon-to-be-released second book – Generative AI: Unlocking the Next Chapter in Healthcare will focus on GAI and the impact of Agent AI on healthcare.