VoiceAI in Medicine: From Theoretical Precision to Operational Reality

By Rodrigo Rebollar and Ángel Mora, Specialist Lead at SDG Group

A new paradigm is emerging in healthcare: voice artificial intelligence (AI) has begun to take hold in medical consultations. Its function goes beyond listening and transcribing—it assists healthcare professionals in real time, promising to humanize medical care and provide greater control over diagnosis and early disease detection. However, as this technology moves from controlled pilots to large-scale deployment, the evaluation of its success is shifting from simple accuracy to resilience and operational value. What is really needed for it to work in the real world?

Today's healthcare environment is marked by increasing pressure, with overcrowded schedules and burnout painting a complex picture. The heavy burden of documentation adds to this, with new digitalized advancements such as the Electronic Health Record (EHR) bringing complexity to routine. Manually documenting consultations not only consumes time spent with the patient, but also fragments the professional's attention between the person and the screen.

This is where voice AI comes into play. Based on advanced speech recognition models and Natural Language Processing (NLP), this technology captures the clinical conversation, transcribes it, and extracts the relevant information to generate a draft report in the EHR. The professional always retains final control, but is freed from the mechanical task of typing.

From Transcription to Insight: The Role of Cognitive Platforms

The most advanced solutions go far beyond speech-to-text conversion. They are configured as cognitive platforms that integrate voice AI with Business Intelligence (BI) technologies to transform conversations into actionable insights. An example of this evolution is YOURVOICE, from SDG Group, a platform designed to analyze communications and extract value. Applied within the healthcare sector, it has a wide impact:

• 360º View of the Patient: Consolidation of unstructured information from the conversation (patient questions, family context, emotional barriers) with structured data from the EHR (diagnoses, analytics). This provides a much deeper understanding of the patient's condition and their personal journey.
• AI-Powered Clinical Analytics: Processing of interactions to perform sentiment analysis (detect anxiety or dissatisfaction), categorize content (symptoms, treatment plan, adverse effects), and enrich the data. This allows for proactive identification of treatment adherence problems, for example, or potential adverse events not formally reported—key points for pharmacovigilance.
• Improving the Quality of Care and Operations: By identifying patterns in thousands of interactions, it can reveal opportunities to improve communication protocols, optimize facility workflows, or measure the effectiveness of health information campaigns.
• Flexibility and Scalability: Designed for cloud environments, these platforms offer the ability to scale as needed, integrating in real time with existing systems and providing a high return on investment by improving efficiency and quality of service.
  
  

The Challenge of Accuracy: Measuring What Really Matters

The evaluation of voice AI systems begins with a set of known metrics, but their usefulness changes dramatically once they leave the laboratory. The ones we need to take into account are:

• Word Error Rate (WER): This is the classic metric that measures discrepancies within a human transcription. In the real world, WER can be misleading. In practice, a WER of between 18% and 25% can be perfectly usable if the goal is to extract key information.
• Diarization Error Rate (DER): This measures the system's ability to correctly attribute what each interlocutor (doctor, patient, companion) said, which is crucial for the consistency of the report.
• Task-Specific Metrics (F1 Score): Often, the real value lies in the ability to perform downstream tasks, such as identifying drugs, diagnoses, or allergies. Here, the focus shifts from perfect transcription to the reliable extraction of actionable information.
  
  

Challenges: The Hidden Costs of Deployment

A system that works well in a pilot may fail at scale. This raises challenges that traditional metrics fail to capture. The accuracy of speech recognition in real-life clinical settings is one of the most important of these metrics, where elements such as equipment noise, interruptions, or poor acoustics can generate variability and inconsistency in the audio. It is equally important to pay attention to terminology blindness and hallucinations. General models struggle with specific medical terminology and drug names, as well as the generation of plausible but incorrect information, which is crucial to detect. Finally, it is worth noting that it is not feasible to manually transcribe thousands of hours of audio, so proxy metrics must be developed to detect when model performance is degrading.

The use of AI in medical consultations also requires attention from an ethical and legal standpoint. Informed patient consent, the protection of personal data, and transparency in operation of the systems are conditions of utmost importance in such a highly regulated field.

Operational Readiness Is the True Indicator of Success

When accuracy is "good enough," success is defined by efficiency and economic viability. The conversation then shifts to other elements related to the solution's operability and cost-effectiveness. These include performance and latency: how many hours of audio can be processed and in what time?; cost per minute processed, the metric where engineering meets business reality; and robustness, being able to guarantee controlled degradation and rapid recovery from inevitable failures.

Toward a Multidimensional Assessment

The successful implementation of voice AI in healthcare is not the result of optimizing a single metric. It is a balancing act between accuracy and robustness, scalability and cost, and automation and human oversight. For organizations in the pharmaceutical and healthcare sectors, understanding that value lies not only in transcription, but also in the intelligence platforms that surround it—as demonstrated by the approach of solutions like YOURVOICE—is essential to moving from a technological promise to a real, sustainable, and, above all, secure healthcare transformation.

Original article published in Spanish on PMFarma, here.