News with ScienceSoft’s Expert Analysis

ScienceSoft launches a new series of articles on the practical applications of AI in healthcare.

Our experts explore the latest healthcare AI news through the lens of practical implementation and regulatory compliance. Each analysis is based on publicly available information, industry best practices, and ScienceSoft’s hands-on experience in healthcare IT.

Mount Sinai Launches AI Voice Assistant for Pre-Procedure Calls. Smart Use Case, but Technical Transparency Lags

Published: July 24, 2025

Mount Sinai Hospital in New York has introduced Sofiya, an AI-powered voice assistant that contacts patients before cardiac catheterization procedures. According to an article published in Becker’s Hospital Review on Tuesday, July 8, 2025, Sofiya provides pre-procedure instructions, answers logistical questions, and handles complex, multi-turn conversations. It was developed through close collaboration between a team of healthcare professionals and computer engineers, and is said to have saved over 200 nursing hours in five months. More than 800 calls were reviewed during the initial rollout, with reported patient satisfaction above 95%.

High-impact, yet low-risk initiative

Pre-procedure coordination is a mission-critical yet non-diagnostic area, where risks are lower and immediate value can be delivered. It addresses a clear operational pain point and achieves measurable results, which few AI pilots manage to do.

Critical implementation details are missing

While Mount Sinai has not disclosed the underlying technology behind Sofiya, the following analysis reflects ScienceSoft’s informed speculation based on industry standards and the capabilities described.

Technology stack

Mount Sinai’s AI assistant Sofiya likely runs on a fine-tuned LLM or a carefully prompt-engineered commercial model like GPT used in a HIPAA-compliant way, e.g., via Azure OpenAI Service. To achieve domain fluency, the team probably used a mix of de-identified nurse–patient call transcripts, structured procedural scripts, and synthetic dialogues, which is a common practice among AI teams prioritizing PHI safety.

The assistant’s calm, natural voice suggests the use of a neural text-to-speech (TTS) system. Microsoft’s Azure AI Speech–Voice Live API, which enables low-latency audio generation using Custom Neural Voice technology, is a likely candidate. This level of personalization is key for trust in patient-facing interactions.

AI performance monitoring

The manual review of 800 calls is a solid start, but there’s no mention of ongoing monitoring mechanisms for AI performance. The system may use tools like Azure Monitor, Azure Application Insights, or Amazon CloudWatch to monitor performance metrics and send automated notifications to the security team in case of suspicious changes.

Regulatory compliance

Although there is no explicit mention of HIPAA compliance, the system likely operates in an audited, encrypted, and access-controlled environment (e.g., Azure Virtual Network or Amazon Virtual Private Cloud). It may also rely on Microsoft’s Azure AI Content Safety or Amazon Bedrock Guardrails to track and filter all interactions between the assistant and patients in real time, helping prevent unauthorized disclosures and detect threats like prompt injection attempts.

For further analysis, explore another example of a high-impact yet low-risk AI application in healthcare.