バーチャルケアにおけるAIの性能は、認識する映像の質に左右される

AI is becoming part of how hospitals monitor and treat patients. It now supports patient intake, clinical documentation, observation and virtual visits. But AI in virtual care has a dependency that's easy to overlook: many of these tools rely on reliable, real-time audio and video. If the video is unreliable or poorly governed, the AI built on top of it will be too.

Adoption is accelerating

Healthcare organizations are moving quickly. In Proofpoint and the Ponemon Institute's 2026 report, 45% of organizations had already embedded AI in security or patient care, and another 30% planned to (Proofpoint).

The early results are encouraging. At one major health system, 77% of patients who used an AI intake agent saw a physician the same day (OpenLoop). And on high-risk units, virtual monitoring has reduced falls by an average of 20% within a year (ブラック・ブック). AI in virtual care is moving from pilot to practice.

Where AI in virtual care falls short

AI can only analyze what the video captures. When a ward's Wi-Fi weakens or a camera feed degrades, an AI observation tool can lose sight of the patient it's meant to protect. Gaps in connectivity become gaps in monitoring.

There's also a governance risk. Every AI tool that processes video also processes patient data. In the same Proofpoint research, 24% of organizations cited AI agents operating without guardrails as a leading cause of data loss. Without clear boundaries, AI in virtual care can expose the very data it's meant to use responsibly.

Five steps before deploying AI in virtual care

1. Validate performance where conditions are hardest. Test video on the units with the weakest connectivity, not the strongest. If it performs reliably there, it will give your AI a dependable feed everywhere else.

2. Define clear data boundaries. Know where each video stream is processed, who can access it, and how long it's retained. Make these rules part of every AI vendor evaluation.

3. Keep clinicians in the decision loop. AI should identify the risk; clinicians should decide the response. Design alerts so they reach the right person, who confirms before acting.

4. Choose an open video foundation. AI tools are changing fast. An open video layer with APIs and SDKs lets you add or replace AI tools without rebuilding your video infrastructure each time.

5. Start with one use case, and measure it. Pick a single, measurable goal, such as reducing falls on one unit. Establish a baseline first, for example falls per 1,000 patient days, then compare after deployment.

How Vidyo supports AI-ready video

Vidyo doesn't provide the AI. It provides the secure, reliable video foundation that AI in virtual care depends on:

• Consistent video quality. Scalable Video Codec adapts to each connection in real time, delivering up to 4K resolution when bandwidth allows.
• Control over your data. Deploy on-premise, in a private cloud or as a hybrid, with SRTP and TLS encryption. Vidyo is HIPAA and GDPR compliant.
• Built for integration. APIs and SDKs for web, mobile and native apps let teams build video into their own applications, with customizable layouts and Epic integration.
• Works with existing infrastructure. Vidyo supports the PTZ cameras, carts and displays hospitals already own, and Mobile Kiosk turns a standard iPad into a secure care endpoint.

Building AI in virtual care on a solid foundation

Reliable video and well-governed data give AI a stronger foundation, and give clinicians better information to act on. Before your next AI in virtual care project, check that the video underneath it is ready.

Enghouse Videoでは、コミュニケーションは、それを活用する医療チームと同じくらい自然に機能すべきだと考えています。当社のソリューションは、安全な遠隔医療、臨床連携、エンタープライズ向けビデオ管理、そして医療環境全体にわたる連携コミュニケーションを支援することで、組織が医療技術への投資を最大限に活用できるようサポートします。 病院が近代化に向けた実践的な方法を模索し続ける中、最大の成功を収める組織は、必ずしも最大の投資を行っている組織とは限りません。すでに実施した医療技術への投資を最も賢く活用している組織こそが、成功を収めることになるでしょう。.