At your next appointment, your doctor might not be the only one listening to you.
Combining advanced AI and the Internet of Things, ambient artificial intelligence creates proactive environments that are sensitive to the presence of people and can adapt to them seamlessly.
Basically, it’s a healthcare assistant that’s eyes and ears 24/7, seamlessly keeping tabs on patients, their speech, behavior, movement patterns, and other cues.
How is ambient AI used in healthcare today?
Ambient AI is not a new kid on the block, but with the advent of large language models (LLMs), sophisticated data analytics, and advanced sensors, it's finally having its BIG moment across the healthcare board.
Ambient clinical intelligence
With an average caseload of 20 to 30 patients, primary care providers are scrambling to find enough face-to-face time. Addressing the challenge of excessive documentation requirements, ambient clinical documentation tools automate clinical notetaking, jotting down patient encounters during a natural conversation between healthcare providers and patients.
- Integrating clinical data with medically tuned LLMs, the ambient AI scribe Suki AI reported reducing notetaking time by 41%. Zoom Workplace for Clinicians has placed its bets on Suki AI as its ambient scribe tool partner.
- Joined by Suki AI, Abridge AI scribe became a part of athenahealth’s AI-powered Ambient Notes, a product skewed towards community practices and hospitals. Abridge is now valued at $2.75 billion post-money.
- Going beyond clinical scribing, Commure aids healthcare professionals in coding and other ED, hospital, and ambulatory workflows. Seeking to become the leading AI suite for healthcare, Commure received funding from HCA Healthcare, General Catalyst, and Sequoia Capital.
Fall detection and prevention
According to the CDC, around 3 million ER visits are attributed to older people's falls each year. With ambient assisted living systems, falls don’t stand a chance.
Working in tandem with wearable or wall-mounted devices, ambient fall detection systems analyze movement patterns, physiological signals, and environmental data to predict and prevent falls inside and outside hospital settings. And if an incident occurs, such systems instantly alert caregivers.
- Proven to reduce fall-related visits by 80%, SafelyYou acts as a remote sitter for older patients, detecting falls in real time with over 99% accuracy. In 2025, this ambient assisted living system landed $43 million in Series C funding.
- Other solutions, like Sensi AI, boast a more comprehensive approach to fall detection, notifying caregivers not only of falls but also of good-to-know issues like unusual activity levels or secondary health issues such as urinary tract infections. This holistic approach secured Sensi AI $31 million in Series B funding.
Home health management
In a home setting, ambient AI-enabled virtual nurse assistants can nudge patients to keep them on the ball with their meds and treatment. They can also facilitate remote patient monitoring for chronic patients, continuously monitoring vital signs in a wearable-free, non-obtrusive way.
- Billed as a full-home radar system, the Serenity system can monitor resting heart rate and respiration from a distance without any wearable device. In 2024, this AI-powered Environmental, Health, and Safety (EHS) software nabbed $5.5 million in series A funding.
Mental health management
A subtle shift in tone, a change in activity patterns, and other seemingly minor cues can signal a brewing mental health crisis. With advanced behavioral signal processing at the core, mental health ambient AI can pick up those slight variations and analyze them in the background to support self-management and remote mental health care programs.
- Combining multimodal AI and behavioral signal processing, Deliberate AI allows mental health professionals to map individual symptom trajectories and prodromal signals from afar. The company’s AI-driven mental health assessment was selected for the FDA's ISTAND Pilot Program.
The bright side of ambient intelligence in healthcare
In 2023, the global ambient intelligence market size was valued at $23.59 billion and is projected to surpass $172 billion by 2032, with healthcare leading the market. Given the unique benefits that ambient AI in healthcare brings to the table, this trajectory is unsurprising.
The cure to clinician burnout
Each year, clinician burnout costs health systems an estimated $4.6 billion. By taking over the heavy lifting of bureaucratic tasks, AI scribes and other clinical intelligence tools enable clinicians to treat the patient, not the data.
According to Microsoft, ambient intelligence is associated with a 70% improvement in work-life balance for clinicians and an 80% reduction in cognitive burden.
Improved productivity
Every minute counts when you’re knee-deep in the admin tedium and on a packed schedule. Ambient intelligence allows clinicians to reclaim those lost minutes, saving an average of 5 minutes per encounter. Also, by delivering more from patient conversations, ambient intelligence supports faster diagnoses and proactive health interventions.
Improved patient care outcomes
Because of documentation, clinicians often find their eyes flickering between screens and patients during the encounter. This might detract from face-to-face interaction, making patients feel unseen. Recording in the background, ambient AI systems help clinicians focus on patients, capture a complete patient story, and make a more accurate diagnosis that is not based on post-visit memory only.
Thanks to Microsoft Dragon Copilot, 93% of patients noticed their physician becoming more personable and conversational.
Unobtrusive, dependable monitoring
Traditional wearables might be heavy, bulky, and constantly begging for charging. Contactless and embedded right into the physical spaces, ambient AI allows people to go about their lives as usual without the burden of wearables or the feeling of being watched.
The flip side of healthcare ambient AI
Whether the technology will live up to its potential or not hinges on how fast healthcare and health tech stakeholders can mitigate the inherent risks present in ambient intelligence.
Data privacy and security
Extensive data collection enabled by ambient AI inevitably translates into security concerns that include unauthorized access to nodes, eroding privacy rights, digital leakage, and other hot-button issues. Without robust data encryption, secure hardware/software design, and other precautions, ambient AI might become a liability rather than an asset.
Fairness and bias
Like all AI offshoots, ambient AI is vulnerable to the “garbage in, garbage out” effect that stems from skewed input data and the bias in machine learning algorithms. Cognitive and systemic biases that can seep into ambient AI development can also impact the tool’s ability to deliver equitable support to all sides of care.
That’s why the ambient AI governance framework should reinforce the necessary guardrails to ensure ambient AI is constructed in a way that benefits all members of society.
Human-centered approach
Maximizing ambient AI’s promise of equitable care requires an inclusive approach that puts individuals first, especially those in older populations and people with accessibility needs. Involving all target user groups in interface design, performing iterative prototyping, and incorporating explicit feedback mechanisms into the system will help organizations bring ethically sound ambient AI applications on board.
Transparency and explainability
The absence of clarity around the dataset composition, process of annotation, and decision-making processes leads to more “AI-nxiety” and deters the adoption of ambient AI applications. To offset the black box nature of AI, healthcare organizations should abide by Explainable AI (XAI), whose principles and techniques are dedicated to making AI transparent and understandable to humans.
Lack of interoperability
Body Area Networks (= embedded sensors) operate by Bluetooth Low Energy (BLE), Zigbee, IEEE 802.15.6, and other standards and communication protocols. These protocols and standards run counter to the common data exchange standards of legacy healthcare systems, which include HL7, DICOM, IHE, and others.
Unless designed to bridge that gap, ambient AI applications will have a hard time integrating into clinical workflows.
Where ambient AI goes next in healthcare
Right now, the healthcare industry is watching the nascent stages of ambient AI technology. Currently confined to a few select areas, ambient intelligence also holds great potential in:
- Intensive care units — ambient AI can reduce alarm fatigue in healthcare personnel, improve infection control measures, keep an eye on environmental factors, and monitor the patient’s health status and safety.
- Surgeries — ambient sensors placed within operating rooms can check adherence to protocols, assess the surgical skills of healthcare professionals, and deliver insight into the technical efficiency of surgeons.
- Persuasive well-being — ambient intelligence systems can become a crucial component of a persuasive tech stack thanks to their personalized, context-aware nudges that drift individuals towards healthier behaviors.
On the business side of things, three key trends are emerging in the ambient AI sector:
- Market growth and adoption of ambient AI systems are prompted by strategic partnerships between established healthcare providers and innovative technology companies.
- Health tech heavyweights such as Microsoft, Zoom, Google, and others are quick to jump on the ambient AI bandwagon by developing from scratch or coveting existing ambient AI capabilities.
- Startups bank on integrations to outdo larger players.
The outro
Can ambient AI deliver on its promise in healthcare? The reality is — it already does. Over the next few years, we’ll likely watch it transform daily living spaces, clinical environments, and healthcare self-management. The vision is within reach, but it’s a team sport requiring healthcare players to commit to ethical development, responsible deployment, and an unwavering focus on the human element of care.
At Orangesoft, we specialize in developing secure and user-first ambient AI solutions designed for the healthcare industry. Whether you're looking to build, integrate, or refine an ambient AI component, our team is ready to bring your vision to life.