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Ainos Launches Smell AI Study at National Taiwan University Hospital for ER Overcrowding and Respiratory Infection Risk Analysis

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Ainos (NASDAQ:AIMD) launched a Smell AI study at National Taiwan University Hospital’s emergency department starting June 1, 2026. AI Nose devices will monitor scent and environmental patterns to model ER overcrowding and respiratory infection risk, generating over 2,500 hours of data in about six months.

The IRB-approved, non-invasive study collects no images, audio, or personal data and deploys sensors across waiting, treatment, and observation areas to build AI-driven environmental risk maps for smart hospital and public-health applications.

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Positive

  • IRB-approved NTUH emergency department study launching June 1, 2026
  • More than 2,500 hours of emergency department environmental data planned
  • Expansion of AI Nose from industrial into healthcare Smell AI applications
  • Non-invasive design without images, audio, or personally identifiable data
  • Potential use across smart hospitals, public health systems, and smart buildings

Negative

  • None.

News Market Reaction – AIMD

-2.10%
5 alerts
-2.10% Session close to close
+14.9% Peak Tracked
-2.7% Trough Tracked
$20.29M Market Cap
0.1x Rel. Volume

In the Jun 1 session, AIMD declined 2.10%, reflecting a moderate negative market reaction. Argus tracked a peak move of +14.9% during that session. Argus tracked a trough of -2.7% from its starting point during tracking. Our momentum scanner triggered 5 alerts that day, indicating moderate trading interest and price volatility.

Data tracked by StockTitan Argus on the day of publication.

Market Context

This announcement extends Ainos’ Smell AI platform into a real-world emergency department at NTUH, t...
Analysis

This announcement extends Ainos’ Smell AI platform into a real-world emergency department at NTUH, targeting ER overcrowding alerts and respiratory infection risk analysis. The study plans to collect over 2,500 hours of environmental data across multiple care zones over roughly six months, without gathering personal identifiers. It follows earlier hospital and semiconductor deployments, reinforcing a multi-vertical strategy. Investors may watch for future updates on model performance, hospital adoption, and evidence of scalable healthcare use cases.

Key Figures

Study start date: June 1, 2026 Study duration: approximately six months Environmental data: more than 2,500 hours +1 more
4 metrics
Study start date June 1, 2026 NTUH emergency department Smell AI study initiation
Study duration approximately six months Planned length of NTUH environmental monitoring study
Environmental data more than 2,500 hours Emergency department environmental data to be generated
Monitoring zones waiting, treatment, observation Areas within NTUH emergency department receiving AI Nose systems

Previous AI Reports

5 past events · Latest: May 20 (Positive)
Same Type Pattern 5 events
Date Event Sentiment 24h Move Catalyst
May 20 AI Nose commercialization Positive -3.3% VASRO research update on AI Nose commercialization and semiconductor deployments.
May 18 Research coverage Positive -2.4% Zacks Small-Cap Research note on Smell AI commercialization strategy.
May 11 SIC code change Positive +2.4% Updated SIC code to reflect AI-powered sensing and Smell AI focus.
Apr 17 Healthcare expansion Positive +18.7% Announced Smell AI expansion into healthcare infrastructure with new collaborators.
Apr 08 Hospital partnership Positive +11.5% Partnership to deploy AI Nose in high-risk hospital environments starting April 2026.

24h Move is the share-price change in the day after each event; other market factors may also have contributed.

Pattern Detected

AI-focused announcements often read positively but have produced mixed single-day reactions, with several strong gains and a couple of notable selloffs.

Recent Company History

Over the last few months, Ainos has consistently emphasized Smell AI and AI Nose commercialization across semiconductors and healthcare. AI-tagged news included SIC reclassification to computer peripherals, expansion into hospital environments with MacKay Memorial and Topco, and research coverage of semiconductor deployments tied to about 1,400 backend systems and 600 partner systems. Market reactions ranged from double-digit gains (up to 18.67%) to modest declines around research updates, underscoring uneven but often meaningful responses to AI-related milestones.

Key Terms

volatile organic compound, voc, ai electronic nose, institutional review board, +1 more
5 terms
volatile organic compound technical
"move beyond traditional volatile organic compound ("VOC") monitoring and build AI-powered"
Volatile organic compounds are carbon-based chemicals that evaporate easily into the air from products and processes such as paints, solvents, fuels, and manufacturing operations. They matter to investors because VOC emissions can lead to regulatory limits, cleanup expenses, product restrictions, fines, and reputational harm—like an unseen leak that forces a plant to pause—impacting costs, operations and company value.
voc technical
"move beyond traditional volatile organic compound ("VOC") monitoring and build AI-powered"
Volatile organic compounds (VOCs) are carbon-based chemicals that evaporate easily at room temperature—think of the smell from paint, solvents, or gasoline. They matter to investors because limits on VOC emissions, workplace air-quality rules, product-safety concerns or cleanup liabilities can create costs, slow production, or harm reputation; in effect they are like a hidden maintenance problem that can reduce future profits and increase regulatory risk.
ai electronic nose technical
"by Application of AI Electronic Nose for Intelligent Environmental Monitoring"
An AI electronic nose is a device that uses sensor arrays to 'smell' chemical signatures and machine learning to interpret those patterns, turning complex odor or vapor data into clear signals about what is present. For investors it matters because this technology can enable faster, cheaper, noninvasive medical diagnostics, quality control in manufacturing, food safety monitoring, and environmental sensing—areas where reliable, automated detection can create new revenue streams, cut costs, and face regulatory review or approval.
institutional review board regulatory
"NTUH's Institutional Review Board ("IRB") approved the study."
An institutional review board is an independent committee that reviews and approves research involving people to make sure studies are safe, ethical, and protect participants’ rights and privacy. For investors, IRB approval is a gatekeeper: it can determine whether a clinical trial can start or continue, affecting timelines, regulatory risk, cost and the credibility of trial results—similar to a safety inspector whose sign-off is required before work can proceed.
irb regulatory
"NTUH's Institutional Review Board ("IRB") approved the study."
An Institutional Review Board (IRB) is an independent committee that reviews and approves medical and behavioral research involving people to ensure safety, informed consent, and ethical treatment—think of it as a safety inspector for studies. For investors, IRB decisions matter because their approval or requests for changes can speed up, delay, or halt clinical trials and other studies, directly affecting timelines, costs, and the value of companies developing treatments.

AI-generated analysis. How Rhea-AI works. Not financial advice.

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AI Nose Expands from Semiconductor Facilities into Frontline Healthcare Infrastructure

HOUSTON, TX / ACCESS Newswire / June 1, 2026 / Ainos, Inc. (NASDAQ:AIMD)(NASDAQ:AIMDW) ("Ainos" or the "Company"), a Smell AI company digitizing scent into machine-readable data for artificial intelligence applications, today announced the launch of a research program at National Taiwan University Hospital ("NTUH"), one of Asia's leading academic medical centers, to deploy AI Nose inside a live emergency department environment for intelligent environmental monitoring, ER overcrowding early warning, and respiratory infection risk analysis.

The program aims to expand Smell AI, powered by AI Nose, inside a real-world emergency department environment and expands market opportunities beyond semiconductor and industrial environments into frontline healthcare infrastructure.

The study, titled "Establishment of an Early Warning System for Emergency Department Overcrowding and Respiratory Infection Risk by Application of AI Electronic Nose for Intelligent Environmental Monitoring," will deploy AI Nose systems across waiting areas, treatment areas, and observation zones inside the NTUH emergency department.

The systems will continuously analyze environmental scent patterns and broader environmental signals associated with crowd density, patient flow, waiting conditions, and respiratory infection-related environmental changes.

Ainos designed the project to move beyond traditional volatile organic compound ("VOC") monitoring and build AI-powered environmental intelligence infrastructure for complex healthcare environments.

Ainos believes that emergency departments represent some of the most operationally challenging environments in healthcare systems worldwide. Constant movement of patients, visitors, and medical staff continuously changes airflow, occupancy, and environmental conditions, while overcrowding and respiratory infection risks remain major operational and public health challenges.

Using AI Nose, Ainos converts complex environmental scent and air-pattern signals into machine-readable digital data called "Smell ID." The Company will use that data to develop healthcare-specific environmental mapping and AI-driven risk models.

The study will generate more than 2,500 hours of emergency department environmental data and support development of AI models designed to analyze:

  • Emergency department overcrowding conditions

  • Relationships between crowd density and environmental changes

  • Respiratory infection-related environmental signals

  • High-risk crowding pattern alerts

  • Potential scalability across broader hospital environments

"This project represents an important step toward AI-powered environmental intelligence infrastructure," said Eddy Tsai, Chairman, President, and Chief Executive Officer of Ainos.

"Our deployments in semiconductor facilities have taught AI Nose how to recognize abnormal environmental changes inside highly sensitive, high-risk environments. Emergency departments represent another highly complex real-world operating environment."

"The challenge is not detecting a single environmental signal. The challenge is understanding how risk patterns evolve across an entire environment in real time."

"We believe future hospitals will not only see and hear operational risks, but also gain the ability to detect environmental changes associated with those risks through Smell AI."

Ainos recently expanded commercialization efforts for AI Nose across smart manufacturing environments, such as advanced semiconductor manufacturing. Through those deployments, the Company has accumulated operational experience in real-time monitoring, anomaly recognition, and environmental analysis in complex industrial settings.

Ainos believes semiconductor fabs and emergency departments share important characteristics as high-density, high-sensitivity environments that require continuous monitoring and rapid situational awareness.

This program marks a strategic expansion of AI Nose from Industrial Smell AI into Healthcare Smell AI and broader public-environment risk monitoring applications.

Ainos believes Smell AI, powered by AI Nose, could become an important enabling capability across smart hospitals, public health systems, smart buildings, and future intelligent city infrastructure.

The study will not involve individual patient diagnosis or collect images, voice recordings, or personally identifiable information. Researchers designed the project as a non-invasive environmental AI monitoring study focused on environmental-level pattern analysis.

NTUH's Institutional Review Board ("IRB") approved the study. The study is scheduled to begin on June 1, 2026, and is expected to continue for approximately six months.

About AI Nose

AI Nose digitizes scent into Smell ID, an AI-driven form of scent intelligence. The full-stack electronic nose platform integrates high-precision MEMS sensor arrays with proprietary AI algorithms designed to support ppb-level scent detection sensitivity, subject to application conditions and deployment configurations. Smell ID converts analog scent signals into structured, actionable data, while the proprietary Smell Language Model (SLM) is designed to learn, classify, and contextualize complex scent patterns over time.

Built upon more than a decade of accumulated scent data and deep medtech expertise, AI Nose is designed to support continuous monitoring, predictive analysis, and real-time alerts across industrial and manufacturing environments. AI Nose is offered under a SmellTech-as-a-Service architecture, intended to support ongoing access to scent intelligence, analytics, and AI-driven insights through subscription-based deployment models.

About Ainos, Inc.

Ainos, Inc. (NASDAQ:AIMD) is a dual-platform AI and biotech company pioneering smelltech and immune therapeutics. Its AI Nose platform and smell language model (SLM) digitize scent into Smell ID, a machine-readable data format, powering intelligent sensing across robotics, smart factories, and healthcare. The company also develops VELDONA®, a low-dose oral interferon targeting rare, autoimmune, and infectious diseases. Ainos, a fusion of "AI" and "Nose," is redefining machine perception for the sensory age. To learn more, visit https://www.ainos.com. Follow Ainos on X, formerly known as Twitter, (@AinosInc) and LinkedIn to stay up-to-date. Visit media room https://ainos.suite.accessnewswire.com.

Forward-Looking Statements

Certain statements in this press release are forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. All statements other than statements of historical fact are forward-looking statements. Forward-looking statements are based on management's current assumptions and expectations of future events and trends, which affect or may affect the Company's business, strategy, operations or financial performance, and actual results and other events may differ materially from those expressed or implied in such statements due to numerous risks and uncertainties. Forward-looking statements are inherently subject to risks and uncertainties, some of which cannot be predicted or quantified. There are a number of important factors that could cause actual results, developments, business decisions or other events to differ materially from those contemplated by the forward-looking statements in this press release. These factors include, among other things, our expectation that we will incur net losses for the foreseeable future; our ability to become profitable; our ability to raise additional capital to continue our product development; our ability to accurately predict our future operating results; our ability to advance our current or future product candidates through clinical trials, obtain marketing approval and ultimately commercialize any product candidates we develop; the ability to obtain and maintain regulatory approval of our product candidates; delays in completing the development and commercialization of our current and future product candidates; developing and commercializing additional products, including diagnostic testing devices; our ability to compete in the marketplace; compliance with applicable laws, regulations and tariffs, and factors described in the Risk Factors section of our public filings with the Securities and Exchange Commission (SEC). Because forward-looking statements are inherently subject to risks and uncertainties, you should not rely on these forward-looking statements as predictions of future events. These forward-looking statements speak only as of the date of this press release and, except to the extent required by applicable law, the Company undertakes no obligation to update or revise these statements, whether as a result of any new information, future events and developments or otherwise.

Contact Information
Investor Relations
ir@ainos.com

SOURCE: Ainos, Inc.



View the original press release on ACCESS Newswire

FAQ

What is the Ainos (NASDAQ:AIMD) Smell AI study at National Taiwan University Hospital?

The study deploys Ainos’ AI Nose in NTUH’s emergency department to analyze environmental scents and signals for overcrowding and respiratory infection risk. According to Ainos, sensors in waiting, treatment, and observation areas will create AI models for environmental-level risk mapping in healthcare settings.

When does the Ainos Smell AI study at NTUH start and how long will it run?

The Ainos Smell AI study at NTUH’s emergency department starts on June 1, 2026 and is expected to last about six months. According to Ainos, this period should generate over 2,500 hours of environmental data to train AI-based risk models.

How will Ainos’ AI Nose help address ER overcrowding and respiratory infection risk?

AI Nose converts scent and air-pattern signals into digital Smell IDs to model crowding and respiratory risk conditions. According to Ainos, AI will analyze crowd density, environmental changes, and high-risk patterns to support early warnings for emergency department operations and infection-related risks.

Does the Ainos (AIMD) Smell AI study at NTUH collect patient personal data?

The study does not collect individual patient diagnoses, images, voice recordings, or personally identifiable information. According to Ainos, it is designed as a non-invasive environmental monitoring project focused solely on aggregated environmental patterns, reducing privacy concerns while enabling AI-driven operational risk analysis.

Why is Ainos expanding AI Nose from semiconductor fabs into healthcare environments?

Ainos is extending AI Nose from semiconductor facilities into emergency departments to apply its environmental monitoring expertise in healthcare. According to Ainos, both fabs and ERs are high-density, high-sensitivity environments needing continuous monitoring, making Smell AI relevant for smart hospitals and broader public health systems.

What future applications could Ainos’ Smell AI have beyond the NTUH ER study?

Ainos sees potential Smell AI use across smart hospitals, public health systems, smart buildings, and intelligent city infrastructure. According to Ainos, AI Nose-based environmental intelligence could support broader public-environment risk monitoring, extending beyond current industrial and emergency department deployments if the models prove scalable.