AI and IoT in Healthcare: How Are Connected Devices Changing Hospital Operations?

AI and IoT in healthcare are often discussed together, but the two technologies play different roles. IoT and IoMT enable devices to connect, collect, and transmit data, while AI can analyze that data to identify patterns, detect anomalies, forecast demand, or support action prioritization.

In hospitals, this combination may appear across many areas, including patient monitors, laboratory analyzers, diagnostic imaging equipment, infusion pumps, environmental sensors, asset tracking systems, rehabilitation equipment, and facility management platforms.

However, a network-connected device does not automatically become a “smart” system. If data are incorrectly identified, connections are frequently interrupted, alerts are not linked to an appropriate response workflow, or an AI model generates too many false alerts, the technology may increase workload rather than reduce it.

Within the digital health ecosystem, interoperability, data sharing, and data governance are important conditions for AI and connected systems to create practical value rather than exist as isolated solutions.

Quick Summary

- IoT enables devices and sensors to collect, transmit, and exchange data; AI helps analyze data to support detection, forecasting, or optimization.
- IoMT generally refers to the ecosystem of connected medical devices and related technologies used in healthcare environments.
- An AI + IoT system typically includes devices, a network or gateway, an integration platform, data, an analytics layer, and an action workflow.
- Applications may include equipment monitoring, alert prioritization, asset management, predictive maintenance, environmental monitoring, and operational analytics.
- Not every use case requires AI; in many situations, simple rules or technical thresholds may be more appropriate.
- Data quality, identity management, timestamps, interoperability, and security directly affect the value of the system.
- AI should not automatically replace decisions made by healthcare professionals or engineers without appropriate controls.
- Projects should begin with a specific operational problem, be piloted on a limited scope, and have their effectiveness measured before expansion.

How Are AI, IoT, and IoMT Different?

These three terms are often used together but are not interchangeable.

Concept	Primary Role	Examples in Hospitals
IoT	Connects devices, sensors, and systems so they can exchange data	Temperature sensors, asset tracking, environmental monitoring
IoMT	An ecosystem of connected medical devices and related technologies	Patient monitors, laboratory analyzers, infusion pumps, connected imaging equipment
AI	Analyzes data to identify patterns, forecast outcomes, or support decisions	Anomaly detection, failure prediction, utilization analysis

Not every IoT device used in a hospital is a medical device.

For example:

- Server room temperature sensors.
- Door sensors.
- Energy meters.
- HVAC control systems.

These may be part of a hospital's IoT environment but are not necessarily managed as medical devices.

Conversely, connected patient monitors, ventilators, or laboratory analyzers may become part of the IoMT ecosystem when their data are exchanged with other systems.

The FDA notes that medical devices are increasingly connected to the Internet, hospital networks, and other devices for data exchange. These capabilities may support healthcare operations but also increase cybersecurity risks that need to be managed throughout the device lifecycle.

Why Are AI and IoT Often Implemented Together?

IoT generates data.

AI can help find meaning in that data.

A simple way to visualize the process is:

Device → data → connectivity → platform → AI/analytics → alert → human action

With IoT alone, a hospital may collect large volumes of data while staff still need to determine manually which information requires attention.

With AI alone but without sufficiently stable data sources, the model may lack appropriate inputs for analysis.

Value emerges only when both elements are properly connected to a real-world action workflow.

For example, a temperature sensor may transmit data periodically.

The system may:

1. Record the temperature.
2. Compare it with operating rules.
3. Detect an abnormal upward trend.
4. Combine it with historical data where necessary.
5. Determine whether inspection is required.
6. Send an alert to the appropriate person.
7. Create a work order where appropriate.
8. Record the response outcome.

If an alert appears only on a dashboard without a responsible person or response process, the system remains operationally incomplete.

Which Layers Are Typically Involved in an AI + IoT System in a Hospital?

The data flow can be understood through six steps.

1. Devices and Sensors Generate Data

Data sources may include:

- Patient monitors.
- Ventilators.
- Laboratory analyzers.
- Diagnostic imaging equipment.
- Infusion pumps.
- Temperature sensors.
- Humidity sensors.
- Location-tracking devices.
- Building management systems.
- Logs from software or devices.

Each source may produce data with different structures and frequencies.

A gateway typically receives, aggregates, transforms, or transmits data between devices and upstream systems. In some architectures, a gateway may also generate logs or metadata for monitoring and operations, but it should not automatically be treated as a primary measurement source in the same way as a sensor or medical device.

2. Data Are Transmitted Through a Network or Gateway

Devices may use:

- Wired networks.
- Wi-Fi.
- Gateways.
- Manufacturer platforms.
- Internal connections.
- Cloud connectivity where appropriate.

At this stage, the hospital should control:

- Device identity.
- Authentication.
- Connection stability.
- Encryption where appropriate.
- Credential management.
- Procedures for network loss.

NIST emphasizes that when organizations acquire and deploy IoT products, they should consider how devices will integrate into the system and which cybersecurity requirements the device and vendor need to meet.

3. Data Are Standardized and Given Context

A single value may have limited meaning on its own.

The system may need to know:

- Which device generated the data?
- Which model?
- Where is the device located?
- Which department does it belong to?
- Is it associated with a patient?
- When was the data recorded?
- What is the unit of measurement?
- What is the device status?

If a patient monitor is moved from one bed to another but the system does not update the association, the data may be technically accurate but linked to the wrong context.

4. Data Are Stored and Integrated

Depending on the purpose, data may be transferred to:

- HIS/EMR.
- LIS.
- RIS/PACS.
- CMMS.
- IoT platforms.
- Data warehouses.
- Asset management systems.
- Building management systems.

Not all data need to be sent to the same system.

Clinical data, technical data, and environmental data may serve different purposes.

5. AI or Analytics Tools Process the Data

Methods may include:

- Rules.
- Thresholds.
- Trend analysis.
- Anomaly detection.
- Classification.
- Time-series forecasting.
- Risk scoring.
- Optimization.
- Machine learning where appropriate.

An important point is that not every problem requires AI.

Where a technical limit is clearly defined, a rule-based alert may be easier to control and explain than a complex model.

6. Results Are Converted into Actions

Outputs may lead to:

- Alerts.
- Work orders.
- Priority adjustments.
- Inspection requests.
- Equipment reassignment.
- Environmental checks.
- Spare part preparation.
- Vendor contact.

This is the step that determines whether the technology creates real operational value.

1. Monitoring Equipment and Operating Status

One of the most direct applications of IoT is helping hospitals understand equipment status.

The system may record:

- Whether the device is powered on or off.
- Whether it is active or idle.
- Whether it is connected to the network.
- Battery level.
- Operating hours.
- Alert status.
- Software version.
- Current location where an appropriate location system is available.

This information may be particularly useful for equipment shared among multiple departments.

For example, instead of calling several departments to locate a patient monitor or infusion pump, the operations team may use location and status data to determine:

- Where is the device?
- Is it currently in use?
- Is it waiting for cleaning?
- Is it undergoing maintenance?
- Is it available for allocation?

AI may add value by analyzing historical patterns to identify periods when equipment is frequently in short supply or to forecast demand by location.

However, location data should not be assumed to be completely accurate until the system has been tested under actual operating conditions.

2. Supporting Alert Management

Connected devices can generate large numbers of alerts.

If every alert appears on a dashboard with the same priority, staff may experience alert overload.

AI and data analytics may help:

- Group related alerts.
- Remove duplicate events.
- Identify recurring alerts.
- Rank alerts according to predefined priorities.
- Detect abnormal changes compared with baseline conditions.
- Route alerts to the appropriate response team.

For example, a device losing connectivity for a few seconds may be different from a device repeatedly disconnecting throughout a shift.

The important point is that AI should only support classification and prioritization where appropriate. It should not automatically alter the safety level or clinical priority of an alert unless an appropriate mechanism has been assessed and approved.

3. Analyzing Equipment Utilization

A hospital may experience two situations at the same time:

- Department A lacks equipment.
- Department B has similar equipment that is rarely used.

IoT data and asset management systems can help monitor:

- Utilization time.
- Idle time.
- Movement frequency.
- Number of cycles.
- Peak periods.
- Demand by department.
- Equipment frequently retained in one area despite limited use.

AI can analyze these patterns to support:

- Equipment allocation.
- Procurement planning.
- Rental decisions.
- Shared equipment pool design.
- Demand forecasting by time period.

However, low utilization does not automatically mean the equipment is unnecessary.

Some devices must remain available for emergencies or infrequent but high-priority situations.

4. Predictive Maintenance

This is one of the applications most frequently discussed in relation to AI + IoT.

Devices may provide:

- Logs.
- Temperature.
- Voltage.
- Number of cycles.
- Operating hours.
- Alert codes.
- Battery level.
- Component status.
- Restart history.

When combined with CMMS data such as:

- Repair history.
- Spare parts.
- Downtime.
- Model.
- Equipment age.
- Maintenance results.

the model may support identification of risk or deterioration trends.

For example, the system may identify that:

- A particular alert often appears before a specific failure.
- Batteries in a group of devices are deteriorating faster than expected.
- A particular model tends to fail after a certain number of cycles.
- A device is beginning to require more frequent restarts.

However, predictive maintenance does not replace maintenance, verification, or calibration activities that must be performed according to instructions, professional requirements, or applicable regulations.

Within an AI + IoT use case, predictive maintenance should be viewed as an application of connected data rather than a default capability of every smart device.

5. Monitoring Environmental and Technical Conditions

IoT in hospitals is not limited to medical devices.

Sensors may be used to monitor:

- Temperature.
- Humidity.
- Certain environmental conditions.
- Power status.
- Pressure in appropriate areas.
- HVAC operation.
- Storage conditions.
- Refrigerator or freezer conditions.

These data may support areas such as:

- Laboratories.
- Storage facilities.
- Server rooms.
- Certain areas requiring environmental control.
- Appropriate medical supply storage areas.

AI may support trend analysis.

For example, rather than waiting until temperature exceeds a threshold, the system may identify a gradual abnormal upward trend.

However, where technical or control limits are clearly defined, threshold-based alerts should still be maintained appropriately. AI should not replace a simple safety mechanism merely because a product offers advanced analytics.

6. Optimizing Laboratory Workflows

Laboratory analyzers and automation systems may generate data relating to:

- Sample volume.
- Processing time.
- Alerts.
- Reagent status.
- Equipment downtime.
- Number of reruns.
- Queues.

When data from multiple devices are consolidated, facilities can assess:

- Which equipment is overloaded.
- Which stage creates a bottleneck.
- When sample volumes are highest.
- Which analyzer has a high downtime rate.
- Whether samples should be considered for routing to another system.

AI may support volume forecasting or detection of abnormal operating patterns.

However, this use case relates to laboratory operations. It should not be used to imply that an operational analytics system can independently interpret test results or make diagnoses unless the product has been designed and evaluated for that purpose.

7. Analyzing Diagnostic Imaging System Operations

CT, MRI, X-ray, and other imaging systems may generate not only images but also technical operating data.

Operational data may help monitor:

- Number of cases.
- Utilization time.
- Equipment idle time.
- Start-up time.
- Errors.
- Downtime.
- Data volume sent to PACS.

When combined with RIS or scheduling data, facilities may analyze:

- Peak periods.
- Schedule delays.
- Equipment utilization.
- Bottlenecks between workflow stages.

AI may support demand forecasting or anomaly detection in operations, but this should not be treated as equivalent to AI that analyzes medical images for diagnostic support.

8. Facility and Energy Management

A hospital operates many technical systems beyond medical devices:

- HVAC.
- Electricity.
- Generators.
- UPS.
- Water systems.
- Elevators.
- Lighting.
- Server rooms.

IoT can provide status data.

AI or analytics tools may support:

- Detection of abnormal consumption.
- Demand forecasting.
- Detection of abnormal mechanical or electrical equipment behavior.
- Maintenance prioritization.
- Performance comparison across areas.

This demonstrates that “AI in healthcare” does not necessarily always relate directly to clinical decision-making.

Part of its value may lie in helping hospitals operate infrastructure more reliably and efficiently.

9. Remote Monitoring and Extending Care Beyond the Hospital

Connected devices may also transmit data from outside the hospital where the service model is appropriate.

These may include:

- Remote monitoring devices.
- Wearables.
- Certain connected treatment devices.
- Telehealth platforms.
- Devices used in home healthcare models.

This changes the boundaries of the system the hospital needs to manage.

The facility should clarify:

- Who is responsible for managing the device?
- How does external network infrastructure affect operation?
- Where are data transmitted?
- What happens if connectivity is interrupted?
- Who monitors alerts?
- How is the device updated?

Connected devices used outside the hospital may introduce additional cybersecurity, privacy, and operational challenges because part of the environment lies outside the facility's direct control.

What Does AI Actually Do with IoT Data?

Not every “smart” feature works in the same way.

They can be divided into five groups.

Anomaly Detection

The objective is to identify when a device or process differs from its normal state.

Examples include:

- Abnormal temperature increases.
- Increasing fault frequency.
- Frequent device disconnections.
- Rapid battery deterioration.

Classification

The system may help categorize:

- Fault types.
- Alert groups.
- Event types.
- Priority levels.

Forecasting

It may support forecasting of:

- Failure risk.
- Equipment demand.
- Workload.
- Spare part requirements.

Optimization

Examples include:

- Scheduling.
- Equipment allocation.
- Resource allocation.
- Selection of maintenance timing.

Processing Unstructured Data

AI may support searches across:

- Work orders.
- Technical notes.
- Logs.
- Documentation.
- Repair history.

However, each function should be evaluated separately. A general “AI” label should not be used to describe every form of analytics or automation.

When Is AI Not Necessary?

A common mistake is assuming that every IoT project needs machine learning.

In many cases, a simpler solution may be more appropriate.

Clear Thresholds

For example, a technical parameter has a defined limit.

→ A rule-based alert may be appropriate.

Clear Business Logic

For example, equipment is overdue for maintenance.

→ CMMS can generate an automatic alert.

Simple Status Conditions

For example, a device is disconnected.

→ A network monitoring tool may be sufficient.

AI may be more useful when:

- Multiple variables are involved.
- Relationships are difficult to describe using simple rules.
- Appropriate historical data are available.
- Pattern detection is required.
- There is a clearly defined forecasting objective.

A simple, controllable system that creates practical value may be more appropriate than a complex AI model that is difficult to explain.

Data Quality Determines AI Quality

An AI + IoT project may encounter problems at the data level before anything else.

Common issues include:

- Duplicate device IDs.
- Unsynchronized device clocks.
- Missing data.
- Data loss during network interruptions.
- Different units of measurement.
- Different models using different error codes.
- Work orders missing root causes.
- Equipment being transferred between departments without the system being updated.
- Post-maintenance data not being recorded.

For example, the same connectivity issue may be recorded as:

- Network offline.
- Communication error.
- Connection lost.
- Error 305.
- No connection.

Without standardization, an analytics system may treat these as different types of incidents.

Time Synchronization Is a Small but Important Detail

In a connected system, time is also data.

If:

- A monitor records 10:05.
- A gateway records 10:03.
- A server records 10:07.

it may become difficult to determine the correct sequence of events.

This can affect:

- Incident analysis.
- Data matching.
- Logs.
- Alerts.
- Audits.

Hospitals should have an appropriate mechanism for managing time synchronization across important systems.

Interoperability Matters More Than Simply Being “Connected”

A device having Ethernet or Wi-Fi does not demonstrate its actual integration capability.

The hospital should determine:

- Which systems can the device exchange data with?
- What data formats are used?
- Is the interface documented?
- Is dedicated software required?
- Is a gateway required?
- Is an additional license required?
- Is data exchange one-way or bidirectional?
- Can data be exported if the vendor changes?

Interoperability and reliable data are important foundations before a facility expands into more complex AI applications.

Should Processing Occur on the Device, at the Edge, or in the Cloud?

IoT data can be processed in different locations.

On-Device Processing

This may be appropriate when:

- Rapid response is required.
- The system should not depend entirely on the network.
- The function is integrated directly into the device.

Gateway or Edge Processing

This may help:

- Aggregate data.
- Standardize data.
- Filter events.
- Reduce network traffic.
- Maintain certain functions when external connectivity is interrupted.

Server or Cloud Processing

This may be useful for:

- Analyzing data from multiple devices.
- Storage.
- Centralized management.
- Updating certain models.

There is no single model suitable for every situation.

Facilities should assess:

- Latency.
- Bandwidth.
- Data control rights.
- Ability to operate during network loss.
- Cost.
- Security.
- Vendor architecture.

Cybersecurity Cannot Be Separated from AI and IoT

Connected devices increase the number of components participating in the hospital network.

Risks may relate to:

- Default passwords.
- Shared accounts.
- Devices that no longer receive updates.
- Remote access.
- Unnecessary ports or services.
- Outdated software versions.
- Inappropriate authentication mechanisms.
- Devices not included in the inventory.
- Unmanaged cloud connectivity.

The FDA notes that medical devices connected to the Internet, hospital networks, or other devices provide new capabilities but also increase cybersecurity risks; reducing these risks requires coordination among manufacturers, hospitals, and other stakeholders.

Specific incidents also show that these risks are not merely theoretical. In 2025, the FDA warned about cybersecurity vulnerabilities affecting certain Internet-connected patient monitors, including risks of unauthorized access and potential impact on device data or functionality.

What Should Hospitals Check Before Connecting a Device?

Hospitals may review:

- Does the device have a unique identifier?
- Does it use default accounts?
- Can authentication credentials be changed?
- Is there a firmware update mechanism?
- How long will the manufacturer provide support?
- Which systems does the device need to communicate with?
- Does the device actually require Internet access?
- Which ports or services need to be used?
- Is remote access available?
- Who is permitted to access it?
- Are logs available?
- Can the device be placed within an appropriate network segment?

Devices should not be connected to the network first and have their integration and security requirements investigated afterward.

NIST also treats integration of an IoT product into a system as a change that may affect the risk assessment of the overall system. Security requirements should therefore be considered from the acquisition and implementation stages.

How Can AI Be Wrong?

False Alerts

The system detects a problem, but inspection finds no actual abnormality.

Potential consequences include:

- Increased workload.
- Unnecessary inspections.
- Reduced user trust.

Missed Events

An incident occurs, but the system does not generate an alert.

AI should therefore not automatically become the only control mechanism in situations where additional safety layers are still required.

The Model Does Not Fit the Environment

A model may have been developed using data from:

- A different device model.
- Another hospital.
- A different environment.
- Different utilization levels.

Performance at the new facility may therefore vary.

Performance Degradation Over Time

Data may change when:

- Firmware is updated.
- Components are replaced.
- Workflows change.
- Equipment is relocated.
- User groups change.

The model should therefore be monitored after deployment.

AI Does Not Replace Accountability

An AI alert should be linked to questions such as:

- Who receives the alert?
- Who validates it?
- Within what timeframe?
- What is the next action?
- Who is authorized to close the alert?
- Where is the outcome recorded?

If the system generates hundreds of alerts without a response workflow, the hospital has simply created another information source that staff must monitor.

An appropriate approach is generally to use AI within a system that has clear governance, human accountability, and mechanisms for evaluating effectiveness, rather than assuming that every decision should be automated.

Roadmap for Implementing AI and IoT in Hospitals

Step 1: Select a Specific Problem

Do not begin with:

“Deploy IoT across the entire hospital.”

Instead, the hospital may begin with:

- Too much time is spent locating equipment.
- A group of devices frequently fails unexpectedly.
- Temperature in a particular area needs to be monitored.
- The hospital needs to understand utilization of shared equipment.
- A system is generating too many alerts.

Step 2: Define the Action the System Needs to Support

Ask:

If the system detects this issue, what will staff do?

If there is no clear answer, it may be too early to build an AI model.

Step 3: Review Existing Data

Determine:

- Are data available?
- How far back do the data extend?
- Are there enough abnormal cases?
- Are device identifiers standardized?
- Are timestamps sufficiently reliable?
- Can the data be exported?

Step 4: Design the Connectivity Architecture

Determine:

- Devices.
- Gateway.
- Network.
- Server or cloud.
- API.
- Target system.
- Users.

Step 5: Establish Baseline Data

Before implementation, determine:

- How long does the process currently take?
- How many incidents occur?
- How many hours of device or system downtime occur?
- How many alerts are generated?
- What is the current utilization rate?

Without baseline data, it will be difficult to demonstrate effectiveness after implementation.

Step 6: Pilot on a Limited Scope

Select:

- One equipment group.
- One department.
- One alert type.
- One specific use case.

During the pilot, the hospital should maintain an appropriate validation mechanism, manual oversight, or a way to return to the existing workflow where necessary, rather than becoming completely dependent on the new system from the beginning.

It is not always necessary to operate two complete workflows in parallel if doing so causes:

- Duplicate data entry.
- Inconsistent data.
- Increased workload.
- Uncertainty among users about which system is authoritative.

The objective is to be able to verify results and return to a safe operating approach when necessary, not to duplicate the entire workflow.

Step 7: Evaluate

Possible measures include:

- Correct alerts.
- False alerts.
- Missed events.
- Detection time.
- Response time.
- Workload.
- Staff acceptance.

Step 8: Expand with Controls

Expand only when:

- Data are stable.
- Connectivity is sufficiently reliable.
- Effectiveness is sufficiently clear.
- Responsible personnel are defined.
- An operating budget is available.
- A long-term support plan is in place.

AI + IoT Solution Evaluation Checklist

Devices and Connectivity

- Which models are supported?
- Is a gateway required?
- Which protocols are used?
- Is bidirectional connectivity available?
- Is there an offline mode?
- Can data be exported?

Data

- Who controls the data?
- Where are data stored?
- How long are they retained?
- In which formats can they be exported?
- Are data used to train models?
- Is there a data quality control mechanism?

AI

- What problem does the AI address?
- Where do the training data come from?
- Are test data available?
- How is the false alert rate measured?
- How is the missed detection rate measured?
- Can thresholds be adjusted?
- Can users trace the reason or data associated with an alert?

Integration

- How does the solution connect with HIS, EMR, or CMMS?
- Is the API included within the scope of supply?
- Who is responsible for integration?
- Is a test environment available?
- Are transaction logs available?

Cybersecurity

- How is the device authenticated?
- Is there a security update mechanism?
- Is remote access available?
- How does the vendor manage software dependencies?
- What is the process when a vulnerability is identified?

Operations

- Who receives alerts?
- Is a support SLA available?
- Who trains users?
- How does the system operate during network loss?
- Can the AI function be disabled and an appropriate fallback workflow restored?

Lifecycle Cost

Costs to clarify may include:

- Licenses.
- Gateways.
- Cloud services.
- Storage.
- Integration.
- Maintenance.
- Upgrades.
- Connecting additional devices.
- Technical support.

Indicators to Monitor

Category	Example Indicators
Connectivity	Percentage of devices online, number of disconnections
Alerts	Number of alerts, false alert rate
Equipment	Utilization, downtime, recurring faults
Maintenance	Response time, percentage of maintenance completed on time
Operations	Equipment search time, response time
Data	Missing data rate, identification errors
System	Availability, recovery time
Users	Utilization, feedback, number of manual steps

Hospitals should not measure only the number of connected devices.

A project with 5,000 devices online does not necessarily create more value than a project connecting only 100 devices but solving a clearly defined operational problem.

Common Mistakes

Labeling a Threshold-Based Alert System as AI

A rule-based system can still be highly useful.

What matters is actual functionality, not the marketing label.

Assuming More Connectivity Is Always Better

More data do not automatically create more value.

Collecting Data Before Defining the Objective

If the hospital does not know which decision the data are supposed to support, the system can easily become an underused data repository.

Failing to Include Engineering and IT in the Same Project

IoMT typically sits between:

- Devices.
- Networks.
- Software.
- Data.

No single department can manage the entire ecosystem alone.

Depending on the Vendor's Cloud Without a Contingency Plan

The hospital should clarify:

- Data control rights.
- APIs.
- Data export capability.
- Operation during connectivity loss.
- Data handling when the contract ends.

Failing to Manage Legacy Devices

Older equipment may not support newer security or connectivity mechanisms.

Hospitals should not attempt to connect every device at any cost.

Automating Actions Too Early

During early stages, the hospital may prioritize a model such as:

AI detects → human validates → system records

rather than:

AI detects → automatically performs an action that directly affects the device

The level of automation should be determined according to product functionality, workflow, and risk level.

Failing to Measure the Workload Created by Alerts

A system that detects more events but requires staff to process hundreds of additional alerts every day may not necessarily create value.

Frequently Asked Questions About AI and IoT in Healthcare

Are AI and IoT in Healthcare the Same?

No. IoT mainly concerns device connectivity and data exchange, while AI focuses on analyzing data to identify patterns, make forecasts, or support decisions.

What Is IoMT?

IoMT stands for Internet of Medical Things and generally describes an ecosystem of connected medical devices and related technologies that exchange data within healthcare environments.

Is a Device with Wi-Fi Automatically Part of IoMT?

Not necessarily. Physical connectivity is only one component. The hospital should assess which systems the device exchanges data with, for what purpose, and how that connection is managed.

Can AI Automatically Control Medical Devices?

Certain products may include specific automated functions according to their design and intended use. However, hospitals should not assume that an AI analytics system is automatically authorized to change how a medical device operates. The level of automation should be defined according to product functionality, professional workflows, and applicable requirements.

Can AI Replace Biomedical Engineers?

No. AI may support anomaly detection, priority ranking, or historical analysis, but engineers still need to assess actual equipment condition and determine the appropriate action.

Is AI Required to Implement IoT in a Hospital?

No. IoT can create value through monitoring, location tracking, alerts, or rule-based automation without machine learning.

Can Older Equipment Participate in IoMT?

It depends on the device. Some systems may connect through gateways or middleware, but compatibility, safety, and manufacturer support should be assessed before implementation.

Where Should a Hospital Begin an AI + IoT Project?

The hospital should begin with a specific operational problem, define the required data and the actions that should follow an alert, and then pilot the solution on a sufficiently limited scope to evaluate effectiveness before expansion.

This article is provided for informational purposes only. Device connectivity, AI implementation, changes to technical workflows, and automated actions should be assessed by qualified personnel and implemented in accordance with manufacturer instructions, safety requirements, cybersecurity requirements, and current regulations.

How Much Practical Value Can AI and IoT Create? Explore the Perspective at PHARMEDI & HEALTHCARE VIETNAM 2026

As medical devices become increasingly connected, hospitals need to evaluate not only whether devices can transmit data, but also how those data are used, whether the system integrates with existing infrastructure, and how alerts are incorporated into operational workflows.

At PHARMEDI & HEALTHCARE VIETNAM 2026, the International Conference Series on Investment and Innovation in Healthcare directly addresses topics related to AI, investment, and hospital operations.

Day 1 – September 22, 2026: includes the session “Artificial Intelligence in Healthcare: Hype, Reality & Applications in ASEAN” from 2:30 PM–3:15 PM, alongside discussions on industry growth, investment, and procurement.

Day 2 – September 23, 2026: focuses on Hospital Management & Operations, covering the hospital of the future, operations, quality, clinical innovation, and growth strategies.

Across the exhibition area, professional visitors can continue comparing digital health, smart healthcare, medical devices, diagnostics, and laboratory solutions against their facility's real-world requirements for connectivity, data, security, and operations.

B2B Matchmaking – Exploring Implementation Capabilities in Greater Depth

For organizations that have already defined their AI, IoT, or IoMT use cases, B2B Matchmaking provides opportunities for one-to-one discussions with companies about device connectivity, gateways, middleware platforms, data control rights, downtime approaches, integration with HIS/EMR/CMMS, and long-term expansion costs.

In this way, PHARMEDI VIETNAM 2026 helps hospitals evaluate solutions using a more practical question: can the system be connected, governed, and used to create measurable value under real operating conditions?

Dates: September 22–24, 2026

Venue: Saigon Exhibition and Convention Center (SECC), Ho Chi Minh City

Exhibitor Registration:https://pharmed.vn/exhibitor/application-form

Visitor Registration:https://ers-vn.informa-info.com/pha2026?cid=FB

Contact:

Booth Booking: [email protected] (Thanh Thuý)

Media Support: [email protected] (Anita Pham)

Visitor Support: [email protected] (Ciel Le)