Multimodal AI Development for Healthcare: Architecture, Medical Imaging Integration, & Clinical Data Processing

Healthcare institutions produce extensive data content, including medical imaging, electronic health records (EHRs), lab results, doctors' notes, and patient monitoring systems. But much of that data is stored in various systems.

This creates a problem when designing an AI agent that needs to have a complete picture of the patient's clinical record. For example, an image-based model may be provided with a CT image but not with lab test data, previous diagnoses, or clinical history of the patient.

Multimodal AI attempts to solve this problem by taking in data from multiple data types into a single AI model. Multimodal AI can bring and merge different data types, such as the use of medical imagery with structured and unstructured clinical notes and other patient data, for contextual understanding.

This helps medical technology companies build a healthcare app that supports imaging workflows, clinical data analysis, patient monitoring, and decision-making.

But developing these kinds of apps goes beyond just piecing together multiple AI models. It's also necessary for the underlying platform to support varying data formats and to synchronise patient records, compensate for absent inputs, and incorporate results into current healthcare infrastructure. The guide looks at multimodal AI in healthcare, architecture, its application to medical imaging, clinical data, its development, costs, and regulation.

How Multimodal AI Connects Different Healthcare Data Sources

Multimodal AI is a type of artificial intelligence that can analyse and relate information from two or more modes or types of data. AI that uses only a single mode of data is known as single-modality AI.

In healthcare, the data could be medical images, clinical notes, lab results, EHR data, genomic data, wearable data, voice recordings, and so on.

As an example, a healthcare AI system could take an X-ray of a patient's chest in conjunction with their clinical history and lab results. The system can then use each of those things in concert with one another to produce a systemic analysis for later medical review.

Single-modality AI vs. multimodal AI

Data Types Used in Multimodal Healthcare AI

• Medical images: X-ray, CT and MRI images, ultrasound images, pathology images and other diagnostic images.
• E-Health records: Information on the patient's profile, diagnosis, coding, history, drug information, and patient treatment.
• Diagnosis: Laboratory information, blood test reports, pathology reports, biochemical reports, and any other clinical notes.
• Clinical text: Notes from doctors or consultants, referrals, discharge papers, or radiology and patient history.
• Sensors and wearables: Heart rate, saturation, activity, sleep, continuous monitoring.
• Genomic data: Genome, molecular, and other biological data.

But the key word here is "connect", and multimodal AI's performance depends entirely on how well they do that. Putting data together is simply not enough. There has to be the right kind of relationship between them-and, critically, the right clinical context.

In 2024, a review in PeerJ Computer Science summarised 69 papers on healthcare data fusion and described techniques for fusing medical images, structured records, and unstructured clinical data. It also highlighted technical issues with multimodal fusion.

Multimodal AI Healthcare Architecture

A multimodal AI healthcare architecture links various clinical data sources to one another, interprets each source, integrates relevant data, and produces results via an app or within a clinical workflow.

The model should accommodate data variations in formats, processing needs, availability, and clinical interpretation. For instance, an imaging model requires image pre-processing, and clinical notes need to perform text extraction and language processing. A typical architecture contains six layers.

Data ingestion layer

The ingestion layer gathers data from other systems and external devices.

These are typical sources:

• Medical image vendor-neutral archives (VNAs) and PACS.
• EHR and EMR systems for patient records.
• Laboratory information systems (LIS) for test results.
• Hospital information systems (HIS) - for operational and clinical data.
• Wearables and remote patient monitoring devices.
• Clinical documentation platforms for notes and reports.

Integration approaches will be source system-specific. These include, but are not limited to, DICOM, DICOMweb, HL7 v2, FHIR APIs, REST APIs, database connectors, and device protocols.

The ingestion layer will need to perform the validation of all incoming records, detect partially populated payloads, and attach data to the appropriate patient and encounter.

Data preprocessing layer

Different healthcare data types require different preprocessing methods before model inference.

It is necessary to maintain important clinical data. For instance, conversion of lab values without preservation of their units may lead to wrong outcomes.

Modality-specific encoders

Each type of data is encoded through an appropriate model or encoder.

• Vision encoders: Encode features from X-ray, CT, MRI images and pathology images.
• Text encoders: Encode clinical notes, radiology reports, and discharge summary documents.
• Time series encoders: Encode patient monitoring data and sequential data.
• Structured data models: Encode diagnosis, drugs, lab values, and other tabulated information.

The encoder takes the raw input and outputs the encoding that can be further consumed by the fusion layer.

Model selection is determined by the application. For instance, a computer vision model used to perform image segmentation is likely inappropriate to use for summarising medical data.

Multimodal fusion layer

This layer brings together the representations from multiple encoders.

There are three major forms of fusion that are commonly used.

Early Fusion

Early fusion brings together the features of the input before the beginning of the processing stage of the model.

Used when: The sources of data are very similar and accessible at the same time.

Weaknesses: Difficulties arise when data sources have different types of data and are incomplete.

Intermediate Fusion

Intermediate fusion involves processing the modalities separately and then combining them using their representations in the layers, which use an attention- or transformer-based approach.

Used when: The application requires establishing connections between the imaging, clinical text, and structured data.

Weaknesses: Complex architecture and careful training.

Late Fusion

Late fusion is done by combining the outputs of the modalities after they have been processed separately by the aggregating layer.

Used when: The applications require modular models and sometimes work without one or several data sources.

Weaknesses: There are difficulties in capturing cross-modal relationships.

For example, a system that combines CT scan data with clinical notes could potentially be enhanced using intermediate fusion, as it is capable of learning correlations between image-based information and text-based information. A combination of independent images and lab scores can potentially employ late fusion.

A 2024 review on multimodal health care research touches upon the three types of fusion mentioned above.

Model output layer

The process of fusion produces an output from the system according to its functionality.

Examples of outputs are:

• Classification and detection for images.
• Segmentation masks.
• Structured clinical information.
• Image and text summarization.
• Risk assessment for clinical analysis.
• Patient information from associated record(s).

The output must contain proper sources, the version of the model used, processing state, and confidence information wherever applicable.

For clinical use cases, confidence values do not validate the medical correctness of the result.

Application and clinician interface

This is the last layer through which model outputs become accessible via the healthcare application.

This depends on the particular product and may include:

• Radiology review dashboard.
• Clinical decision support system interface.
• Patient summary EHR interface.
• Diagnostic workflow management application.
• Healthcare analytics application.

The interface needs to allow authorised individuals to view the source information, view model outputs, and make their decisions.

Choosing the right model architecture

Not every multimodal healthcare application requires a custom foundation model.

One of the methods that should be adopted for the development process is the evaluation of existing models before considering custom training, since this will save unnecessary development efforts and help in concentrating on other aspects.

Medical Imaging Integration for Multimodal AI

The integration of medical imaging is among the technically challenging tasks in the design of multimodal AI.

Various imaging devices, PACS systems, viewers, and reporting software are used in hospitals and clinics. An AI software solution must be able to integrate with such devices without interfering with the current workflow of medical imaging.

Connecting DICOM, PACS, and DICOMweb

DICOM (Digital Imaging and Communications in Medicine) is the standard for storing, transmitting, and receiving medical imaging information.

It is composed of image data, along with metadata of the following types:

• Patient and study identifiers.
• Imaging modality.
• Study and series information.
• Acquisition date and time.
• Image dimensions and orientation.
• Equipment and acquisition details.

The AI system, based on multiple modalities, could fetch related studies from PACS and send them to an imaging model for processing. This exposes DICOMweb services over HTTP.

This provides the following primary services:

• Search DICOM studies, series, and instances.
• WADO-RS: Retrieves DICOM objects and image data.
• STOW-RS: Stores DICOM objects and supports imaging results.

They enable web-based integration of the image archive, AI processing service, and healthcare application.

Processing images through AI models

Once imaging data is retrieved, the application prepares it for model processing.

The processing requirements depend on the intended task.

For instance, an app for radiology workflow assistance may utilize an imaging model to locate the areas of concern.

A different language model can assist in organizing the relevant information into a draft report, which should then be reviewed by an appropriate clinician.

Connecting imaging results with FHIR

Another relevant integration requirement is that the imaging information is made available to the other applications in the healthcare enterprise.

When considering standards that support them, imaging objects are handled by DICOM, whereas HL7 FHIR offers resources for data exchange.

One example of an integration is:

• ImagingStudy: Represents an entire DICOM imaging study and its related series and instances.
• DiagnosticReport: Defines the diagnostic results and analysis, referencing images and notes.
• Observation: Represents individual clinical measurements or results.
• DocumentReference: References clinical documents and other non-FHIR content.

A key technical difference is that the ImagingStudy resource in FHIR describes the imaging study and its objects. It does not provide the DICOM image files themselves. These are still available via an imaging archive or other storage location.

The DICOM-to-model-to-FHIR workflow

Imagine a healthcare solution that integrates CT imaging and a patient's clinical history.

A suggested processing flow may be:

• Image acquisition: A CT scanner produces a DICOM study. The study is copied onto the PACS.
• Study retrieval: The AI application calls the relevant study via DICOM or DICOMweb.
• Metadata validation: The integration service validates study identifiers, the association with the patient, image characteristics, and the required metadata. The image is fed to the imaging model, which processes it and produces the output for the image. The software pulls relevant patient data from the EHR using authorized identifiers and access controls. The integration layer expresses the study and supported results using the relevant FHIR resources.
• Clinical review: The result is displayed within the approved care pathway. The implementation may also utilise other services for storing images, orchestrating models, processing asynchronously, and auditing. The aim is to integrate the visualization analysis into clinical applications without destroying the hospital PACS/EHR information system.

Clinical Data Processing for Multimodal AI

Medical imaging is only one piece of a patient's clinical record. For developers to create effective multimodal applications, they also need to analyze structured records, clinical notes, lab results, and more.

The problem is that health information is stored in multiple formats, with varying terminology.

Standardizing clinical data with FHIR

FHIR makes the exchange of healthcare information simple by utilizing resources and APIs.

Here are the typical resources for multimodal healthcare:

FHIR mapping enables your application to share data across platforms.

There are still a lot of data-quality issues that aren't solved by adopting FHIR. You still need mapping rules; you still need to validate terminology; you still need patient matching, and you still need to account for differences between system implementations.

Using OMOP for clinical data normalization

An OMOP Common Data Model is a standardised representation of the data collected during observational studies.

It is particularly relevant to applications involving:

• Research datasets from multiple healthcare organizations
• Population-level analytics
• Longitudinal patient data
• Clinical outcomes research
• Predictive modelling across structured clinical records
• FHIR and OMOP serve different purposes

FHIR is focused on healthcare data exchange and interoperability. OMOP is focused on standardization of observational data for analysis. A healthcare app might leverage FHIR for operational integrations and OMOP for research-level data processing. Neither is required for every project.

Processing unstructured clinical notes

Notes in the chart include facts that are not present in structured EHR fields.

Natural language processing (NLP) can extract relevant information from:

• Physician consultation notes
• Radiology reports
• Discharge summaries
• Referral letters
• Clinical assessments
• Pathology reports

For instance, the NLP pipeline could extract diagnoses, medications, symptoms, and other medical events from the notes written by a doctor. Then the information can be mapped to standard clinical concepts and linked with other records. The system has to represent negation, uncertainty, and temporal information. A note that records the fact that the patient does not have a condition does not yield a positive diagnosis.

Handling missing and inconsistent data

A multimodal healthcare system should not take it for granted that data type(s) required for all patient records exist in the system.

A patient could have an image, but not a recent lab report. A second patient could have long clinical notes, but no wearable-device data.

The application should take these cases into account as follows:

• Missing-modality detection
• Data-quality validation
• Timestamp alignment
• Unit and terminology normalization
• Duplicate-record detection
• Patient and encounter matching
• Explicit fallback logic

For example, the model should not expect to be given one input –a note or an imaging study- and then produce an output automatically if the other input is missing.

It should either employ a validated alternative processing path or else it should transparently document that the information needed is missing.

De-identification and data alignment

Healthcare datasets often contain direct identifiers and sensitive clinical information.

Before using data for model development or research, organizations should determine the appropriate legal basis, permitted purpose, access restrictions, and de-identification requirements.

De-identification processes may include removing or transforming identifiers from:

• Patient names and contact information.
• Medical record numbers.
• Dates and location details where required.
• Identifying information in clinical notes.
• DICOM metadata.
• Embedded image annotations.

Image de-identification requires particular care because patient information may appear within image pixels, not just metadata.

Time alignment is another important requirement. A CT scan from one date should not automatically be combined with a laboratory result from several months earlier as if both represented the same clinical state.

The processing pipeline should retain relevant timestamps and distinguish between historical and current information.

Build, Buy, or Partner for Multimodal AI in Healthcare?

Not all sections of an AI system need to be built from the ground up. The optimal solution will vary depending on how you plan to use it, the models available, what kind of data you have, your regulatory environment, and your healthcare infrastructure.

When to buy an existing AI solution

Buying or licensing an existing product may be appropriate when:

• The intended use is already supported by an available model.
• The vendor provides clinical validation and regulatory clearances.
• The organisation needs a specific imaging capability.
• It can be connected to its PACS or EHR.

A similar model can be developed, but it will take a great deal of research.

For instance, a diagnostic centre interested in a proven imaging-analysis tool should examine what products are ready and available before commissioning a new diagnostic model.

When to fine-tune an existing model

According to the level of adaptation required, fine-tuning may be the answer when there's either a readily available pre-existing/predistributed model that already performs the required function.

Possible requirements:

• Supporting a specific imaging modality.
• Adapting to a particular clinical vocabulary.
• Processing a defined set of clinical documents.
• Improving performance on a representative target dataset.
• Linking image and text representations for a single process.

Fine-tuning should have relevant training data, evaluation metrics, and validation. It is not a turnkey solution to establish clinical utility.

When to build a custom multimodal application

Custom development is necessary when the business requires functionality that off-the-shelf products cannot deliver effectively.

Examples include:

• A unified imaging and EHR integration platform.
• A multimodal patient-record analysis application.
• A clinical documentation and information retrieval system.
• A diagnostic workflow management platform.
• An AI-powered healthcare analytics application.
• A custom patient monitoring solution.

Build vs. buy decision guide

Multimodal AI Healthcare Development Cost and Timeline

Multimodal AI's cost of development is also determined by factors such as the number of data modalities and model specifications, healthcare integrations, infrastructure, validation, etc.

An ecosystem that links a current imaging model to an EHR is very different from an entire healthcare platform that ingests imaging, clinical notes, laboratory results, and data from wearables.

Estimated development stages

The following are illustrative planning windows, not guaranteed delivery estimates or industry-wide benchmarks.

These are based on a defined scope of the project, right data access, and decision-maker availability. Clinical validation, procurement, and regulatory activities may extend the overall programme. 

Key Factors That Drive Multimodal AI Development Cost

1. Number of modalities

Merging two data sources is usually easier than merging imaging, test labs, doctor notes, genome information, and wearables.

2. Model selection

Different engineering and compute requirements are needed for an off-the-shelf model, fine-tuning a pretrained model, or creating a custom architecture.

3. Healthcare integrations

Integrating with a single system that is FHIR-enabled isn't the same as integrating with multiple legacy EHRs, PACS systems, and hospital systems.

4. Data preparation

Data annotation, de-identification, normalisation, handling of missing values, and quality checks of data sets can become fairly large workstreams.

5. Validation requirements

A research prototype and a system designed to support clinical decision-making have very different assessment and validation requirements.

6. Infrastructure and operational activities

Total cost of ownership includes the cost of cloud or on-premises deployment, GPU resources, secure storage, monitoring, model updates, and support.

Hidden Costs in Multimodal Healthcare AI Projects

Development budgets must also include:

• Medical image annotation and specialist review
• Third-party model licensing and API usage
• Cloud storage and GPU consumption
• Data migration and integration maintenance
• Security assessments and penetration testing
• Model monitoring and drift evaluation
• Retraining and change management
• Clinical and regulatory consultation

The estimate should be based on an assessment of the discovery and the data readiness, not a per-modality generic price. If a healthcare company is just preparing to bring its very first multimodal AI item to market, a targeted pilot might be less complicated than rushing to construct an entire platform.

Compliance and Regulatory Considerations

Medical AI tools handle personal sensitive data and have the potential to impact care decisions. Their design needs to consider data privacy and security, but also other factors such as use-case and relevant medical-device standards.

Conformance should be addressed in the product planning stage, not "tacked on" at the end of the process.

HIPAA

If your app processes protected health information in the United States, the HIPAA Privacy, Security, and Breach Notification Rules may apply.

All safeguards must be in place, including physical, administrative, and technical safeguards, under the HIPAA Security Rule.

Development considerations include:

• Role-based access controls
• Authentication and authorisation
• Encryption in transit and at rest
• Audit logging
• Secure backups
• Incident response procedures
• Appropriate business associate agreements

The responsibilities that apply will depend on the organisation and the service.

India's Digital Personal Data Protection Act

Healthcare apps that process digital personal data will need to assess their compliance with the Digital Personal Data Protection Act, 2023, and the DPDP Rules, 2025.

The Rules were notified in November 2025 and were brought into force through the commencement notification.

Relevant product-engineering considerations include:

• Clear purposes for personal data processing
• Appropriate notice and consent mechanisms where required
• Data minimisation
• Reasonable security safeguards
• Retention and deletion policies
• Processes for handling data-principal rights
• Contracts and responsibilities involving data processors

Reinspect the applicable commencement dates and requirements for the processing activities for which the organisation is responsible, rather than assuming that each commencement date applied to everything.

GDPR

Medical data would normally be classified as special category personal data.

Those applications which cater to people in the European Economic Area will need to consider the applicable requirements in relation to the processing of health data including:

• A lawful basis that applies and a special-category processing condition
• Data protection by design and default
• Purpose limitation
• Data minimisation
• Appropriate technical and organisational safeguards
• Data protection impact assessments where required
• International data transfer requirements

These requirements should be met in the product architecture using: access controls, data lifecycle management, audit logs and appropriate processing agreements.

EU AI Act

The EU AI Act imposes duties depending on the nature and purpose of the AI system.

Where healthcare AI is used as a safety component of a regulated medical device, then the high-risk AI framework might also be relevant depending on classification and conformity assessment obligations. The original general application date of the AI Act was in November 2026. The new application date will be December 2, 2027, for high-risk AI in Annex III and August 2, 2028, for high-risk AI in products in Annex I. Consider the product's use and medical-device regulations before the manufacturing requirements are defined.

FDA and CDSCO medical-device considerations

Not all healthcare AI apps are automatically a medical device. The regulatory status is determined by intended use, functions, claims, and jurisdiction. US defines some clinical decision-support functions and software that meet the definition of a medical device. One important consideration is whether a health professional is able to independently assess the validity of a recommendation. Software analysing a patient's medical images may not be exempt from non-device CDS as described by the FDA.

In India, the Drugs and Cosmetics Act, 1940 and the Medical Devices Rules, 2017 govern the regulation of medical devices. Within the definition of a medical device that CDSCO uses, software can be classed as a device if it has the intended-use features as defined. Devices are risk-classified.

A healthcare company would need to consult regulatory authorities before commercialising an AI application intended for diagnosis, treatment, or other regulated medical functions.

It can be very different across the three different applications that make up a typical workflow: a software tool for building a clinical workflow; a paper-based administrative summary for providers; an AI-generated diagnostic recommendation.

Our Approach to Multimodal AI Development: Start with a Pilot

Build a multimodal healthcare product around a well-understood business or clinical workflow. Accumulating multiple data sources, models, and integrations can add to the complexity before the fundamental design has been proven. A pilot-first approach allows a healthcare company to pilot the architecture, test the data, and determine integration pain points before rolling out the product.

Step 1. Define the use case and requirements

The first step is to identify what the application needs to accomplish.

This includes:

• Defining the intended users and workflow.
• Identifying the data modalities required.
• Selecting the expected model outputs.
• Reviewing existing healthcare infrastructure.
• Establishing functional and technical requirements.

Deliverable: A product requirements document and initial technical architecture.

Step 2. Conduct a data-readiness assessment

Review the availability, quality, structure, and accessibility of the required clinical data.

The assessment should cover:

• Data formats and standards.
• DICOM and FHIR compatibility.
• Patient and encounter identifiers.
• Missing or inconsistent records.
• Data access permissions.
• De-identification requirements.

Deliverable: A data-readiness report and integration plan.

Step 3. Develop the pilot architecture

Select suitable models and define how the different data sources will communicate.

The development team can establish:

• Data ingestion pipelines.
• Modality-specific preprocessing.
• Model integration and orchestration.
• Fusion architecture.
• Output generation.
• Basic application interfaces.

Deliverable: A working pilot with the agreed data modalities and functionality.

Step 4. Evaluate and validate the application

Test the application against representative datasets and predefined evaluation criteria.

Depending on the intended functionality, evaluation may include:

• Input data quality.
• Image-processing performance.
• Clinical information extraction.
• Missing-modality handling.
• Output consistency.
• Integration reliability.
• User workflow usability.

Clinical validation requirements should be established with appropriate healthcare professionals. Technical testing alone does not establish clinical safety or effectiveness.

Deliverable: Evaluation results, identified limitations, and an improvement plan.

Step 5. Deploy and monitor

After the necessary technical and regulatory requirements have been addressed, the application can move towards deployment.

This stage includes:

• Secure infrastructure configuration.
• API and healthcare-system integration.
• User access management.
• Audit logging.
• Application monitoring.
• Model version management.
• Maintenance and support.

Deliverable: A deployment-ready application and an ongoing maintenance plan.

Reference Build and Data-Readiness Resources

One way to assess a multimodal AI architecture is to develop a limited reference implementation using research datasets.

This allows development teams to test data ingestion, preprocessing, model integration, and output generation before connecting to production healthcare systems.

Public datasets for multimodal AI development

Some useful research datasets include:

MIMIC-IV and MIMIC-CXR require credentialing, training, and a data-use agreement. They are not intended to be freely available public downloads.

CheXpert may also be used for research and development of chest X-ray models. Check the terms of access and allowed uses before applying it for a commercial project. For example, a reference implementation could join a clinical dataset with an imaging dataset to show how separate data pipelines and model outputs can be connected.

That doesn't mean patient records from different datasets can be directly matched, however. These must have identifiers, access terms, and possible research applications before one might try to link records.

A demonstration created using research datasets that is not labelled as a clinical prototype. The demonstration must not, directly or indirectly: a. give the impression that it has been shown to be clinically validated; or b. result in a clinical validation or a demonstration of diagnostic accuracy.

Multimodal AI data-readiness checklist

Here are some considerations you should keep in mind before you start the development process.

• Identify the intended application and users.
• List all required data modalities.
• Confirm access to representative datasets.
• Review DICOM, HL7, and FHIR compatibility.
• Check patient and encounter identifiers.
• Assess missing, duplicate, and inconsistent records.
• Define preprocessing and normalization requirements.
• Establish data privacy and de-identification requirements.
• Select suitable pretrained or custom models.
• Define evaluation and validation criteria.
• Identify required PACS, EHR, and LIS integrations.
• Plan secure deployment and ongoing monitoring.

This evaluation enables the detection of issues in the course of development if they are likely to threaten the time frame or budget of the project.

Conclusion

To create an effective multimodal healthcare application, you need a hybrid of AI engineering, software engineering, data integration, and AI workflow orchestration expertise.

Suffescom specialises in building digital products and implementing AI features in business applications. The extent of development is determined by the technical and other compliance requirements of the project.

Do not claim to be a provider of clinically validated diagnostic models or regulatory-cleared medical devices without evidence. Do not make causal claims based on non-peer-reviewed evidence. Do not interpret the data, analyse it, or draw conclusions about model selection, clinical validation, and regulatory approval separately.

Our goal is to help healthcare companies design the right AI features into operational, secure, and easily manageable digital products.

FAQs

1. What is multimodal AI in healthcare?

Multimodal AI in healthcare is an approach that combines different types of patient information, such as medical images, EHR records, laboratory results, clinical notes, and wearable data. It allows AI applications to process information from multiple sources instead of relying on a single data type.

2. How is multimodal AI different from traditional medical AI?

Traditional medical AI applications often focus on a specific task, such as analyzing an X-ray or extracting information from clinical notes.

Multimodal AI connects multiple data types within one architecture. For example, it can combine imaging information with relevant clinical records to generate a more context-aware output.

3. What data is required to build a multimodal healthcare AI application?

The required data depends on the intended functionality. An imaging and clinical-record application may require DICOM images, EHR records, radiology reports, and laboratory results.

The development team should assess data availability, quality, permissions, and compatibility before selecting the final architecture.

4. How long does multimodal AI development take?

A limited proof of concept may take approximately 6–10 weeks, while a focused multimodal pilot may require 10–16 weeks. A larger healthcare platform with multiple integrations and workflows can take several months or longer.

The timeline depends on data readiness, model selection, integration complexity, and validation requirements.

5. How much does multimodal AI development cost?

There is no fixed cost for multimodal healthcare development. A prototype using existing models and limited datasets requires a different budget from a production application with PACS, EHR, and FHIR integrations.

Model licensing, data annotation, infrastructure, security, and validation also affect the total investment. A technical discovery and scope assessment is the best way to establish a reliable estimate.

6. Can multimodal AI integrate with existing PACS and EHR systems?

Yes. Healthcare applications can integrate with existing PACS and EHR systems through standards and interfaces such as DICOM, DICOMweb, HL7, and FHIR.

The integration approach depends on the capabilities of the existing systems and the data that the AI application needs to access.

7. Is multimodal AI safe and compliant for clinical use?

Multimodal AI applications require appropriate data protection, cybersecurity, model evaluation, and clinical validation.

Regulatory requirements depend on the application's intended use and the markets in which it operates. A technical prototype should not be treated as a clinically validated system.