Multimodal AI in healthcare: Uses, benefits, challenges and more

Multimodal AI refers to machine learning or artificial intelligence models designed to process and integrate data from multiple data types, such as video, imaging, PDFs, audio or other modalities, to create richer datasets that enable more comprehensive analysis. One of the more exciting applications of Multimodal AI is in healthcare, which uses ML and AI models to integrate and analyze data from sources like genomics, single-cell data, electronic medical records (EMRs) and medical imaging to improve disease diagnosis and treatment. Common applications of multimodal AI in healthcare include personalized medicine, early disease detection, triage and clinical trial design. 

Implementing multimodal AI offers the healthcare industry many benefits, such as more accurate diagnoses, personalized treatment and improved patient outcomes. However, there are significant challenges in using multimodal AI in healthcare, which include the complexity of training AI models, maintaining data privacy and security as well as effectively integrating and scaling multimodal AI applications.

This blog explores the potential of multimodal AI in healthcare and how healthcare workers can unlock this potential. Let’s start with examining the role of multimodal AI in healthcare.

What is multimodal AI in healthcare?

Multimodal AI in healthcare is leveraging AI/ML models to first combine data from multiple sources such as clinical notes, imaging, genomics and wearable sensor data, and then use this integrated dataset to generate more accurate insights for diagnosis, treatment planning and research. By integrating diverse data types, multimodal AI systems can gain a more holistic understanding of human health than single-data-stream models.

Outside of healthcare, multimodal AI refers to ML models that can process and analyze multiple data types, such as text, images, video, and audio. Multimodal AI can help perform tasks like image recognition, language translation and spoken speech recognition. It is also useful for more complex AI applications like self-driving vehicles or virtual agents who can understand voice commands, text inputs and visual cues to provide advice and services to human users. 

The main difference between multimodal AI and single-modal AI is how users input data into the application. Single-modal AI is limited to a single type of input, such as only analyzing X-ray images or only processing clinical text; this makes single-modal AI less capable of capturing complex interactions across biological or clinical contexts. In contrast, multimodal AI can integrate and analyze all kinds of healthcare data types, which enables models to recognize patterns across modalities that would otherwise have been lost in the noise.

An example of multimodal AI in healthcare is Google’s MedPaLM, a large language model trained on a combination of medical imaging, clinical text, genomics, EMRs and patient metadata. MedPaLM was the first AI system to surpass the 60% pass mark on U.S. Medical Licensing Examination-style questions. With the newer MedPaLM 2, users will be able to input medical imaging like X-rays into the application and ask for clinical analysis and reports.

Let’s move on to exploring common applications of multimodal AI in healthcare.

How is multimodal AI used in healthcare?

Multimodal AI has a wide variety of applications in healthcare, from designing more effective clinical trials to accelerating drug discovery. By combining traditionally siloed data sources like genomics, proteomics and imaging, multimodal AI is transforming how clinicians make care decisions and detect diseases as well as how researchers develop new therapies. Key uses of multimodal AI in the healthcare industry include:

• Personalized medicine: Multimodal AI helps stratify patients more effectively by integrating datasets related to clinical history, genetic profiles, lifestyle data, real-time biometrics and more. By using AI to analyze these large multimodal datasets at scale, healthcare providers can tailor individual treatments based on a holistic view of each patient. This improves therapeutic outcomes while reducing the risk of adverse reactions.

• Early disease detection: In the same vein as personalized medicine, multimodal AI can combine data from imaging scans, electronic health records (EHRs), genetic profiles and molecular diagnostics to identify patterns that signal the early onset of disease. This can often detect illnesses like cancer before symptoms appear, enabling earlier and more effective treatment.

• Clinical trial design: Multimodal AI enables more effective clinical trials by improving patient selection, monitoring and outcome prediction. By integrating genomic, imaging, EHR and behavioral data from wearable devices and smartphones, AI models can identify ideal candidates for a given trial and predict likely responders. Multimodal AI applications also make it easier to continuously monitor safety and efficacy signals throughout a clinical trial so researchers can adapt as needed. This not only increases the likelihood of regulatory success, but also reduces trial costs and shortens timelines.

• Improved target discovery in drug development: In pharmaceutical research, finding the target molecules or genetic entities affected by a disease is vital to developing new drugs or treatments. Multimodal AI accelerates target identification by rapidly correlating data like gene expression or protein interactions with phenotypic outcomes observed in clinical or imaging data. By quickly analyzing this multimodal data using AI applications, researchers can prioritize viable drug targets and design more effective therapeutic interventions earlier in the development pipeline. TileDB’s database technology plays a key role in target identification by helping researchers organize, structure, collaborate on and analyze multimodal data at scale.

What are the benefits of using multimodal AI in healthcare?

Multimodal AI benefits the healthcare industry by using AI and ML models to bridge the gaps between healthcare data types. By integrating clinical, genetic, molecular and behavioral data, multimodal AI enables a deeper, more contextual understanding of health and disease. Some benefits of using multimodal AI in healthcare include:

More accurate diagnoses
A more holistic vision of a patient’s health enables more targeted and effective treatment. By analyzing a range of data sources and modalities simultaneously, multimodal AI equips clinicians to pinpoint specific disease mechanisms and reduce diagnostic uncertainty. This can help detect complex or rare conditions that single-modality approaches might miss.

Streamlined drug development
In biotech research and development, multimodal AI accelerates timelines by improving target identification, patient recruitment and clinical trial design. By integrating analysis of omics, imaging, clinical data and other data types, multimodal AI can help research teams eliminate non-viable candidates, generate new molecular structures and predict drug interactions. This translates to reduced cost and increased success rates.

Improved patient outcomes
In addition to more precise diagnostics, the more complete view of patient health enabled by multimodal AI also supports personalized care strategies. These strategies draw on different datasets related to a patient’s health to align care with individual risks and responses. The results are earlier and more effective interventions, fewer adverse events and better treatment efficacy.

What are the challenges of multimodal AI in healthcare?

The challenges of multimodal AI in healthcare can be summed up as adding complexity to complexity. It requires taking the innate complexity of multimodal healthcare data from many sources and formats then adding the technical and operational hurdles of training robust AI/ML models. To even make multimodal data readable by AI applications, life sciences researchers must make their data FAIR (Findable, Accessible, Interoperable and Reusable). This structures and standardizes datasets in ways that support scalable and reproducible AI development.

Here are three key challenges of implementing multimodal AI in healthcare:

• Complexity of training AI/ML models: Developing multimodal AI models requires training them to align and analyze datasets across formats, sources and time points—each with its own noise and bias. This requires experts in machine learning, data science and life sciences research to work together to ensure AI/ML models can identify meaningful patterns across modalities. Training and operating such AI models is also computationally intensive, so research teams will also need abundant cloud computing resources.

• Data privacy and security issues with AI in healthcare: Healthcare data is highly sensitive and subject to strict privacy regulations such as HIPAA and SOC Type 2. Multimodal AI systems often require aggregating data from multiple sources and institutions to work effectively. This means models need to be trained to comply with patient consent, data anonymization and other information security requirements. A promising solution to this issue is federated learning, which enables AI models to be trained on decentralized datasets by accessing the sensitive data without moving it from its secure location. Learn more about federated queries in trusted research environments here.

• Difficulty integrating and scaling multimodal AI applications: Beyond the challenges of creating and training multimodal AI, it can also be complicated to deploy into clinical workflows or research pipelines. AI applications must be properly integrated into legacy systems like EHR repositories, imaging archives and laboratory information systems, which means making all these datasets FAIR. Additionally, scaling these AI systems across institutions is difficult due to variations in data quality, staff expertise, storage infrastructure and interoperability standards.

In short, healthcare companies often struggle with implementing multimodal AI due to the sheer complexity of both the data and the systems involved. Bringing disparate healthcare data types together in a usable form requires not only advanced technical infrastructure but also strict adherence to FAIR data principles, which many healthcare organizations are still working to adopt. Adding to the challenge is the need to ensure regulatory compliance and safeguard patient privacy—all while demonstrating clear clinical or business value that earns the trust of clinicians. As a result, even when organizations have the capacity and desire to implement multimodal AI, operational, technical and cultural hurdles can delay or limit implementation.

What tools can be used for multimodal AI implementation in healthcare?

Multimodal AI implementation tools facilitate the  storage, integration and analysis of diverse biomedical data types while meeting healthcare’s requirements for compliance, scalability and interoperability across systems. When you adopt a solution to streamline your AI development, it needs to handle the entire pipeline from data ingestion and harmonization to model training and deployment—all while maintaining data privacy and delivering performance at scale.

Here are profiles of three leading solutions for implementing multimodal AI in healthcare:

TileDB:
TileDB built its data management platform on multi-dimensional arrays designed to handle high-resolution biomedical datasets such as genomics, medical imaging, spatial transcriptomics, single-cell data and more. This enables users to optimize how they model such complex data modalities for more efficient storage and querying in a unified data catalog. The result is a multimodal data foundation that accelerates AI workflows. TileDB also facilitates secure data sharing and federated learning, making its platform well-suited for collaborative and privacy-sensitive trusted research environments.

Flywheel:
Flywheel is a database platform designed to streamline medical imaging data management. By integrating with hospital systems and research tools, Flywheel organizes and analyzes imaging alongside structured clinical and behavioral data. It supports AI model development with end-to-end workflows that include de-identification, annotation and machine learning integration for multimodal datasets. Flywheel also boasts a dedicated team focusing on compliance with standards like HIPAA, GDPR and 21 CFR Part 11, making it a strong choice for clinical research and translational medicine.

Owkin:
Owkin is an agentic AI platform that combines diverse multimodal data and quantification technologies to create AI agents built for life sciences research. It features federated learning capabilities that enable researchers to develop AI models across multimodal datasets without moving the sensitive data from its original location. Owkin's AI tools specialize in using multimodal analysis for biomarker discovery, patient stratification and clinical trial optimization.

Tips for selecting tools for multimodal AI implementation in healthcare:

• Ensure the solution is compliant with healthcare regulations (HIPAA, GDPR, SOC Type 2, etc.) and includes capabilities to simplify compliance.

• Look for solutions offering native support of the data modalities you use most, such as imaging, genomics and single-cell data.

• Check for interoperability with your existing data infrastructure. If standards like HL7, FHIR and DICOM are important to your operations, you want your multimodal AI solution to follow them without disruption.

• Evaluate the solution’s ability to scale and perform well across your research and production environments. Multimodal datasets are inherently complex, so you need a data management platform that can scale with demand.

• Prioritize tools with built-in capabilities for data harmonization, annotation, cross-team collaboration and federated learning. This will simplify both your user experience and the training of AI models.

• Consider platforms with a proven track record in clinical or pharmaceutical deployments. It helps if the provider organization includes clinical experts who recognize the complexities of healthcare data and regulations.

How TileDB empowers the healthcare industry

TileDB equips healthcare providers and research teams with a database designed for discovery, helping to organize, structure, ease collaboration on and analyze multimodal data. This elegant data platform effortlessly consolidates diverse modalities in a unified dataset, enabling queries with an array-powered serverless engine that scales to your most complex data use cases. To support your multimodal AI applications, TileDB helps make multimodal data FAIR-compliant so it can be used effectively by AI and ML models. For example, we delivered FAIR and ML-ready genomics data in a unified data mesh for Quest Diagnostics, helping them store and scale 6 million yearly samples of analysis-ready variant data.

To learn more about how TileDB can empower your AI applications to fulfill the potential of your multimodal data, contact us.