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

Originally published: Aug 4, 2025

Table Of Contents:

  1. What is multimodal AI in healthcare?
  2. How is multimodal AI used in healthcare?
  3. What are the benefits of using multimodal AI in healthcare?
  4. What are the challenges of multimodal AI in healthcare?
  5. What tools can be used for multimodal AI implementation in healthcare?
  6. How TileDB empowers the healthcare industry

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.

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. 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.

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. 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.
  • Early disease detection: 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.
  • Clinical trial design: Multimodal AI enables more effective clinical trials by improving patient selection, monitoring and outcome prediction.
  • Improved target discovery in drug development: Multimodal AI accelerates target identification by rapidly correlating data like gene expression or protein interactions with phenotypic outcomes.

What are the benefits of using multimodal AI in healthcare?

Multimodal AI benefits the healthcare industry by bridging the gaps between healthcare data types, enabling a deeper, more contextual understanding of health and disease. Some benefits include:

  • More accurate diagnoses
  • Streamlined drug development
  • Improved patient outcomes

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. Key challenges include:

  • Complexity of training AI/ML models: Requires experts in multiple fields to ensure AI/ML models can identify meaningful patterns across modalities.
  • Data privacy and security issues with AI in healthcare: Healthcare data is highly sensitive and must comply with strict regulations.
  • Difficulty integrating and scaling multimodal AI applications: Complicated to deploy AI applications into clinical workflows or research pipelines.

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

Tools for multimodal AI implementation facilitate the storage, integration and analysis of diverse biomedical data types. Key profiles include:

  • TileDB: Data management platform built on multi-dimensional arrays, facilitating secure data sharing and federated learning.
  • Flywheel: Streamlines medical imaging data management while integrating compliance with healthcare regulations.
  • Owkin: Agentic AI platform that specializes in biomarker discovery, patient stratification and clinical trial optimization.

How TileDB empowers the healthcare industry

TileDB equips healthcare providers with a database designed for discovery, helping to organize, structure, ease collaboration on and analyze multimodal data. For example, we delivered FAIR and ML-ready genomics data.

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