Multimodal AI Examples and Applications | 2025 Edition

Ever wondered how AI can read a medical scan, understand a customer’s voice, and even recognize what’s happening in a video, all at once? That’s multimodal AI in action. In simple terms, multimodality means the ability to understand and connect different types of information text, speech, images, video, and even sensor data, in the same way as humans do. In this article, we’ve shared real-world examples and applications of multimodal AI to help you understand how it’s being used across industries.

Application: Multimodal AI in Customer Service and CX 

Multimodal AI is reshaping customer service by allowing AI agents to understand text, voice, images, video, and emotions, all simultaneously, and resolve queries with human-like empathy.    

Multimodal AI Example 1: Crescendo.ai Revolutionizing Customer Support with Multimodality

Crescendo.ai is a pioneer in applying multimodal AI to CX and customer service. Here’s how it works in action.

1. Seamless Support: Crescendo.ai’s multimodal AI lets customers move effortlessly between text, audio, and email within the same conversation. 

For example, a customer can start typing in chat, then tap the audio button to start explaining the query with voice messages (like on WhatsApp). While the chat continues, the AI can simultaneously email supporting documents or links for review, all without ending the session or losing context.

2. Smarter Visual Troubleshooting: Its AI agents can analyze invoices, screenshots, documents, forms, images, or videos that customers upload to identify issues instantly.

For example, it detects product defects, incorrect installations, or billing document mismatches using vision models.

3. AI Agents with Emotional Intelligence: Crescendo.ai’s advanced speech-to-text + NLP + LLM + sentiment analysis helps AI agents understand tone, emotions, and intent beyond words. It can detect frustration, disappointment, or urgency from the customer’s tone and adapts replies accordingly.

4. Agent Assist and Training: Crescendo.ai’s Expert Assist feature is based on multimodal principles where the AI listens to calls, analyzes chat context, reads attached documents and images, and recommends next best actions or templates to human agents. 

Application: Multimodal AI in the Healthcare Industry

Multimodal AI is making a huge impact in healthcare, transforming how doctors diagnose, treat, and monitor patients by combining data from multiple sources, text, images, audio, sensors, and more. Here’s how it’s being used.

1. Medical Imaging + Text Fusion for Diagnosis

Multimodal AI combines radiology images (X-rays, MRIs, CT scans) with clinical notes and lab results to improve diagnostic accuracy. AI doesn’t just “see” the image; it reads the doctor’s notes and correlates both.

2. Predictive Healthcare and Risk Scoring

By integrating EHR data, genomics, wearable sensor data, and lifestyle inputs, multimodal AI predicts the likelihood of conditions like cardiac arrest, diabetes, or cancer recurrence.

3. Drug Discovery and Precision Medicine

AI models combine molecular structures, chemical properties, and biomedical literature to identify new drug candidates faster.

4. Clinical Decision Support

Multimodal AI systems assist clinicians in real time, pulling together speech transcripts from patient visits, EHR data, and lab results to suggest next steps or highlight anomalies.

5. Patient Monitoring and Telemedicine

Combining video, voice, sensor, and biometric data allows for continuous, remote patient monitoring. It detects subtle signs of deterioration (breathing patterns, facial color changes, voice tone).

Multimodal AI Example 2: Google’s Med-PaLM M and Microsoft-Nuance use Multimodal AI in Healthcare

• Comprehensive Diagnosis with Multimodal Understanding: Med-PaLM M processes medical images (X-rays, CT, MRI) alongside clinical text, pathology reports, and patient history. It cross-references findings, e.g., spotting a lung lesion on an X-ray and linking it to the patient’s smoking history and lab results to suggest likely diagnoses with context.

• Real-Time Clinical Documentation and Decision Support: Microsoft-Nuance Dragon Medical One uses speech + text + EHR data fusion. It listens to doctor-patient conversations, converts speech into structured clinical notes, pulls matching patient data

• Enhancing Patient Experience and Reducing Physician Burnout: By combining speech recognition, natural language processing, and contextual EHR data, Microsoft-Nuance’s Dragon Medical One automatically generates clinical documentation during consultations. This multimodal approach lets doctors focus on patient interaction instead of manual note-taking, improving accuracy and saving time across hospitals and clinics.

Multimodal AI Example 3: AliveCor’s Multimodality in Diagnosis

• Multimodal ECG + clinical data integration: AliveCor devices (like Kardia) capture ECG sensor signals and combine them with contextual patient data (symptoms, prior history) to provide more accurate arrhythmia or cardiac risk predictions. 

• Enabling decentralized, patient-centric monitoring: The system transmits ECG data (sensor modality) and other metadata to clinicians or research platforms (e.g., Clinical StudyPal), supporting remote diagnosis, continuous monitoring, and trial enrollment outside hospitals. 

Application: Multimodality in Education and Learning

Multimodal AI is transforming education and learning by combining text, audio, video, handwriting, gestures, and facial expressions to create more interactive, personalized, and effective learning experiences. Here’s how it’s used in practice 

1. Personalized Learning Experiences

Multimodal AI systems analyze speech, text, handwriting, and engagement cues (like facial expressions or eye movement) to adapt content in real time. For example, a student struggling with math word problems may get a visual explanation (diagram/video) instead of more text-based problems.

Platforms like Knewton and Coursera AI adjust learning paths based on student responses, tone, and attention span.

2. AI Tutors and Virtual Class Assistants

Multimodal AI tutors understand voice, gestures, and whiteboard drawings, enabling natural teacher-student interaction. For example, a student can show their rough geometry sketch, and the AI analyzes it visually to correct mistakes.

Khanmigo (by Khan Academy) and Google’s Gemini models allow students to ask questions verbally, upload handwritten work, or share diagrams, and the AI interprets and explains concepts in multiple formats.

3. Enhanced Assessment 

Multimodal AI tools grade assignments by combining textual answers, handwriting recognition, voice responses, and video submissions. For example, AI analyzes the oral presentations for clarity, confidence, and eye contact, not just spoken content.

Systems like Gradescope and Turnitin’s AI tools evaluate not just the correctness but also presentation, tone, and engagement.

4. Special Education and Accessibility

For neurodiverse learners or students with disabilities, AI combines speech recognition, visual captioning, and gesture tracking to make content accessible. Tools like Microsoft Reading Progress and Seeing AI offer multimodal reading support, which includes reading text aloud, translating, and explaining visuals simultaneously.

5. Language Learning and Pronunciation Coaching

Language learning platforms use audio, video, and text inputs to teach pronunciation, grammar, and context more naturally. For example, the AI corrects tone and mouth movement using visual + audio cues.

Multimodal AI Example 4: Khanmigo’s Use of Multimodality to Improve Students’ Academic Performance

Here are real-life applications of Khanmigo (by Khan Academy) showing how it uses multimodal/AI-augmented techniques in academics.

1. AI Writing Coach with in-depth feedback: Khanmigo guides students through outlining, drafting, and revising essays, giving feedback on coherence, evidence, tone, and organization (not just grammar).

1. AI to improve maths via Socratic prompting: Instead of giving the answers to math questions, Khanmigo uses AI to ask leading questions, offer hints, and gradually help students uncover solutions, adapting based on their responses.

1. Teacher assistance in lesson planning and assessments: To help teachers, Khanmigo’s AI can generate lesson plans, exit tickets, rubrics, discussion prompts, and classroom activities aligned to curriculum standards, reducing prep time. 

Example 5: Duolingo’s Multimodal Approach for Language Coaching

Duolingo’s AI uses multimodal learning by combining text, speech, and visual cues to teach pronunciation, grammar, and context in real time. It listens to users’ voices, analyzes tone and accuracy, and adjusts lessons dynamically using speech recognition and visual feedback.

Application: Multimodal AI in Manufacturing Industry

Multimodal AI is increasingly used in the manufacturing industry, and it’s transforming everything from quality control to predictive maintenance and worker safety.

1. Multimodal inspection systems on assembly lines

Multimodal AI combines computer vision, sound analysis, and sensor data to identify production defects in real time.

• Cameras capture visual flaws (scratches, dents, misalignments).
• Acoustic sensors detect abnormal sounds in machinery.
• AI correlates both to determine whether a part is faulty or needs rework.

2. Predictive Maintenance

Instead of relying on just one signal (like vibration data), multimodal AI fuses:

• Thermal images
• Vibration & acoustic data
• Machine logs and operator notes

It predicts potential equipment failures before they happen, saving downtime and maintenance costs.

3. Worker Safety and Training

Wearables, cameras, and audio sensors work together with multimodality to detect unsafe worker behavior or environmental hazards.

• Detects when workers enter restricted areas.
• Identifies fatigue, improper posture, or lack of safety gear.
• Provides real-time alerts or training feedback.

4. Supply Chain & Inventory Optimization

Combining textual data (inventory logs), visual data (warehouse cameras), and sensor data (RFID, IoT) helps optimize logistics, packaging, and storage, predicting demand surges and bottlenecks.

5. Human-AI Collaboration on Factory Floor

Multimodal AI agents can understand voice commands, gestures, or images from engineers to perform tasks or diagnose issues, making human-machine interaction natural and efficient.

Multimodal AI Example 6: Siemens’s Multimodal / AI Fusion in Manufacturing

While Siemens doesn’t explicitly position itself as a multimodal AI company, its AI Suite and Industrial Copilot clearly demonstrate robust multimodal capabilities.

• AI Copilot for Engineers: Siemens’ Industrial Copilot uses multimodal AI (text, code, and visual data) to let engineers describe problems in natural language and instantly receive code snippets, machine settings, or design corrections, reducing downtime and setup time.
  
  
• Smarter Factory Floors: By fusing video feeds, sensor data, and control system logs, Siemens AI detects production bottlenecks, equipment anomalies, or worker safety risks in real time, helping supervisors make data-driven adjustments.
  
  
• Predictive Maintenance: Siemens integrates vibration, temperature, and sound data from machines into AI models that predict mechanical failures before they occur, minimizing unplanned downtime.
  
  
• Quality Control Automation: Cameras and edge AI analyze product images alongside machine parameters to detect micro-defects that human inspectors might miss, improving accuracy and yield rates.
  
  
• Digital Twin Optimization: Siemens’ Xcelerator platform combines CAD designs, IoT sensor data, and historical production logs to simulate and optimize factory operations, enabling continuous improvement across multimodal data sources.

Wrapping up,

Thanks to advanced models like GPT-4, Gemini, and others, AI is now breaking data silos and blending these inputs to make smarter, more natural decisions. From diagnosing diseases and guiding factory robots to powering intelligent chat support and personalized learning, multimodal AI is reshaping how industries work, communicate, and innovate.

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