From automating diagnostics to curating massive datasets, computer vision in healthcare is emerging as a core enabler of smarter, faster, and more equitable care. As diagnostic imaging, video, and patient monitoring data continue to grow exponentially, the ability to extract actionable insight from visual information is becoming a strategic imperative across the healthcare industry.
In this post, we showcase 12 real-world case studies drawn from our recent webinar series, “Visual AI in Healthcare,” led by leading researchers and practitioners. Each one highlights a practical application of computer vision — from research labs to clinical deployments — that’s shaping the future of medicine. Whether you're building medical AI tools, leading imaging research, or deploying clinical workflows, these case studies offer a front-row seat to where visual AI is delivering impact today.
1. Vision-based behavior monitoring in autism care: solving the wearable challenge
Speaker: Somaieh Amraee - Northeastern University
The clinical challenge:
Traditional behavior monitoring for autism spectrum disorder (ASD) relies heavily on wearable biosensors, but up to 10% of patients reject these devices due to sensory sensitivity, leaving caregivers without critical data for intervention planning.
The visual AI solution:
Researchers at Northeastern University developed a
computer-vision-driven behavior analysis system that uses RGB cameras and multi-object tracking (MOT) to monitor aggressive or self-injurious behaviors without requiring any wearable devices.
How vision-based monitoring system works:
- Multi-object tracking (MOT) to simultaneously follow patients and staff
- Pose estimation to extract skeletal keypoints to detect non-standard postures like crawling or curling
- Action recognition to identify dysregulated behaviors
- Behavior classification models trained specifically on ASD patterns
Key insights:
Standard MOT algorithms like ByteTrack and DeepSORT significantly underperform in clinical environments compared to standard benchmarks. The challenges are uniquely medical, such as staff in uniforms, frequent occlusion, and non-standard patient poses. Custom clinical datasets are critical to building robust models that perform in the real environment.
Vision-based behavior monitoring can fill a critical gap in autism care technology, enabling continuous monitoring for previously excluded patient populations. This requires prioritizing clinical-grade data collection and evaluation methods that reflect the complexities of real-world care environments.
Watch the full webinar.2. PRISM: Counterfactual medial imaging, explaining AI for medical diagnosis
Speaker: Amar Kumar - McGill University
The clinical challenge:
Radiologists increasingly rely on AI as a medical diagnosis tool, but black-box models provide little insight into their reasoning, especially when medical devices or imaging artifacts cloud the image.
The visual AI solution:
PRISM, developed at McGill University in collaboration with
Google Research, leverages Stable Diffusion 1.5 to generate counterfactual medical images that visually explain AI decisions.
Clinical applications
- Artifact removal: Automatically clean chest X-rays by removing pacemakers, wires, and other artifacts
- Counterfactual image generation: Visualize the healthy version of a patient image, providing radiologists with visual evidence of model focus areas
- Synthetic data augmentation: Create synthetic training examples that improve classifier performance by 10%
Counterfactual imaging transforms AI from a black box to a transparent tool, enabling radiologists to validate model reasoning and identify potential failures before they reach patients.
Watch the full webinar.3. Building your medical digital twin: Real-world evaluation of medical LLMs
Speaker: Ekaterina Kondrateva - Maastricht University
The clinical question:
LLMs are increasingly used for clinical triage and decision support, but their reliability varies dramatically based on input format, prompt design, and demographic factors. Kondrateva conducted a live experiment using her blood work to find how reliable LLMs are when applied to real medical data.
Critical findings:
Testing GPT-4, Claude, and DeepSeek on real patient symptoms and laboratory data revealed significant gaps:
- Cost variability: Recommendations for follow-up testing varied wildly per model, ranging from $100-$500
- Bias amplification: Including demographic data led to systematically different diagnostic pathways
- Context-seeking remains broken: Even advanced models fail at follow-up questions
- Confidence calibration varies: Models with higher confidence scores generally produce more accurate diagnoses, but this relationship isn't consistent across model families
Key insights:
- GPT-4 O3 leads comprehensive medical benchmarks across 35 different test categories
- Data format matters: Converting lab results to structured JSON reduced hallucinations significantly
- Prompt engineering impact: Chain-of-thought reasoning and context-seeking prompts improved safety measures
Kondrateva’s experiment demonstrates that LLMs require human oversight and cannot function as standalone diagnostic tools in clinical settings yet. However, they can serve as a useful triage aid or second opinion when deployed with appropriate safeguards.
Model success depends heavily on rigorous data preparation and sophisticated prompt design, making it essential for healthcare organizations to develop deep expertise in data quality management, input standardization protocols, and model validation frameworks before clinical deployment.
Watch the full webinar.4. Medical imaging models: Benchmarking MedGemma, VISTA-3D, and MedSAM-2
Speaker: Dan Gural - Voxel51
The clinical challenge:
With the proliferation of vision foundation models, healthcare developers face a growing need to assess which models perform best for specific tasks (e.g., segmentation, classification, visual QA) across different imaging modalities.
The comparative analysis:
Gural presented a comprehensive demo evaluating three leading medical imaging models across clinical workflows, using
FiftyOne to systematically compare model outputs, visualize failure cases, and assess performance consistency.
- Google’s MedGemma excelled at multimodal visual question answering but struggled with precise spatial tasks
- NVIDIA’s VISTA-3D proved most effective for large-scale 3D organ segmentation workflows
- Meta’s MedSAM-2 demonstrated superior performance in promptable segmentation and label propagation tasks
Key insights:
Model selection should prioritize task-specific performance over general capabilities. Multimodal models excel at interpretation tasks while specialized architectures deliver superior results for structured spatial analysis, making targeted deployment more effective than one-size-fits-all approaches.
Watch the full webinar.5. Dataset curation at scale: Automating medical imaging workflows
Speaker: Brandon Konkel, PhD - Booz Allen Hamilton
The clinical challenge:
Creating high-quality medical imaging datasets for AI development typically requires weeks of manual work from radiologists and data engineers, creating bottlenecks in research and model development cycles.
The visual AI solution:
Booz Allen Hamilton developed a multimodal pipeline that automatically identifies relevant scans, extracts clinical context, and flags quality issues across the hospital picture archiving and communication system (PACS). This automation transformed what was previously a weeks-long manual curation process into an automated workflow that completes in just minutes.
The technical stack integrates:
- BioMedCLIP multimodal AI model for image-text embedding and protocol classification
- RadLLama for radiology report parsing and patient assessment
- TotalSegmentator for automated segmentation, artifact detection, and 3D version generation
All outputs were embedded back into DICOM metadata and made searchable using
FiftyOne’s visual interface, while maintaining regulatory traceability, essential for both research and compliance.
Key insights:
Automated curation reduces clinical burden while improving dataset consistency. Organizations should prioritize tools that integrate with existing PACS infrastructure and regulatory frameworks.
Watch the full webinar.6. Real-time cardiac assessment on mobile devices: Democratizing cardiac care
Speaker: Jeffrey Gao - Caltech
The clinical challenge:
Heart failure affects millions globally, yet routine echocardiograms remain inaccessible due to cost, time constraints, and sonographer shortages, leading to delayed diagnoses and preventable hospitalizations.
The technical breakthrough:
Caltech researchers developed a deep learning system that estimates right atrial pressure from handheld ultrasound devices. The model identifies the inferior vena cava (IVC), guides proper scan acquisition, and calculates pressure while running entirely on consumer iPads in real-time.
The solution combines:
- Largest RAP dataset ever: 16,000+ labeled examples from 45+ cardiologists
- Dual-model architecture: X3D for IVC detection, SlowFast for pressure estimation
- iOS-native deployment: CoreML and Metal shaders for edge inference
- Real-time guidance: Interactive feedback for proper probe positioning
Key insights:
Edge deployment using consumer hardware (iPad + $4,000 probe) makes sophisticated cardiac screening accessible in urgent care settings, dramatically expanding the scope of point-of-care diagnostics.
Watch the full webinar.7. Continuous visual monitoring: Transforming inpatient care
Speaker: Paolo Gabriel, PhD - LookDeep Health
The clinical challenge:
Hospital patient monitoring faces a fundamental gap: traditional monitoring through nurse check-ins or call buttons can be intermittent and reactive. Nurses spend only 37% of their shift in direct patient care and physicians average just 10 visits per hospital stay, leaving patients unmonitored for the majority of their admission. This creates vulnerability windows where critical events like falls, rapid clinical deterioration, and adverse reactions can occur without immediate detection.
The visual AI solution:
LookDeep Health deployed
continuous visual monitoring to track patient activity 24/7 across 11 hospital systems for almost 3 years, processing over 30,000 hours of video monthly.
The system features:
- YOLOv4 object detection running at 1 FPS on embedded devices
- Hybrid deployment: Edge interface with cloud analytics
- Privacy-first design: On-device processing with automated blurring
- Continuous learning: Every fourth week of data collection is held out as a test set for comparing model performance over time
- Human-in-the-loop workflows: Seamless integration of expert labeling by using FiftyOne to curate data sent for labeling
Key insights:
Continuous monitoring enables proactive care coordination with minimal additional infrastructure requirements. Successful clinical deployment requires robust MLOps practices, including continuous test set evolution and metadata tagging for model reliability across diverse hospital environments.
Watch the full webinar.8. Auto-RECIST development: AI-enabled oncology
Speaker: Asba Tasneem, PhD
The clinical challenge:
RECIST (Response Evaluation Criteria in Solid Tumors) is a standardized protocol for evaluating cancer treatment effectiveness. However, the manual measurement process is labor-intensive and variable across radiologists, creating a bottleneck in cancer drug development.
The visual AI solution:
A comprehensive Auto-RECIST development program demonstrates the complete lifecycle of an AI-enabled medical device: from automated detection, tracking, to measurement of tumors.
The development process included:
- Gold-standard dataset: Built with expert annotations of 6,000+ patient studies
- Multi-phase validation: Training, testing, and regulatory QA with blinded datasets
- Post-deployment monitoring: Continuous performance tracking and data drift detection
Key insights:
Clinical AI deployment requires fundamentally different validation approaches than academic research, with emphasis on regulatory compliance, data drift monitoring, and long-term performance tracking. Organizations should plan for post-deployment monitoring as a core system requirement, not an afterthought.
Watch the full webinar.9. Foundation models in pathology: Navigating benefits and biases
Speaker: Heather (Dunlop) Couture - PixelScientia
The clinical challenge:
Pathology datasets are massive yet weakly labeled, making training from scratch slow and expensive. Meanwhile, off-the-shelf embeddings often introduce site-specific biases that limit generalizability.
The visual AI solution:
Comprehensive evaluation of how DINOv2, CLIP, and other foundation models are being used for histopathology. While these models learn rich representations from unlabeled tiles and can be fine-tuned, there are some hidden biases to look out for.
- Domain specificity matters: Models pre-trained on histology significantly outperform ImageNet-based approaches
- Site bias persistence: All foundation models encode hospital-specific features that enable shortcut learning
- Stain normalization limitations: Traditional preprocessing doesn't eliminate site-specific artifacts
- Medical center robustness index: New metrics needed to evaluate cross-site generalization
Key insights:
Foundation models enable faster prototyping and better tile embeddings, but still require rigorous validation. Pathology labs must prioritize bias auditing and multi-site validation alongside traditional performance metrics to successfully deploy models that generalize across different hospitals, scanners, and patient populations without compromising diagnostic accuracy.
Watch the full webinar.10. MedVAE: High-fidelity compression for medical images
Speakers: Aswin Kumar & Maya Varma - Stanford
The clinical challenge:
High-resolution medical imaging — especially 3D modalities like MRI and CT — creates storage and computational bottlenecks that limit AI development, particularly for resource-constrained hospitals and academic research centers.
The visual AI solution:
MedVAE is a family of six generalizable 2D and 3D variational autoencoders that downsizes high-dimensional medical images, reducing storage by up to 512x and accelerating downstream tasks by up to 70x while preserving clinically relevant features. MedVAE was trained on over one million images from 19 open-source medical imaging datasets using a novel two-stage training strategy.
Strategic implications: Carefully designed medical image compression can preserve diagnostic information while enabling AI development in resource-constrained environments. Medical image compression is not just a technical optimization, but a critical factor in making healthcare accessible.
Watch the full webinar.
11. LesionLocator: Zero-shot universal tumor segmentation and tracking
Speaker: Maximilian Rokuss - DKFZ German Cancer Research Center
The clinical challenge:
Tumor tracking across time series represents one of the most challenging problems in medical imaging. Current clinical practice, following the RECIST criteria (Response Evaluation Criteria in Solid Tumors), only tracks 5 target lesions maximum, missing the complete disease picture.
The visual AI solution:
LesionLocator from DKFZ combines promptable 3D segmentation with longitudinal tracking, enabling radiologists to follow lesions across entire treatment cycles with point-and-click simplicity. This is the first system to achieve near-human performance in 3D tumor segmentation and tracking.
The technical framework includes:
- 3D U-Net architecture trained on 50+ public datasets
- Synthetic longitudinal data for robust temporal modeling
- Multimodal support: CT, MRI, and PET imaging compatibility
- Joint segmentation + tracking training for consistent lesion identity
- Multi-prompt support: Points, boxes, and mask propagation
Performance milestones:
- Near-human accuracy: Performance approaching human inter-rater variability on multiple tumor types, 5-10 dice point improvement over SAM and MedSAM
- Tracking consistency: Accuracy maintained across multiple follow-up time points
- Real-world validation: 5-fold cross-validation on real patient data
Key insights:
Joint training of segmentation and tracking models produces more robust results than sequential processing, while synthetic longitudinal data enables training where real temporal datasets are unavailable.
Watch the full webinar.12. LLMs for smarter diagnosis: Benchmarking real-world performance
Speaker: Gaurav K Gupta - Lake County Health Department
The clinical challenge:
With healthcare providers increasingly using LLMs for clinical support, understanding their diagnostic accuracy across different conditions and model types is crucial for safe implementation. Gupta's systematic evaluation provides essential benchmarking data for technical teams.
The Benchmarking Study:
Systematic evaluation of leading LLMs (GPT-4, Claude, DeepSeek) across 100+ symptom-based scenarios, measuring diagnostic accuracy, confidence calibration, and bias patterns.
Performance and implementation insights:
- DeepSeek R1 outperformed GPT-4 O3 Mini on chronic diseases (88% vs 78% category-level accuracy) despite requiring significantly fewer computational resources
- GPT-4 exceeded the newer O1 Preview model across all metrics, challenging assumptions about model progression
- Respiratory diseases proved challenging for all models due to overlapping symptom presentations
- Prompt engineering impact: Structured inputs with chain-of-thought reasoning significantly improved safety
- Confidence calibration: Models with higher confidence scores didn't necessarily provide more accurate diagnoses
- Demographic bias: Including patient demographics led to systematically different recommendations
Key insights:
Healthcare organizations should implement LLMs as augmentation tools with robust safety frameworks rather than autonomous diagnostic systems. While bias and hallucinations remain challenges, LLMs show strong potential in medical diagnostics and triage. Prompt engineering and bias auditing are as critical as model selection.
Watch the full webinar.The future of computer vision in healthcare
These 12 case studies illustrate that computer vision has moved beyond proof-of-concept demonstrations to become an operational infrastructure across healthcare settings. The technology is enabling new care models, accelerating research pipelines, and democratizing access to sophisticated diagnostic capabilities.
The question isn't whether to adopt computer vision in healthcare, but how efficiently your organization can build the technical capabilities, operational frameworks, and clinical partnerships needed to deploy these technologies safely and quickly.
Success in computer vision in healthcare depends fundamentally on data quality and model understanding. Building robust models that perform reliably in clinical environments requires deep insight into failure modes and performance patterns. With
FiftyOne, healthcare teams can visualize exactly what their models detect, identify systematic failure patterns across different patient populations and imaging conditions, and iterate based on concrete performance data rather than abstract metrics. This data-centric approach eliminates guesswork, surfaces actionable insights about model behavior, and enables teams to confidently develop and deploy production-ready visual AI systems that meet the rigorous standards required for clinical care.
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