AIRead: Multimodal AI for Imaging & Assistive Tech

Updated 7 December 2025

AIRead is a multimodal AI framework that integrates convolutional and transformer-based models to extract and interpret data from images, text, and scenes.
It pairs specialized image encoders with large language models to generate clinical reports with high diagnostic accuracy and minimal hallucination rates.
Beyond clinical applications, AIRead enables assistive technologies for the visually impaired and supports data readiness assessments for advanced AI tasks.

AIRead refers to a set of technologies and models at the intersection of artificial intelligence and the automated interpretation or extraction of meaning from complex data—especially medical images and multimodal inputs. AIRead systems utilize large-scale deep learning architectures to process, analyze, and synthesize human-interpretable outputs from sources such as chest radiographs, natural scenes, and textual data. Critical deployments include clinical report generation, assistive technologies for visually impaired users, and evaluation of data readiness for downstream AI applications.

1. Model Architectures and Core Components

AIRead systems are generally characterized by multimodal stacking of specialized modules, often combining deep convolutional or transformer-based image encoders with pretrained LLMs. A canonical instance is the AIRead medical image model, which comprises two principal components: (1) an image encoder that localizes potential abnormalities and assigns up to 61 predefined chest-radiograph findings, and (2) a LLM consuming encoder outputs to generate a free-text report. The total parameter count in this architecture is approximately 2.6 billion (version v0.2.5) (Lim et al., 29 Nov 2025).

In nonclinical contexts, the AIRead umbrella includes real-time assistive systems for visually impaired users. For example, an AIRead-capable assistant leverages a YOLOv8 object detector, with anchor-free architecture and support for 80+ classes, combined with a Large Language and Vision Assistant (LLaVA). YOLOv8 is fine-tuned to support additional classes relevant to visually impaired users (e.g., "door") and provides bounding-box localization at low latency. LLaVA serves both as an OCR module (via fixed prompt templates and a CLIP-derived ViT encoder) and as a visual question answering (VQA) system for scene description and interactive Q&A (Marquez-Carpintero et al., 8 Nov 2025).

2. Training Regimes, Data, and Deployment

AIRead models are trained on extensive, domain-specific datasets. The medical AIRead system utilizes approximately 14 million CXR–report pairs curated from 11 tertiary hospitals in Korea and multiple sites in the U.S., ensuring model exposure to a diverse array of pathology presentations and reporting styles. Of these, 8 million pairs train the image encoder, while the remainder refine the LLM head. All reference reports are radiologist-written to promote clinically accurate natural language synthesis (Lim et al., 29 Nov 2025).

Deployment architecture typically features hardware acceleration for inference (e.g., NVIDIA H100 for medical models, GTX 1080Ti/A40 for YOLOv8/LLaVA) and adopts a client-server split in assistive scenarios. On-device front ends (Ionic/Capacitor/Vue for Android/iOS) handle UI, image capture, and communication with cloud-hosted inference servers. Functional pipelines typically include:

Mode selection (Object Finder, OCR, Scene Description/VQA)
Image capture and streaming to backend
Model-specific inference (object detection, text transcription, or scene QA)
Return of actionable, multimodal feedback (audio cues, haptic signals, minimal graphics for low-vision users) (Marquez-Carpintero et al., 8 Nov 2025)

3. Evaluation Metrics and Empirical Performance

The performance of AIRead systems is evaluated using both task-specific metrics and user-centric acceptability studies. In medical imaging, the following criteria are central:

RADPEER scoring system: Quantifies interpretive disagreement, with the primary focus on clinically significant misses (3b). AIRead achieves 5.3% RADPEER 3b (vs. radiologists 13.9%; P<.001).
Clinical acceptability: Proportion of reports deemed acceptable for clinical use (standard: 84.5% AIRead vs. 74.3% radiologists; P<.001).
Hallucination rate: Frequency of statements not supportable from the image. AIRead 0.3% (statistically equivalent to radiologists).
Language clarity: Reports rated as “clear” or “excellent” in 82.9% of cases, outperforming radiologists (78.1%; P=.001).
Finding-level diagnostic performance: Sensitivity for critical findings ranges from 15.5% (emphysema) to 86.7% (cardiomegaly), with specific metrics for each pathology extracted via a BERT-based labeler using CT as reference (Lim et al., 29 Nov 2025).

Assistive deployments evaluate usability via Likert-scale questionnaires (Technology Acceptance Model), reporting averages between “Excellent” and “Best” on intuitiveness and autonomous use. Latency for scene QA and OCR is typically under 10 seconds end-to-end, while object finding operates near real time (~0.5 seconds response) (Marquez-Carpintero et al., 8 Nov 2025).

4. Comparative Analysis with Other AI Systems

Clinical benchmarks position AIRead as outperforming peer and baseline models across most criteria in the generation of chest radiograph reports. Compared models include Lingshu, MAIRA-2, MedGemma, and MedVersa. AIRead attains significantly lower interpretive miss rates, higher clinical acceptability, and minimal hallucination rates. For example, Lingshu's RADPEER 3b rate is 43.0% versus AIRead's 5.3%. Clinical acceptability (standard) is 41.1% for Lingshu, 84.5% for AIRead. Hallucinations are substantially more common in other VLMs, e.g., 11.0% in Lingshu vs. 0.3% in AIRead. These findings highlight AIRead’s relative robustness in both diagnostic fidelity and linguistic quality (Lim et al., 29 Nov 2025).

5. Applications and Impact Domains

AIRead technologies support a spectrum of applications:

Medical imaging: Automated or “second-reader” report drafting for CXR interpretation in emergency or high-volume settings, with low hallucination risk supporting trust and integration in clinical workflows. Performance is especially strong for common acute findings (e.g., lung opacity, pleural effusion, cardiomegaly) (Lim et al., 29 Nov 2025).
Assistive tools for the visually impaired: Real-time object localization, reading of packaging and signage (via OCR), scene summarization, and interactive Q&A. The incorporation of dynamic audio and haptic feedback, as well as multilingual support, fosters autonomy in daily living environments (Marquez-Carpintero et al., 8 Nov 2025).
Data readiness assessment ("AIReadiness"): Frameworks such as AIDRIN operationalize “readiness” via quantitative analysis on completeness, outlier rates, duplication, feature importance, imbalance, fairness, privacy (MM-risk), and FAIR principle compliance. Statistically rigorous metrics (e.g., IQR-based outlier detection, Theil's U for categorical correlations, Shapley values for feature importance) provide a foundation for automated data triage prior to downstream machine learning tasks (Hiniduma et al., 27 Jun 2024).

6. Limitations and Future Directions

Limitations depend on deployment domain. In clinical models, generalizability can be constrained by training data (e.g., predominance of concise Korean reports), exclusion of prior clinical metadata, and lack of evaluation on downstream outcomes and human–AI collaboration (Lim et al., 29 Nov 2025). Assistive systems face challenges with illumination, occlusion, and seamless navigation, as well as scalability with continuous connectivity needs (Marquez-Carpintero et al., 8 Nov 2025).

Planned enhancements include fine-tuning object detectors for rare classes and adverse conditions, onboard speech and dialogue management, spatial navigation, and integration with smart-home environments. In data-readiness, future work focuses on extending metrics beyond tabular data, incorporating structural and governance checks, and automating recommended interventions (Hiniduma et al., 27 Jun 2024).

AIRead encapsulates a broad technological paradigm uniting deep neural architectures and evaluation methodology for robust, automated interpretation of data spanning medical images, natural scenes, and structured tabular input. The empirical evidence supports its efficacy in both expert augmentation and assistive technology, though responsible deployment necessitates domain-specific vigilance with respect to data, model outputs, and user needs.