Evaluating the Accuracy of Artificial Intelligence Tools in Analyzing X-Ray Images for Detecting Respiratory Diseases: A Comparative Study of Recent Research

Q: What does this research mean for the field?

Deep learning models achieve high accuracy (93-98%) in detecting respiratory diseases from chest X-rays, but their clinical reliability and generalizability are currently hindered by methodological challenges such as data imbalance, lack of external validation, and reliance on single-source data. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

Milad Arir¹; Masarra Saleh Mohammed Al-Shaafi¹; Amal Ahmed Youssef Al-Ammari¹; Intisar Ammar Rahouma Issa¹

doi:10.53796/hnsj78/7

August 1, 2026Humanitarian and Natural Sciences JournalOpen Access

Evaluating the Accuracy of Artificial Intelligence Tools in Analyzing X-Ray Images for Detecting Respiratory Diseases: A Comparative Study of Recent Research

View Full Paper

Ask AI

Bookmark

Share

Key Points

This analysis aims to compare the effectiveness of deep learning models in diagnosing respiratory diseases using chest X-ray images.
Compared five recent studies employing deep learning techniques on X-ray images.
Evaluated models based on training strategies, data quality, and performance metrics.
Included assessment of accuracy, sensitivity, specificity, and ROC curve metrics.
Deep learning models demonstrated accuracy ranging from 93% to 98% based on disease type and data used.
Transfer learning significantly enhanced performance, especially with limited training data.
Models trained on multi-source data showed more stable performance compared to single-source models.

Ask about this paper

Answers are grounded in this paper and cite their sources.

Implication

Comparative study evaluates AI model performance on X-ray images for respiratory disease detection, highlighting implications for clinical applications.

Abstract

No abstract yet. The journal hasn't published one we can show.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Arir¹ et al. (2026) studied this question.

synapsesocial.com/papers/6a70400975942ff7265e4b93 https://doi.org/10.53796/hnsj78/7

View Full Paper

Ask AI

Bookmark

Share

Evaluating the Accuracy of Artificial Intelligence Tools in Analyzing X-Ray Images for Detecting Respiratory Diseases: A Comparative Study of Recent Research

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Evaluating the Accuracy of Artificial Intelligence Tools in Analyzing X-Ray Images for Detecting Respiratory Diseases: A Comparative Study of Recent Research

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Keep reading

Keep reading