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Skip search results from other journals and go to results- 45 Journal of Medical Internet Research
- 19 JMIR Formative Research
- 13 JMIR mHealth and uHealth
- 12 JMIR Medical Informatics
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Artificial intelligence (AI) presents a solution by automating and streamlining these processes, potentially augmenting both efficiency and accuracy. However, the adoption of AI in breast cancer screening is not without challenges. Although there are over 20 Food and Drug Administration (FDA)–approved AI applications for breast imaging, their adoption and utilization in clinical settings remain highly variable and generally low [6].
J Med Internet Res 2025;27:e62941
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The average accuracy for v3.5 was 68.4%, while v4.0 achieved an outstanding 87.2% accuracy rate. This indicates an absolute difference of 18.8% between the two versions and a relative improvement of 27.4% in accuracy in the latest platform version.
JMIR AI 2025;4:e66552
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Retrieval-augmented generation (RAG) is a state-of-the-art technique that enhances LLMs by integrating external data retrieval, improving factual accuracy, and reducing costs [13]. By retrieving relevant information from external sources and incorporating it as contextual input, RAG effectively mitigates the issue of hallucinations in LLMs [14].
J Med Internet Res 2025;27:e66098
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Accuracy for objective questions was calculated as the number of correctly answered questions divided by the total number of questions. For diagnosis and classification, accuracy was defined as the number of cases correctly diagnosed or triaged divided by the total number of cases. Specifically for open-ended questions, accuracy was determined based on the number of questions rated “good” or “accurate” on the accuracy scale divided by the total number of questions.
J Med Internet Res 2025;27:e64486
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Therefore, this study aims to comprehensively evaluate the performance and accuracy of LLMs in clinical diagnosis, providing references for their clinical application.
This systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) statement [7]. Specific details can be found in Checklist 1.
JMIR Med Inform 2025;13:e64963
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However, despite being one of the most favored informational modalities, websites often require more content accuracy and better readability [1].
Recently, artificial intelligence (AI)–powered chatbots such as Chat GPT have signified a potential paradigm shift in how patients with cancer can access a vast amount of medical information [1,3,4].
JMIR Cancer 2025;11:e63677
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Despite these advantages, EMA implementation faces challenges, especially in the variability, completeness, and accuracy of participant responses to prompts. Factors such as distraction, self-awareness, boredom, time of day, and interruption burden [11] can impact participant responses. Addressing these issues is essential for maintaining the integrity of research findings. Furthermore, the design of notification strategies may dramatically impact response compliance and quality [12,13].
JMIR Mhealth Uhealth 2025;13:e57018
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Therefore, a comprehensive evaluation of chatbots’ reliability and accuracy in addressing medical inquiries is essential to ensure their effective application in managing diseases like OMG [16].
Recent studies have explored the application of LLMs in ophthalmology. Jaskari et al [17] introduced a model named DR-GPT, designed to analyze fundus images, demonstrating that LLMs can be applied to unstructured medical report databases to aid in classifying diabetic retinopathy.
J Med Internet Res 2025;27:e67883
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Moreover, mean absolute error, mean absolute percentage error (MAPE), 5% accuracy (percentage of MAPE within a 5% range of the reference value), root-mean-squared error (RMSE), and ordinary least squares linear regression were used to evaluate accuracy.
JMIR Cardio 2025;9:e67110
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Notably, when trained on dataset #1, the SBERT-Doc SCAN algorithm emerges as the leading performer, securing an accuracy and F1-score of 0.8985 on the test dataset. This standout performance highlights the algorithm’s capability to classify medical domain data with high precision. Additionally, the SBERT-MEC algorithm also displays comparable performance on the same dataset, with an accuracy and F1-score of 0.8938, making it the second most effective algorithm in our evaluation.
JMIR Form Res 2025;9:e54803
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