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In recent years, research on the application of these methods in the fields of plaque diagnosis, stability assessment, and symptomatic plaque identification has increased significantly. Although these advancements have significantly improved the diagnosis of carotid plaques, variations in data dependency and imaging configurations among different models create inconsistencies in diagnostic accuracy.
J Med Internet Res 2026;28:e77092
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In this context, some studies have used DL for the automatic diagnosis of COPD, such as DL-based chest X-ray (CXR), for the classification of COPD [10] and DL-based cough sound signal analysis [11]. Nevertheless, systematic evidence of the actual performance and comparative advantages of different DL frameworks in the diagnosis of COPD is lacking.
Therefore, we conducted a systematic review and meta-analysis of diagnostic test accuracy studies on DL models for COPD.
J Med Internet Res 2026;28:e83459
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AI-Supported Digital Microscopy Diagnostics in Primary Health Care Laboratories: Scoping Review
English
Non-English
Published peer-reviewed studies
Diagnostic test accuracy studies
Non–peer reviewed studies
Not diagnostic test accuracy studies
Humans
Studies performed on animals
AIa techniques applied as a diagnostic tool on microscopy
Final slide-level diagnosis was performed and compared with a standard microscopist
Outcome valuable for clinicians
Studies that applied AI models on images not conventionally analyzed in microscopy
No final slide diagnosis
Performed at primary health care laboratory (tier
J Med Internet Res 2026;28:e78500
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Feasibility based on image use will be evaluated as the percentage of images suitable for diagnosis out of the total number of OCT images obtained. A percentage greater than 80% is regarded as feasible.
JMIR Res Protoc 2025;14:e76644
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Mammograms are a critical tool in breast cancer diagnosis; however, interpreting them is inherently challenging. Expertise is acquired only after lengthy training; however, there is a shortage of seasoned senior radiologists [1] due to workforce aging and rising demand for breast cancer screening and diagnosis. The scarcity of skilled professionals in this field is particularly critical in health care systems that increasingly prioritize health screening and primary prevention.
JMIR Form Res 2025;9:e66931
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Teledermatology to Support Self-Care in Chronic Spontaneous Urticaria
Given the rich structural and topological elements associated with symptoms (eg, hives, itch), there is natural interest in the use of imaging technologies coupled with computational tools to improve diagnosis [7]. In one recent teledermatological study involving over 16,000 cases, a deep learning system was deployed on photographic images and demonstrated high diagnostic accuracy [8].
JMIR Dermatol 2025;8:e81830
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Trauma-related diagnostic overshadowing for these likelihood ratings was defined as lower ratings for the target diagnosis (ie, OCD or SUD) or higher ratings for a PTSD diagnosis when trauma exposure was present versus absent. Respondents were then asked to select the primary diagnosis they would assign from the list of diagnoses.
JMIR Ment Health 2025;12:e80801
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Among these devices is “DIANNA” (diagnosis and anamnesis), which has undergone substantial improvements since its initial development. A previous randomized controlled trial provided early insights into the tool’s impact on diagnostic accuracy and efficiency. Building on this foundation, DIANNA now includes a body pictogram feature to better select symptomatic areas, making it more intuitive and comprehensive for clinicians [4,5].
JMIR Form Res 2025;9:e56384
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Large Language Models in Lung Cancer: Systematic Review
In recent years, integrated full-cycle management—covering prevention, screening, diagnosis, treatment, and supportive care—has been promoted to improve both survival and quality of life [7,8]. However, this approach requires complex workflows and large-scale data processing, placing heavy demands on medical resources and personnel.
Artificial intelligence, particularly large language models (LLMs), offers a potential solution.
J Med Internet Res 2025;27:e74177
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Diagnostic and Screening AI Tools in Brazil’s Resource-Limited Settings: Systematic Review
RQ1: Is the tool used for diagnosis or screening?
This question helps clarify the primary objective of each intervention. This distinction is essential, as screening focuses on maximizing sensitivity to ensure few actual cases are missed, while diagnosis aims to confirm or rule out a condition in individuals already identified as at risk, requiring a balance between sensitivity and specificity.
RQ2: What is the context and location of the tool’s application?
JMIR AI 2025;4:e69547
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