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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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How to Improve Pancreatic Cancer Network Care Using a Human-Centered Design Sprint
Regarding quality parameters of oncological care, multicenter care in Dutch pancreatic cancer patients is associated with repeated diagnostic investigations, delayed time-to-diagnosis, and delayed time-to-treatment [15]. These studies illustrate the complexity of pancreatic cancer network care and underline the necessity of close collaboration between the non-expert and expert centers.
J Med Internet Res 2025;27:e55598
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It would be advisable to include a comparative analysis to evaluate diagnosis accuracy and the prioritization of additional tests between physicians and LLMs.
Furthermore, the absence of actual data around patient history and other diagnostic parameters beyond what was reported in billing reports (reported as “ground truth” in the study) is a weakness. This can lead to an incomplete or partial diagnosis being labeled as the final diagnosis, leading to miscalculations about the accuracy of LLMs.
JMIRx Med 2025;6:e81235
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Given the recent progress in artificial intelligence (AI), large language models (LLMs) have been proposed to help with various aspects of clinical work, including diagnosis [7]. GPT-4, an LLM developed by Open AI, has shown promise in medical applications with its ability to pass medical board exams in multiple countries and languages [8-11].
JMIRx Med 2025;6:e67661
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