e.g. mhealth
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Skip search results from other journals and go to results- 20 Journal of Medical Internet Research
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Evaluating the histopathological response after NACT is crucial for prognosis and guiding subsequent treatment decisions.
JMIR Res Protoc 2025;14:e56825
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Prognostic Disclosure in Metastatic Breast Cancer: Protocol for a Scoping Review
Patients who understand their prognosis tend to have more realistic expectations about treatment [4]; they are also better able to prepare for the future, have higher quality end-of-life (Eo L) care and have better chances of dying in their preferred place [5].
JMIR Res Protoc 2025;14:e57256
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comparative sufficiency of ChatGPT, Google Bard, and Bing AI in answering diagnosis, treatment, and prognosisprognosis
JMIR Dermatol 2025;8:e72540
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comparative sufficiency of ChatGPT, Google Bard, and Bing AI in answering diagnosis, treatment, and prognosisprognosis
JMIR Dermatol 2025;8:e71768
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These projects include the following:
Chameleon: a project focused on developing AI algorithms for cancer diagnosis and prognosis.
Eu Can Image: a project aimed at creating a large-scale cancer image database.
Pro CAncer-I: a project focused on developing AI-based tools for personalized cancer treatment.
Incisive: a project contributing a significant amount of cancer image data to EUCAIM.
Primage: a project focused on developing AI-based image analysis techniques for cancer diagnosis.
J Med Internet Res 2025;27:e66273
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Cardiac arrest (CA) remains a critical challenge in contemporary medicine, characterized by a dismally low survival rate and poor prognosis, and, therefore, has garnered global attention [1]. CA can be classified by the occurrence location into in-hospital CA (IHCA) and out-of-hospital CA (OHCA). Despite advancements in cardiopulmonary resuscitation techniques, global registry data indicate that the incidence and survival rates of CA have not significantly improved.
J Med Internet Res 2025;27:e67871
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Artificial Intelligence in Lymphoma Histopathology: Systematic Review
In this extensive study, we systematically reviewed the literature exploring the use of AI technologies, including traditional machine learning (ML) and deep learning (DL) methods, to assess digital pathology images for lymphoma diagnosis, prognosis, and other pertinent applications. Our review encompasses research that focuses on individual diagnostic factors, such as histological subtypes, as well as studies that perform computer-assisted tasks like tumor segmentation.
J Med Internet Res 2025;27:e62851
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We divided the included studies into 2 subgroups for independent evaluation based on different research objectives, namely, subgroup A: studies that aim at image classification or segmentation, assessed using an adapted version of QUADAS-2; and subgroup B: with the goal of predicting patient prognosis, assessed using PROBAST.
J Med Internet Res 2025;27:e60888
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