e.g. mhealth
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Skip search results from other journals and go to results- 27 Journal of Medical Internet Research
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Data from Chat GPT-4 and Menta LLa MA replies were copied to an Excel sheet, indicating the vignette number, gender, sexual orientation, and round number. Female gender and heterosexual orientation were coded as “0.” We performed all analyses in RStudio [64]. Data quality of Menta LLa MA results was low and yielded no reliable results (Multimedia Appendix 1).
JMIR Ment Health 2025;12:e57986
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Women-identifying and women+ gender faculty (women+ is inclusive of different identities pertaining to gender, hereto described as women+ faculty) in medicine and biomedical sciences face numerous barriers to career advancement. These barriers include fewer invitations to keynote speaking engagements, reduced academic promotion rates, decreased leadership opportunities, and disparities in research funding and other research metrics [1-4].
JMIR Form Res 2025;9:e65561
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Extracting comorbidities for an index condition based on age, gender, and other features required intensive computation power and storage capacity when dynamic linking the patient and clinical tables. The same challenge was seen for other data analyses.
J Med Internet Res 2025;27:e52385
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Independence between surveys was tested with the t test for continuous variables (eg, age), and with the chi-square test for gender, education, and health literacy. We performed linear regressions to analyze associations between the 4 periods and level of worry (model A), self-reported adherence (model B), and perceived adherence of others to restrictions (model C).
Interact J Med Res 2025;14:e55636
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Table 2 shows the number and percentage of participants reporting interest in specialties depending on gender.
The association between medical specialty choice and gender, presented as ORs with 95% CIs, is shown in Figure 1.
Number and percentage of participants reporting interest in specialties depending on gender.
The association between medical specialty choice and gender, presented as odds ratios with 95% CIs.
Interact J Med Res 2024;13:e60223
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This paucity of data is particularly apparent in the evaluation of gender differences in the clinical presentation and medical management of IHD in LMICs [8,9]. A promising direction is the use of electronic health records (EHRs) to analyze patient data to better inform clinical decision-making and assess adherence to IHD treatment guidelines using a gender lens.
JMIR Form Res 2024;8:e42774
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Use of ChatGPT to Explore Gender and Geographic Disparities in Scientific Peer Review
The names of the first and last authors along with the first author’s country of affiliation were collected, and the gender of both the first and last authors was determined. The authors’ genders were categorized in 2 steps. The gender was determined based on names alone, classifying names as man or woman accordingly. For authors whose gender could not be inferred from their names, professional networks and university websites were searched for photos or text containing gender-specific pronouns.
J Med Internet Res 2024;26:e57667
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Gender Bias in AI's Perception of Cardiovascular Risk
Gender bias refers to the systematic and often unconscious differential or inappropriate treatment and consideration of patients based on their gender [4]. This phenomenon has been extensively reported in the management of cardiovascular disease, with evidence showing that women are inadequately represented in research, underdiagnosed, and subject to treatment disparities [5,6].
J Med Internet Res 2024;26:e54242
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Binary (women vs men) gender estimation by authors’ first names was performed by genderize.io, a popular probabilistic gender inference service built on a large international database of gender-name associations collected from various web sources. The percentages of total female authors, FFAs, and FSAs were calculated for each year to allow comparisons before and during the pandemic-affected time frame.
JMIR Dermatol 2024;7:e50396
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Existing methods to remove representational gender bias from word embeddings aim to remove sensitive information, for example, gender, from the embeddings using data augmentation [15,16], in-training methods modifying the training objective [17], or posttraining methods such as projections to subspaces [4,18,19]. Recently, adversarial training [3,20,21] was also applied to remove information about protected attributes, for example, gender or race, from the representations.
JMIR AI 2024;3:e49546
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