The Use of Artificial Intelligence Tools to Prepare Medical School Applications

Hashimoto, Daniel A.; Johnson, Kevin B.

doi:10.1097/acm.0000000000005309

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…It seems that the differences between the developed and developing countries are largely due to their curricular designs, especially concerning the role of AI or lack thereof. Therefore, it is recommended that medical schools consider mechanisms for knowledge sharing about AI and develop curricula to teach the use of AI tools as a competency [ 43 ]. In addition, medical students should be practically exposed to AI technology incorporations [ 44 ].…”

Section: Discussionmentioning

confidence: 99%

Psychometric properties of the persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS)

et al. 2023

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Introduction There are numerous cases where artificial intelligence (AI) can be applied to improve the outcomes of medical education. The extent to which medical practitioners and students are ready to work and leverage this paradigm is unclear in Iran. This study investigated the psychometric properties of a Persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) developed by Karaca, et al. in 2021. In future studies, the medical AI readiness for Iranian medical students could be investigated using this scale, and effective interventions might be planned and implemented according to the results. Methods In this study, 502 medical students (mean age 22.66(± 2.767); 55% female) responded to the Persian questionnaire in an online survey. The original questionnaire was translated into Persian using a back translation procedure, and all participants completed the demographic component and the entire MAIRS-MS. Internal and external consistencies, factor analysis, construct validity, and confirmatory factor analysis were examined to analyze the collected data. A P ≤ 0.05 was considered as the level of statistical significance. Results Four subscales emerged from the exploratory factor analysis (Cognition, Ability, Vision, and Ethics), and confirmatory factor analysis confirmed the four subscales. The Cronbach alpha value for internal consistency was 0.944 for the total scale and 0.886, 0.905, 0.865, and 0.856 for cognition, ability, vision, and ethics, respectively. Conclusions The Persian version of MAIRS-MS was fairly equivalent to the original one regarding the conceptual and linguistic aspects. This study also confirmed the validity and reliability of the Persian version of MAIRS-MS. Therefore, the Persian version can be a suitable and brief instrument to assess Iranian Medical Students’ readiness for medical artificial intelligence.

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Section: Discussionmentioning

confidence: 99%

Psychometric properties of the persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS)

et al. 2023

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“…For example, applicants are using LLMs to generate drafts of admissions essays, and admissions professionals are using NLP to identify themes of interest in applications. 3,12 Students are using LLMs to simplify and summarize complex material ahead of assessments, and educators are using these tools to draft instructional materials and generate assessment items. 13 Educators are teaching students to use AI-enhanced tools in care delivery.…”

Section: The Need For Ai Literacymentioning

confidence: 99%

“…Over the last decade, AI and LLMs have made a mark on core elements of HPE practice and scholarship. For example, applicants are using LLMs to generate drafts of admissions essays, and admissions professionals are using NLP to identify themes of interest in applications 3,12 . Students are using LLMs to simplify and summarize complex material ahead of assessments, and educators are using these tools to draft instructional materials and generate assessment items 13 .…”

Section: The Need For Ai Literacymentioning

confidence: 99%

The Promise and Perils of Artificial Intelligence in Health Professions Education Practice and Scholarship

Patino,

Amiel,

Brown

et al. 2024

Acad Med

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Artificial intelligence (AI) methods, especially machine learning and natural language processing, are increasingly affecting health professions education (HPE), including the medical school application and selection processes, assessment, and scholarship production. The rise of large language models over the past 18 months, such as ChatGPT, has raised questions about how best to incorporate these methods into HPE. The lack of training in AI among most HPE faculty and scholars poses an important challenge in facilitating such discussions. In this commentary, the authors provide a primer on the AI methods most often used in the practice and scholarship of HPE, discuss the most pressing challenges and opportunities these tools afford, and underscore that these methods should be understood as part of the larger set of statistical tools available. Despite their ability to process huge amounts of data and their high performance completing some tasks, AI methods are only as good as the data on which they are trained. Of particular importance is that these models can perpetuate the biases that are present in those training datasets, and they can be applied in a biased manner by human users. A minimum set of expectations for the application of AI methods in HPE practice and scholarship is discussed in this commentary, including the interpretability of the models developed and the transparency needed into the use and characteristics of such methods. The rise of AI methods is affecting multiple aspects of HPE including raising questions about how best to incorporate these models into HPE practice and scholarship. In this commentary, we provide a primer on the AI methods most often used in HPE and discuss the most pressing challenges and opportunities these tools afford.

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Digital Ink and Surgical Dreams: Perceptions of Artificial Intelligence–Generated Essays in Residency Applications

Crawford,

Hendzlik,

Lam

et al. 2024

Journal of Surgical Research

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The Use of Artificial Intelligence Tools to Prepare Medical School Applications

Cited by 6 publications

References 20 publications

Psychometric properties of the persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS)

Psychometric properties of the persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS)

The Promise and Perils of Artificial Intelligence in Health Professions Education Practice and Scholarship

Digital Ink and Surgical Dreams: Perceptions of Artificial Intelligence–Generated Essays in Residency Applications

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