Ilan Reinstein
Biographic Data
| ID | 9648623 |
|---|---|
| NAME | Ilan Reinstein |
| GIVEN NAMES | Ilan |
| FAMILY NAME | Reinstein |
| SIGNATURE | REINSTEIN I |
| AFFILIATIONS | I. Reinstein is a data science engineer, Institute for Innovations in Medical Education, NYU Grossman School of Medicine, New York, New York ; [email protected] |
| ORCID | 0000-0001-7360-4436 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
Artificial Intelligence Screening of Medical School Applications
A New Tool for Holistic Residency Application Review
Identifying Meaningful Patterns of Internal Medicine Clerkship Grading Distributions
Toward (More) Valid Comparison of Residency Applicants’ Grades
Clerkship grades are a core element of medical school assessment and are used by residency program directors to select residency applicants, despite substantial variation in grading systems and distributions across institutions. 1–4 Despite calls to abandon the practice, 5 program directors are likely to continue comparing applicants across programs using clerkship grades, especially as the United States Medical Licensing Examination Step 1 goes …
Development and Validation of a Machine Learning-Based Decision Support Tool for Residency Applicant Screening and Review
PURPOSE: Residency programs face overwhelming numbers of residency applications, limiting holistic review. Artificial intelligence techniques have been proposed to address this challenge but have not been created. Here, a multidisciplinary team sought to develop and validate a machine learning (ML)-based decision support tool (DST) for residency applicant screening and review. METHOD: Categorical applicant data from the 2018, 2019, and 2020 resid…
No prominent works on this page.
Development and Validation of a Machine Learning-Based Decision Support Tool for Residency Applicant Screening and Review
PURPOSE: Residency programs face overwhelming numbers of residency applications, limiting holistic review. Artificial intelligence techniques have been proposed to address this challenge but have not been created. Here, a multidisciplinary team sought to develop and validate a machine learning (ML)-based decision support tool (DST) for residency applicant screening and review. METHOD: Categorical applicant data from the 2018, 2019, and 2020 resid…
Toward (More) Valid Comparison of Residency Applicants’ Grades
Clerkship grades are a core element of medical school assessment and are used by residency program directors to select residency applicants, despite substantial variation in grading systems and distributions across institutions. 1–4 Despite calls to abandon the practice, 5 program directors are likely to continue comparing applicants across programs using clerkship grades, especially as the United States Medical Licensing Examination Step 1 goes …
Artificial Intelligence Screening of Medical School Applications
A New Tool for Holistic Residency Application Review
Identifying Meaningful Patterns of Internal Medicine Clerkship Grading Distributions
Computer Science (5 works) · Diversity and Career in Medicine (5 works) · Medical Education and Admissions (5 works) · Medical education (4 works) · Medicine (4 works) · Artificial Intelligence (3 works) · Machine learning (3 works) · Medical school (3 works) · Psychology (3 works) · Artificial Intelligence in Healthcare and Education (2 works)