Preparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum: a pilot study
Artificial intelligence (AI) is rapidly being introduced into the clinical workflow of many specialties. Despite the need to train physicians who understand the utility and implications of AI and mitigate a growing skills gap, no established consensus exists on how to best introduce AI concepts to m...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Medical Education Online |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10872981.2024.2315684 |
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| _version_ | 1832593498673512448 |
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| author | Soo Hwan Park Roshini Pinto-Powell Thomas Thesen Alexander Lindqwister Joshua Levy Rachael Chacko Devina Gonzalez Connor Bridges Adam Schwendt Travis Byrum Justin Fong Shahin Shahsavari Saeed Hassanpour |
| author_facet | Soo Hwan Park Roshini Pinto-Powell Thomas Thesen Alexander Lindqwister Joshua Levy Rachael Chacko Devina Gonzalez Connor Bridges Adam Schwendt Travis Byrum Justin Fong Shahin Shahsavari Saeed Hassanpour |
| author_sort | Soo Hwan Park |
| collection | DOAJ |
| description | Artificial intelligence (AI) is rapidly being introduced into the clinical workflow of many specialties. Despite the need to train physicians who understand the utility and implications of AI and mitigate a growing skills gap, no established consensus exists on how to best introduce AI concepts to medical students during preclinical training. This study examined the effectiveness of a pilot Digital Health Scholars (DHS) non-credit enrichment elective that paralleled the Dartmouth Geisel School of Medicine’s first-year preclinical curriculum with a focus on introducing AI algorithms and their applications in the concurrently occurring systems-blocks. From September 2022 to March 2023, ten self-selected first-year students enrolled in the elective curriculum run in parallel with four existing curricular blocks (Immunology, Hematology, Cardiology, and Pulmonology). Each DHS block consisted of a journal club, a live-coding demonstration, and an integration session led by a researcher in that field. Students’ confidence in explaining the content objectives (high-level knowledge, implications, and limitations of AI) was measured before and after each block and compared using Mann-Whitney U tests. Students reported significant increases in confidence in describing the content objectives after all four blocks (Immunology: U = 4.5, p = 0.030; Hematology: U = 1.0, p = 0.009; Cardiology: U = 4.0, p = 0.019; Pulmonology: U = 4.0, p = 0.030) as well as an average overall satisfaction level of 4.29/5 in rating the curriculum content. Our study demonstrates that a digital health enrichment elective that runs in parallel to an institution’s preclinical curriculum and embeds AI concepts into relevant clinical topics can enhance students’ confidence in describing the content objectives that pertain to high-level algorithmic understanding, implications, and limitations of the studied models. Building on this elective curricular design, further studies with a larger enrollment can help determine the most effective approach in preparing future physicians for the AI-enhanced clinical workflow. |
| format | Article |
| id | doaj-art-c836e92c546f4570805a83d25239324b |
| institution | Kabale University |
| issn | 1087-2981 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Medical Education Online |
| spelling | doaj-art-c836e92c546f4570805a83d25239324b2025-01-20T13:43:56ZengTaylor & Francis GroupMedical Education Online1087-29812024-12-0129110.1080/10872981.2024.2315684Preparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum: a pilot studySoo Hwan Park0Roshini Pinto-Powell1Thomas Thesen2Alexander Lindqwister3Joshua Levy4Rachael Chacko5Devina Gonzalez6Connor Bridges7Adam Schwendt8Travis Byrum9Justin Fong10Shahin Shahsavari11Saeed Hassanpour12Geisel School of Medicine at Dartmouth, Hanover, NH, USAGeisel School of Medicine at Dartmouth, Hanover, NH, USAGeisel School of Medicine at Dartmouth, Hanover, NH, USADepartment of Radiology, Stanford Medicine, Palo Alto, CA, USAGeisel School of Medicine at Dartmouth, Hanover, NH, USAGeisel School of Medicine at Dartmouth, Hanover, NH, USAGeisel School of Medicine at Dartmouth, Hanover, NH, USAGeisel School of Medicine at Dartmouth, Hanover, NH, USAGeisel School of Medicine at Dartmouth, Hanover, NH, USAGeisel School of Medicine at Dartmouth, Hanover, NH, USAGeisel School of Medicine at Dartmouth, Hanover, NH, USAGeisel School of Medicine at Dartmouth, Hanover, NH, USAGeisel School of Medicine at Dartmouth, Hanover, NH, USAArtificial intelligence (AI) is rapidly being introduced into the clinical workflow of many specialties. Despite the need to train physicians who understand the utility and implications of AI and mitigate a growing skills gap, no established consensus exists on how to best introduce AI concepts to medical students during preclinical training. This study examined the effectiveness of a pilot Digital Health Scholars (DHS) non-credit enrichment elective that paralleled the Dartmouth Geisel School of Medicine’s first-year preclinical curriculum with a focus on introducing AI algorithms and their applications in the concurrently occurring systems-blocks. From September 2022 to March 2023, ten self-selected first-year students enrolled in the elective curriculum run in parallel with four existing curricular blocks (Immunology, Hematology, Cardiology, and Pulmonology). Each DHS block consisted of a journal club, a live-coding demonstration, and an integration session led by a researcher in that field. Students’ confidence in explaining the content objectives (high-level knowledge, implications, and limitations of AI) was measured before and after each block and compared using Mann-Whitney U tests. Students reported significant increases in confidence in describing the content objectives after all four blocks (Immunology: U = 4.5, p = 0.030; Hematology: U = 1.0, p = 0.009; Cardiology: U = 4.0, p = 0.019; Pulmonology: U = 4.0, p = 0.030) as well as an average overall satisfaction level of 4.29/5 in rating the curriculum content. Our study demonstrates that a digital health enrichment elective that runs in parallel to an institution’s preclinical curriculum and embeds AI concepts into relevant clinical topics can enhance students’ confidence in describing the content objectives that pertain to high-level algorithmic understanding, implications, and limitations of the studied models. Building on this elective curricular design, further studies with a larger enrollment can help determine the most effective approach in preparing future physicians for the AI-enhanced clinical workflow.https://www.tandfonline.com/doi/10.1080/10872981.2024.2315684Artificial intelligencemachine learningdata scienceundergraduate medical educationpreclinical training |
| spellingShingle | Soo Hwan Park Roshini Pinto-Powell Thomas Thesen Alexander Lindqwister Joshua Levy Rachael Chacko Devina Gonzalez Connor Bridges Adam Schwendt Travis Byrum Justin Fong Shahin Shahsavari Saeed Hassanpour Preparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum: a pilot study Medical Education Online Artificial intelligence machine learning data science undergraduate medical education preclinical training |
| title | Preparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum: a pilot study |
| title_full | Preparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum: a pilot study |
| title_fullStr | Preparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum: a pilot study |
| title_full_unstemmed | Preparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum: a pilot study |
| title_short | Preparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum: a pilot study |
| title_sort | preparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum a pilot study |
| topic | Artificial intelligence machine learning data science undergraduate medical education preclinical training |
| url | https://www.tandfonline.com/doi/10.1080/10872981.2024.2315684 |
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