The Desire of Medical Students to Integrate Artificial Intelligence Into Medical Education: An Opinion Article

Frommeyer, Timothy C.; Fursmidt, Reid; Gilbert, Michael M.; Bett, Ean S.

doi:10.3389/fdgth.2022.831123

Cited by 11 publications

(8 citation statements)

References 28 publications

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“…Indeed, pattern-recognition-based clinical reasoning models have been proposed in the pedagogy of medical education, and already the concept of AI-assisted pattern recognition for diagnosis is being investigated. 58 , 59 Just as experience with a constellation of patient signs and symptoms can lead to rapid recognition of an ICU clinical diagnosis, medication use also has certain patterns. 59 , 60 When an ICU clinician reviews the medication profile, each individual medication order is evaluated in a broader clinical context with the other medication orders.…”

Section: Discussionmentioning

confidence: 99%

A common data model for the standardization of intensive care unit medication features

Sikora,

Keats,

Murphy

et al. 2024

JAMIA Open

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Objective Common data models provide a standard means of describing data for artificial intelligence (AI) applications, but this process has never been undertaken for medications used in the intensive care unit (ICU). We sought to develop a common data model (CDM) for ICU medications to standardize the medication features needed to support future ICU AI efforts. Materials and Methods A 9-member, multi-professional team of ICU clinicians and AI experts conducted a 5-round modified Delphi process employing conference calls, web-based communication, and electronic surveys to define the most important medication features for AI efforts. Candidate ICU medication features were generated through group discussion and then independently scored by each team member based on relevance to ICU clinical decision-making and feasibility for collection and coding. A key consideration was to ensure the final ontology both distinguished unique medications and met Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles. Results Using a list of 889 ICU medications, the team initially generated 106 different medication features, and 71 were ranked as being core features for the CDM. Through this process, 106 medication features were assigned to 2 key feature domains: drug product-related (n = 43) and clinical practice-related (n = 63). Each feature included a standardized definition and suggested response values housed in the electronic data library. This CDM for ICU medications is available online. Conclusion The CDM for ICU medications represents an important first step for the research community focused on exploring how AI can improve patient outcomes and will require ongoing engagement and refinement.

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Section: Discussionmentioning

confidence: 99%

A common data model for the standardization of intensive care unit medication features

Sikora,

Keats,

Murphy

et al. 2024

JAMIA Open

View full text Add to dashboard Cite

show abstract

“…Indeed, pattern-recognition based clinical reasoning models have been proposed in the pedagogy of medical education, and, already, the concept of AI-assisted pattern recognition for diagnosis is being investigated. [37 38] Just as experience with a constellation of patient signs and symptoms can lead to rapid recognition of a new ICU clinical diagnosis, medication use also has certain patterns. [38 39] When an ICU clinician reviews the medication profile, each individual medication order is evaluated in a broader clinical context with the other medication orders.…”

Section: Discussionmentioning

confidence: 99%

A Common Data Model for the standardization of intensive care unit (ICU) medication features in artificial intelligence (AI) applications

Sikora,

Keats,

Murphy

et al. 2023

Preprint

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Objective: Common Data Models provide a standard means of describing data for artificial intelligence (AI) applications, but this process has never been undertaken for medications used in the intensive care unit (ICU). We sought to develop a Common Data Model (CDM) for ICU medications to standardize the medication features needed to support future ICU AI efforts. Materials and Methods: A 9-member, multi-professional team of ICU clinicians and AI experts conducted a 5-round modified Delphi process employing conference calls, web-based communication, and electronic surveys to define the most important medication features for AI efforts. Candidate ICU medication features were generated through group discussion and then independently scored by each team member based on relevance to ICU clinical decision-making and feasibility for collection and coding. A key consideration was to ensure the final ontology both distinguished unique medications and met Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles. Results: Using a list of 889 ICU medications, the team initially generated 106 different medication features, and 71 were ranked as being core features for the CDM. Through this process, 106 medication features were assigned to two key feature domains: drug product-related (n=43) and clinical practice-related (n=63). Each feature included a standardized definition and suggested response values housed in the electronic data library. This CDM for ICU medications is available online. Discussion: The CDM for ICU medications represents an important first step for the research community focused on exploring how AI can improve patient outcomes and will require ongoing engagement and refinement.

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“…Frommeyer et al ( 5 ), and more recently Ravindran ( 4 ), gave some convincing arguments for the practical benefits of using AI tools, which operate on the basis of machine learning (ML), in the provision of certain healthcare services. These advantages relate to the enormous savings in time and human resources when conducting (i) radiological imaging (i.e., optimizing screening of potentially cancerous tissues during oncological examination), (ii) discovery of new drugs by using large datasets containing information about the chemical and molecular constituents of pre-existing drugs, and (iii) creating therapeutic and treatment regiments that are personalised according to the patient's needs [for details, see ( 4 – 6 )].…”

Section: Ai Knowledge That Mattersmentioning

confidence: 99%

“…It has proven its merits in the healthcare industry over the past decades and there are promising prospects that it will continue to do so in the years to come ( 3 , 4 ). The recent opinion article by Frommeyer et al ( 5 ) highlighted the value of AI for the medical profession with respect to precision medicine, drug discovery, disease diagnosis, and healthcare management, before making a “call to action” for the introduction of “guided seminars and courses on biostatistics, digital health literacy, and engineering technologies ( 5 , p. 3).” In this commentary, we respond to this call for AI-oriented medical education by providing some internationally applicable ideas that would turn this proposal of Frommeyer's et al ( 5 ) into reality.…”

Section: Introductionmentioning

confidence: 99%

Commentary: The desire of medical students to integrate artificial intelligence into medical education: An opinion article

Zhong

Fischer

2023

Front. Digit. Health

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The Desire of Medical Students to Integrate Artificial Intelligence Into Medical Education: An Opinion Article

Cited by 11 publications

References 28 publications

A common data model for the standardization of intensive care unit medication features

A common data model for the standardization of intensive care unit medication features

A Common Data Model for the standardization of intensive care unit (ICU) medication features in artificial intelligence (AI) applications

Commentary: The desire of medical students to integrate artificial intelligence into medical education: An opinion article

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