Machine learning and medical education

Kolachalama, Vijaya B.; Garg, Priya S.

doi:10.1038/s41746-018-0061-1

Cited by 212 publications

(214 citation statements)

References 14 publications

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“…(32) We believe that a radiology AI/ML curriculum should aim for literacy rather than proficiency, allowing radiologists to understand concepts behind algorithms in practice, collaborate with data scientists to uncover clinical usage of AI/ML as well as appreciate its limitations, pitfalls and safety issues. (30,32) This is similar to the objectives behind the imaging physics curriculum within the radiology residency programme, which is validated by our results, with 80.0% of our respondents agreeing that AI/ML training is as important as the imaging physics curriculum. Surprisingly, a high 72.8% of respondents also agreed that AI/ML training is as important as the clinical skills and knowledge curriculum and 67.2% believed it should be an accessible domain in the residency programme.…”

Section: Discussionmentioning

confidence: 99%

Attitudes toward artificial intelligence in radiology with learner needs assessment within radiology residency programmes: a national multi-programme survey

Skg

Makmur

Ayq

et al. 2021

smedj

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Section: Discussionmentioning

confidence: 99%

Attitudes toward artificial intelligence in radiology with learner needs assessment within radiology residency programmes: a national multi-programme survey

Skg

Makmur

Ayq

et al. 2021

smedj

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“…It is now beyond any debate that AI should be incorporated into medical curriculum for under graduates (Kolachalama and Garg, 2018). Many articles have proved it that introducing AI at an earlier point in medical career helps to shape the students better.…”

Section: Discussionmentioning

confidence: 99%

Knowledge and Education about Artificial Intelligence among Medical Students from Teaching Institutions of India: A Brief Survey

Jindal¹,

Bansal²

2020

MedEdPublish

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Objectives: To assess the knowledge and education about artificial intelligence (AI) among medical students in India. Materials and methods: A web-based questionnaire was designed using Survey Monkey and sent via WhatsApp to second and third-year medical students of three premier medical institutions of India. The questionnaire consisted of ten questions aiming to assess the knowledge and education related to AI and computer language in medical field. The inclusion was voluntary with prior intimation that the results can be used for analysis. Results: A total of 226 students responded to the survey, out of these 115 were boys and 111 were girls with a median age of 20 years. Majority of the students had heard about AI (62.5%) and internet was the main source of information for them. Unfortunately, none wanted to pursue their career in AI. 85.9% students showed interest to learn more about AI, although knowledge about any computer language was lacking in 73% students. 73.4% students were also unaware about journals and publications going on in the field of AI. However, 89.1% students were optimistic about scope of integration of AI in medicine. For all the responses, there was no significant difference between boys and girls. Conclusion: This brief survey highlights that although our medical students are not fully aware about AI and its applications, yet they are keen to learn more about AI and its associated computer logistics. They also understand the potential of AI in medical science which is why the present medical curriculum needs to be AI-oriented.

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“…Machine learning represents the next wave in advancing modern medicine. The next generation of medical professionals should be equipped with the right ML techniques that will enable them to become part of this emerging revolutionary technology [16]. For more information on ML, see the book in [17] and other books available at Amazon.com…”

Section: Resultsmentioning

confidence: 99%

Machine Learning in Medicine: A Primer

Sadiku¹,

Musa²,

Omotoso³

2019

IJTSRD

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Machine learning is the field that focuses on how computers learn from data. Today, machine learning is playing an integral role in the medical industry. This is due to its ability to process huge datasets beyond the scope of human capability, and then convert the data analyzed into clinical insights that aid physicians in providing care. Machine learning is a powerful, relatively easy to implement tool with numerous possibilities to enhance medical practice. The applications of machine learning in medicine are advancing medicine into a new realm. Therefore, educating the next generation of medical professionals with machine learning is essential. This paper provides a brief introduction to applying machine learning in medicine.

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Machine learning and medical education

Cited by 212 publications

References 14 publications

Attitudes toward artificial intelligence in radiology with learner needs assessment within radiology residency programmes: a national multi-programme survey

Attitudes toward artificial intelligence in radiology with learner needs assessment within radiology residency programmes: a national multi-programme survey

Knowledge and Education about Artificial Intelligence among Medical Students from Teaching Institutions of India: A Brief Survey

Machine Learning in Medicine: A Primer

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