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Cited by 164Artificial Intelligence in Healthcare and EducationArtificial Intelligence in EducationInnovations in Medical Education

Artificial Intelligence Revolutionizing the Field of Medical Education

Open access
Narayanan, Suresh, Ramakrishnan, Rajprasath, Durairaj, Elantamilan, Das, Arghya·2023·Cureus
DOI 10.7759/cureus.49604PMC PMC10755136
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Abstract

Medical education has ventured into a new arena of computer-assisted teaching powered by artificial intelligence (AI). In medical institutions, AI can serve as an intelligent tool facilitating the decision-making process effectively. AI can enhance teaching by assisting in developing new strategies for educators. Similarly, students also benefit from intelligent systems playing the role of competent teachers. Thus, AI-integrated medical education paves new opportunities for advanced teaching and learning experiences and improved outcomes. On the other hand, optical mark recognition and automated scoring are ways AI can also transform into a real-time assessor and evaluator in medical education. This review summarizes the AI tools and their application in medical teaching or learning, assessment, and administrative support. This article can aid medical institutes in planning and implementing AI according to the needs of the educators.

AI snapshot

Written from the paper’s full text.

TL;DR

This narrative review summarizes AI tools and applications across medical education, including teaching/learning, assessment, research, and administration. It argues AI can personalize learning, automate assessment, and improve administrative efficiency, while noting data, bias, and workforce concerns.

Key findings

  • AI can influence student learning through direct teaching, support teaching, and empowering the learner; subthemes include chatbots, intelligent tutoring systems, virtual patients, gamification, and adaptive learning systems.
  • AI assessment applications include optical mark recognition, automated essay scoring, quizzing, virtual reality, simulation assessment, and assessment of diagnostic, interpretation, prescription-writing, and emergency-response skills.
  • Administrative uses include recruitment/admissions, curriculum design and review, staff/student records, financial aid, feedback management, telemedicine, distance education, virtual inquiry systems, budgeting/resource allocation, and campus safety.
  • The paper cites an estimate that AI in healthcare could save a net sum of 200 to 360 billion USD per year if tapped adequately in the United States alone.

Methods

  • Narrative review summarizing AI tools and their application in medical teaching or learning, assessment, research, and administrative support.
  • No systematic search strategy, databases, inclusion/exclusion criteria, or sample are stated in the paper.
  • Main analysis is a categorical description of AI tools by use and stakeholder, including students, faculty, researchers, and administrators.

Limitations

  • Stated in the paper: machine learning and deep learning need enormous datasets; sharing data between medical institutes is challenging due to organizational resistance, data privacy, and security concerns; AI models may produce biased outcomes; and staff may fear replacement by AI.
  • Evident from the text: as a narrative review, it lacks a systematic search and empirical evaluation, and many claims are supported by cited reviews or commentary rather than primary effectiveness studies.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.7759/cureus.49604

Cites

49 of 49 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 60 cited sources checked · 0 supported · 0 flagged

  • Premisecitation

    There are limited studies that summarize the available AI tools and how they can be effectively used in medical education.

    “There are limited studies that summarize the available AI tools and how they can be effectively used in medical education [3].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [3] Sapci AH, Sapci HA. Artificial intelligence education and tools for medical and health informatics students: systematic review. JMIR Med Educ. 2020, 6:e19285. 10.2196/19285 source

      Not checked in the free run.

  • Premisecitation

    Very few studies have attempted to categorize the AI tools based on their uses for relevant stakeholders such as students, faculty members, and administrators.

    “Although the current studies focused on analyzing the trends of AI usage in medical education, very few studies have attempted to categorize the AI tools based on their uses for relevant stakeholders (e.g., students, faculty members, and administrators) [3,6].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [3] Sapci AH, Sapci HA. Artificial intelligence education and tools for medical and health informatics students: systematic review. JMIR Med Educ. 2020, 6:e19285. 10.2196/19285 source

      Not checked in the free run.

    • cites [6] Varma JR, Fernando S, Ting BY, Aamir S, Sivaprakasam R. The global use of artificial intelligence in the undergraduate medical curriculum: a systematic review. Cureus. 2023, 15:e39701. 10.7759/cureus.39701 source

      Not checked in the free run.

  • Premisecitation

    Presently available chatbots can potentially create false references, a phenomenon described as hallucination.

    “Despite these, presently available chatbots can potentially create false references, a phenomenon described as “hallucination” [7].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [7] Ghorashi N, Ismail A, Ghosh P, Sidawy A, Javan R. AI-powered chatbots in medical education: potential applications and implications. Cureus. 2023, 15:e43271. 10.7759/cureus.43271 source

      Not checked in the free run.

  • Premisecitation

    Chatbots can efficiently automate time-intensive tasks, such as summarizing and evaluating research and medical literature.

    “In the context of medical education, chatbots can efficiently automate time-intensive tasks, such as summarizing and evaluating research and medical literature [9].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [9] Sallam M. ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. Healthcare (Basel). 2023, 11:887. 10.3390/healthcare11060887 source

      Not checked in the free run.

  • Premisecitation

    Automated essay scoring scans the entire document and can understand the overall meaning and the concept even if written with grammatical errors.

    “AES scans the entire document and can understand the overall meaning and the concept even if written with grammatical errors [26].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [26] Gierl MJ, Latifi S, Lai H, Boulais AP, De Champlain A. Automated essay scoring and the future of educational assessment in medical education. Med Educ. 2014, 48:950-62. 10.1111/medu.12517 source

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  • Classified as probably not a substantial claim. 21
  • May combine two claims. 25

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationreasoning

    AI-integrated medical education paves new opportunities for advanced teaching and learning experiences and improved outcomes.

    “Thus, AI-integrated medical education paves new opportunities for advanced teaching and learning experiences and improved outcomes.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Chatbots can serve as preclinical interactive tutors and ward assistants in clinical settings to help students understand complex clinical scenarios and assist in developing decision-making skills.

    “These can serve as preclinical interactive tutors and ward assistants in clinical settings to help students understand complex clinical scenarios and assist in developing decision-making skills [7].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [7] Ghorashi N, Ismail A, Ghosh P, Sidawy A, Javan R. AI-powered chatbots in medical education: potential applications and implications. Cureus. 2023, 15:e43271. 10.7759/cureus.43271 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    AI-based intelligent tutoring systems are adaptive instructional systems that try to emulate the benefits of one-on-one human tutoring.

    “AI-based ITSs are adaptive instructional systems that try to emulate the benefits of one-on-one human tutoring [11].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [11] Randhawa GK, Jackson M. The role of artificial intelligence in learning and professional development for healthcare professionals. Healthc Manage Forum. 2020, 33:19-24. 10.1177/0840470419869032 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    AI can serve as an intelligent tool facilitating the decision-making process effectively in medical institutions.

    “In medical institutions, AI can serve as an intelligent tool facilitating the decision-making process effectively.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    AI can enhance teaching by assisting in developing new strategies for educators.

    “AI can enhance teaching by assisting in developing new strategies for educators.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    AI has emerged as an integral part of healthcare education and has been adopted by several medical institutions worldwide.

    “In recent years, AI has emerged as an integral part of healthcare education and has been adopted by several medical institutions worldwide [2,3].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [2] Chan KS, Zary N. Applications and challenges of implementing artificial intelligence in medical education: integrative review. JMIR Med Educ. 2019, 5:e13930. 10.2196/13930 source

      Not checked in the free run.

    • cites [3] Sapci AH, Sapci HA. Artificial intelligence education and tools for medical and health informatics students: systematic review. JMIR Med Educ. 2020, 6:e19285. 10.2196/19285 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    The primary benefits of using chatbots include integrating content, quick access, allowing multiple users, and immediate assistance.

    “The primary benefits of using chatbots include integrating content, quick access, allowing multiple users and immediate assistance [9,10].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [9] Sallam M. ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. Healthcare (Basel). 2023, 11:887. 10.3390/healthcare11060887 source

      Not checked in the free run.

    • cites [10] Al Kahf S, Roux B, Clerc S, et al.. Chatbot-based serious games: a useful tool for training medical students? A randomized controlled trial. PLoS One. 2023, 18:e0278673. 10.1371/journal.pone.0278673 source

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  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Premisecitation

    Creating awareness among medical educators and students about AI tools is essential to developing and implementing AI-based tools in medical education.

    “Creating awareness among medical educators and students about AI tools is essential to developing and implementing AI-based tools in medical education [4,5].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [4] Kansal R, Bawa A, Bansal A, Trehan S, Goyal K, Goyal N, Malhotra K. Differences in knowledge and perspectives on the usage of artificial intelligence among doctors and medical students of a developing country: a cross-sectional study. Cureus. 2022, 14:e21434. 10.7759/cureus.21434 source

      Not checked in the free run.

    • cites [5] Ahmed Z, Bhinder KK, Tariq A, et al.. Knowledge, attitude, and practice of artificial intelligence among doctors and medical students in Pakistan: a cross-sectional online survey. Ann Med Surg (Lond). 2022, 76:103493. 10.1016/j.amsu.2022.103493 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationreasoning

    Integrating AI tools in learning can enhance students' knowledge, skill development, and understanding of complex medical concepts.

    “Integrating AI tools in learning can enhance students' knowledge, skill development, and understanding of complex medical concepts.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    Optical mark recognition and automated scoring are ways AI can transform into a real-time assessor and evaluator in medical education.

    “On the other hand, optical mark recognition and automated scoring are ways AI can also transform into a real-time assessor and evaluator in medical education.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    Students benefit from intelligent systems playing the role of competent teachers.

    “Similarly, students also benefit from intelligent systems playing the role of competent teachers.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    AI can influence the student learning process in three ways: direct teaching, support teaching, and empowering the learner.

    “AI can influence the student learning process in three ways: direct teaching (imparting knowledge to the student while playing the role of a teacher), support teaching (playing a supporting role and collaborating with students as they learn), and empowering the learner (multiple students can collaborate to solve a complex problem upon feedback provided by the teacher) [2,3,6].”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [2] Chan KS, Zary N. Applications and challenges of implementing artificial intelligence in medical education: integrative review. JMIR Med Educ. 2019, 5:e13930. 10.2196/13930 source

      Not checked in the free run.

    • cites [3] Sapci AH, Sapci HA. Artificial intelligence education and tools for medical and health informatics students: systematic review. JMIR Med Educ. 2020, 6:e19285. 10.2196/19285 source

      Not checked in the free run.

    • cites [6] Varma JR, Fernando S, Ting BY, Aamir S, Sivaprakasam R. The global use of artificial intelligence in the undergraduate medical curriculum: a systematic review. Cureus. 2023, 15:e39701. 10.7759/cureus.39701 source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    Automated essay scoring, if incorporated into routine practice, can save time and human resources, and can keep track of the studying and knowledge pattern of the learner to give individualized feedback focusing on areas of improvement.

    “This method, if incorporated into routine practice, can save time and human resources, and it can also keep track of the studying and knowledge pattern of the learner to give individualized feedback focussing on the areas of improvement.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Gamification provides opportunities for risk-free clinical decision-making and immediate feedback to students, thereby becoming an integral part of undergraduate medical education.

    “They also provide opportunities for risk-free clinical decision-making and immediate feedback to the students, thereby becoming an integral part of undergraduate medical education [18,19].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [18] McCoy L, Lewis JH, Dalton D. Gamification and multimedia for medical education: a landscape review. J Am Osteopath Assoc. 2016, 116:22-34. 10.7556/jaoa.2016.003 source

      Not checked in the free run.

    • cites [19] Abd-Alrazaq A, Abuelezz I, Hassan A, et al.. Artificial intelligence-driven serious games in health care: scoping review. JMIR Serious Games. 2022, 10:e39840. 10.2196/39840 source

      Not checked in the free run.

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    Premisecitation

    AI with machine learning is being used to diagnose microbial infections, which can be immense for training and educating technicians in microbiology.

    “Similarly, AI with machine learning is being used to diagnose microbial infections, which can be immense for training and educating technicians in microbiology [23].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [23] Mishra A, Khan S, Das A, Das BC. Evolution of diagnostic and forensic microbiology in the era of artificial intelligence. Cureus. 2023, 15:e45738. 10.7759/cureus.45738 source

      Not checked in the free run.

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    Premisecitation

    AI tools can aid the researcher in collecting relevant research articles, identifying knowledge gaps in the literature, formulating research questions, recommending appropriate statistical methods for the available data, creating a graphical representation of the data, and manuscript writing.

    “AI tools can aid the researcher in collecting relevant research articles, identifying the knowledge gaps in the literature, formulating research questions, recommending appropriate statistical methods for the available data, creating a graphical representation of the data, and manuscript writing [32-35].”
    Quote in the paperNot a substantial claim0 of 4 sources checked
    • cites [32] Cole V, Boutet M. ResearchRabbit. J Can Health Libr Assoc. 2023, 44:43-7. 10.29173/jchla29699 source

      Not checked in the free run.

    • cites [33] King MR. A conversation on artificial intelligence, chatbots, and plagiarism in higher education. Cell Mol Bioeng. 2023, 16:1-2. 10.1007/s12195-022-00754-8 source

      Not checked in the free run.

    • cites [34] Fröhling L, Zubiaga A. Feature-based detection of automated language models: tackling GPT-2, GPT-3 and Grover. PeerJ Comput Sci. 2021, 7:e443. 10.7717/peerj-cs.443 source

      Not checked in the free run.

    • cites [35] Jeyaraman M, K SP, Jeyaraman N, Nallakumarasamy A, Yadav S, Bondili SK. ChatGPT in medical education and research: a boon or a bane?. Cureus. 2023, 15:e44316. 10.7759/cureus.44316 source

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  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Most medication errors happen in the transcription process, followed by prescription errors and then by errors in administration.

    “Most of the errors happen in the transcription process, followed by prescription errors and then by errors in administration [29].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [29] Zirpe KG, Seta B, Gholap S, et al.. Incidence of medication error in critical care unit of a tertiary care hospital: where do we stand?. Indian J Crit Care Med. 2020, 24:799-803. 10.5005/jp-journals-10071-23556 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    AI can be utilized in various ways for assessment in medical education to avoid subjective bias.

    “AI can be utilized in various ways for assessment in medical education to avoid subjective bias.”
    Quote in the paperNot a substantial claim

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    Premisecitation

    Personalized and adaptive teaching methods improve learning efficacy and efficiency.

    “These personalized and adaptive teaching methods improve learning efficacy and efficiency [20,21].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [20] Fontaine G, Cossette S, Maheu-Cadotte MA, et al.. Efficacy of adaptive e-learning for health professionals and students: a systematic review and meta-analysis. BMJ Open. 2019, 9:e025252. 10.1136/bmjopen-2018-025252 source

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    • cites [21] Gupta S, Ojeh N, Sa B, Majumder MA, Singh K, Adams OP. Use of an adaptive e-learning platform as a formative assessment tool in the cardiovascular system course component of an MBBS programme. Adv Med Educ Pract. 2020, 11:989-96. 10.2147/AMEP.S267834 source

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    Premisecitation

    The common themes of intelligent tutoring system use in medical education include teaching course content, diagnosing strengths or gaps in students' knowledge, providing automated feedback, curating learning materials based on student's needs, and facilitating collaboration between learners.

    “The common themes of ITS use in medical education include teaching course content, diagnosing strengths or gaps in students' knowledge, providing automated feedback, curating learning materials based on student's needs, and facilitating collaboration between learners [12].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [12] El Saadawi GM, Tseytlin E, Legowski E, et al.. A natural language intelligent tutoring system for training pathologists: implementation and evaluation. Adv Health Sci Educ Theory Pract. 2008, 13:709-22. 10.1007/s10459-007-9081-3 source

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    Premisecitation

    Gamification of the learning process increases student engagement, enhances collaborative efforts, and improves learning outcomes.

    “Gamification of the learning process increases student engagement, enhances collaborative efforts, and improves learning outcomes.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [18] McCoy L, Lewis JH, Dalton D. Gamification and multimedia for medical education: a landscape review. J Am Osteopath Assoc. 2016, 116:22-34. 10.7556/jaoa.2016.003 source

      Not checked in the free run.

    • cites [19] Abd-Alrazaq A, Abuelezz I, Hassan A, et al.. Artificial intelligence-driven serious games in health care: scoping review. JMIR Serious Games. 2022, 10:e39840. 10.2196/39840 source

      Not checked in the free run.

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    Premisecitation

    Machine learning is incorporated to interpret laboratory data accurately and detect abnormal values.

    “Machine learning is incorporated to interpret laboratory data accurately and detect abnormal values [28].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [28] Rabbani N, Kim GY, Suarez CJ, Chen JH. Applications of machine learning in routine laboratory medicine: current state and future directions. Clin Biochem. 2022, 103:1-7. 10.1016/j.clinbiochem.2022.02.011 source

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  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationreasoning

    Virtual reality can be best employed in assessing the surgical skills of postgraduate students.

    “VR can be best employed in assessing the surgical skills of postgraduate students.”
    Quote in the paperNot a substantial claim

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    Premisecitation

    When interacting with virtual patients, medical students can practice their communication and clinical reasoning skills, creating an immersive and interactive virtual environment that mimics real-world scenarios.

    “When interacting with virtual patients, medical students can practice their communication and clinical reasoning skills, creating an immersive and interactive virtual environment that mimics real-world scenarios [13,14].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [13] Kononowicz AA, Woodham LA, Edelbring S, et al.. Virtual patient simulations in health professions education: systematic review and meta-analysis by the digital health education collaboration. J Med Internet Res. 2019, 21:e14676. 10.2196/14676 source

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    • cites [14] Kyaw BM, Saxena N, Posadzki P, et al.. Virtual reality for health professions education: systematic review and meta-analysis by the digital health education collaboration. J Med Internet Res. 2019, 21:e12959. 10.2196/12959 source

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    Premisecitation

    Content-based image retrieval helps by searching for images with contents similar to a reference image using information derived from the images.

    “CBIR helps by searching for images with contents similar to a reference image using information derived from the images [22].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [22] Santos MK, Ferreira Júnior JR, Wada DT, Tenório AP, Barbosa MH, Marques PM. Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine. Radiol Bras. 2019, 52:387-96. 10.1590/0100-3984.2019.0049 source

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    Medication errors are a significant preventable hazard in the health care system.

    “Medication errors are a significant preventable hazard in the health care system.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [29] Zirpe KG, Seta B, Gholap S, et al.. Incidence of medication error in critical care unit of a tertiary care hospital: where do we stand?. Indian J Crit Care Med. 2020, 24:799-803. 10.5005/jp-journals-10071-23556 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationreasoning

    AI can reduce the student burden in searching for reliable resources, provide a personalized adaptive learning experience with immediate feedback, and provide a simulation-based platform for a practical learning experience.

    “To summarize, AI can reduce the student burden in searching for reliable resources, provide a personalized adaptive learning experience with immediate feedback, and provide a simulation-based platform for a practical learning experience.”
    Quote in the paperNot a substantial claim

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    Premisecitation

    Some studies show a lack of knowledge on managing anaphylaxis and safe blood transfusion skills.

    “Some studies show a lack of knowledge on managing anaphylaxis and safe blood transfusion skills [30,31].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [30] Drupad HS, Nagabushan H. Level of knowledge about anaphylaxis and its management among health care providers. Indian J Crit Care Med. 2015, 19:412-5. 10.4103/0972-5229.160288 source

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    • cites [31] Ray GK, Mukherjee S, Routray SS, Sahu A, Mishra D, Naik A, Prakash S. Knowledge, attitudes and practices of resident doctors and interns on safe blood transfusion practices: a survey-based study. Hematol Transfus Cell Ther. 2023, 45:342-9. 10.1016/j.htct.2022.06.004 source

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    Premisecitation

    AI-based budgeting could present several options to enhance allocation procedures.

    “AI-based budgeting could present several options to enhance the allocation procedures.”
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    • cites [47] Budgeting in the age of artificial intelligence - new opportunities and challenges. (2022). Accessed. October 5, 2023: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4168230.

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    Premisecitation

    ResearchRabbit enables researchers in medical institutes with novel ways to search for articles and writers, monitor research landscapes, and collaborate with researchers from other institutes.

    “ResearchRabbit (Human Intelligence Technologies Incorporated, Seattle, WA, USA), an AI-based tool developed in 2021, enables researchers in medical institutes with novel ways to search for articles and writers, monitor research landscapes, and collaborate with researchers from other institutes.”
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    • cites [32] Cole V, Boutet M. ResearchRabbit. J Can Health Libr Assoc. 2023, 44:43-7. 10.29173/jchla29699 source

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    Premisecitation

    The use of an external dataset can validate results and reduce bias.

    “The use of an external dataset can validate results and reduce bias.”
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    • cites [49] Aung YY, Wong DC, Ting DS. The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. Br Med Bull. 2021, 139:4-15. 10.1093/bmb/ldab016 source

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    Premisereasoning

    Collaboration between institutions or even at the national level is crucial to allow for separate datasets for comparison.

    “Therefore, collaboration between institutions or even at the national level is crucial to allow for separate datasets for comparison [49].”
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    • cites [49] Aung YY, Wong DC, Ting DS. The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. Br Med Bull. 2021, 139:4-15. 10.1093/bmb/ldab016 source

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    Premisecitation

    Many countries have started implementing a dual approval system for continuing medical education, enabling a multi-fold rise in certified professionals.

    “Many countries have started implementing a 'dual approval system' for continuing medical education. This has enabled a multi-fold rise in certified professionals [45].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [45] Mir MM, Mir GM, Raina NT, et al.. Application of artificial intelligence in medical education: current scenario and future perspectives. J Adv Med Educ Prof. 2023, 11:133-40. 10.30476/JAMP.2023.98655.1803 source

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    Premisecitation

    AI-enabled telehealth offers contributions in the form of quality improvement, enhancement of existing practice, and the instigation of new models of care and education.

    “AI-enabled telehealth offers contributions in the form of quality improvement, enhancement of existing practice, and the instigation of new models of care and education [44].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [44] Kuziemsky C, Maeder AJ, John O, Gogia SB, Basu A, Meher S, Ito M. Role of artificial intelligence within the telehealth domain. Yearb Med Inform. 2019, 28:35-40. 10.1055/s-0039-1677897 source

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    Premisecitation

    A study estimates AI in healthcare could save a net sum of 200 to 360 billion USD per year if tapped adequately in the United States alone.

    “A study estimates AI in healthcare could save a net sum of 200 to 360 billion USD per year if tapped adequately in the United States alone [46].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [46] Sahni NR, Stein G, Zemmel R, Cutler DM. The potential impact of artificial intelligence on healthcare spending. The Economics of Artificial Intelligence: Health Care Challenges. Agrawal A, Gans J, Goldfarb A, Tucker C (ed): University of Chicago Press, Chicago, IL; 2023.

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    Premisecitation

    AI can help researchers in analyzing vast amounts of data from patients' treatment charts, laboratory reports, and textual feedback from students, and extract relevant information needed for research followed by sorting out the data in a structured manner for statistical analysis.

    “AI can also help researchers in analyzing vast amounts of data from patients’ treatment charts, laboratory reports, and textual feedback from students, and extract the relevant information needed for the research followed by sorting out the data in a structured manner for statistical analysis [36,37].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [36] Dahmen J, Kayaalp ME, Ollivier M, Pareek A, Hirschmann MT, Karlsson J, Winkler PW. Artificial intelligence bot ChatGPT in medical research: the potential game changer as a double-edged sword. Knee Surg Sports Traumatol Arthrosc. 2023, 31:1187-9. 10.1007/s00167-023-07355-6 source

      Not checked in the free run.

    • cites [37] Anis S, French JA. Efficient, explicatory, and equitable: why qualitative researchers should embrace AI, but cautiously. Business & Soc. 2023, 62:1139-44. 10.1177/00076503231163286 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    The application of AI in educational management and administration is still in its early stages but has already shown promising results.

    “The application of AI in educational management and administration is still in its early stages but has already shown promising results.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [38] Ahmad SF, Alam MM, Rahmat MK, Mubarik MS, Hyder SI. Academic and administrative role of artificial intelligence in education. Sustainability. 2022, 14:1101. 10.3390/su14031101 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    AI can assist in automating plenty of administrative tasks and improve overall efficiency.

    “It can assist in automating plenty of administrative tasks and improve its overall efficiency [38].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [38] Ahmad SF, Alam MM, Rahmat MK, Mubarik MS, Hyder SI. Academic and administrative role of artificial intelligence in education. Sustainability. 2022, 14:1101. 10.3390/su14031101 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationnone given

    AI can help institutions automate financial aid applications, identify students who may be eligible for scholarships or other aid forms, and provide personalized support to students.

    “AI can help institutions automate financial aid applications, identify students who may be eligible for scholarships or other aid forms, and provide personalized support to students.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    Feedback analytics can be significantly improved with the help of AI.

    “The feedback analytics can be significantly improved with the help of AI.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationnone given

    Although AI has long been adopted in education, its application is still limited in medical institutions.

    “Although AI has long been adopted in education, its application is still limited in medical institutions.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationnone given

    The success of AI in medical education will ultimately rely on organizational support and commitment by teachers and students to use the technology effectively within ethical boundaries.

    “As for any emerging technology, the success of AI in medical education will ultimately rely on organizational support and commitment by teachers and students to use the technology effectively within ethical boundaries.”
    Quote in the paperNot a substantial claim
42 of 42

  • The paper relies on this without support.

    The use of AI in medical education is rampant in Western countries compared to developing countries.

    “The use of AI in medical education is rampant in Western countries compared to developing countries.”

  • The paper relies on this without support.

    The difference in AI use between Western and developing countries could be lessened by more infrastructural support from medical institutions in the developing world.

    “The difference could be lessened by more infrastructural support from medical institutions in the developing world.”

  • The paper relies on this without support.

    AI would be practically impossible to successfully replace educators and administrators in an institute.

    “All cadres of staff need to be sensitized to the fact that AI would be practically impossible to successfully replace educators and administrators in an institute.”
3 of 3

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The importance of equipping future doctors with key AI skills has increased.

    “Moreover, the importance of equipping future doctors with key AI skills has increased, leading to efforts to incorporate AI literacy into undergraduate medical education (UGME) [2–4].”

    From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [3]

    The Introduction and background (page unavailable) says that creating awareness of AI tools among medical students is essential and that AI has been adopted by several medical institutions. It does not specifically say that the importance of equipping future doctors with AI skills has increased or that this has led to efforts to incorporate AI literacy into undergraduate medical education. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • AI can support personalized, AI-driven training for clinicians.

    “One promising example is the use of AI to support personalized, AI-driven training for clinicians—both in clinical education and in understanding AI-related concepts [44].”

    From Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22. (2025) · cites this paper as [44]

    The Review (page unavailable) describes AI systems that tailor medical teaching and clinical-case assessments to a learner’s knowledge level, supporting personalized clinical education. It does not describe personalized training for clinicians in understanding AI-related concepts; its discussion of AI awareness is not such a training example. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Study participants did not believe AI would replace human teachers but believed that the role of the teacher may change.

    “In addition, study participants did not believe AI would replace human teachers [49] but that the role of the teacher may change [3].”

    From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [3]

Cites

Flagged first, then by citations

Research works printed but not linked: 2

  1. Artificial intelligence reshaping human resource management. a review. (2020). Accessed: October 5, 2023: https://ieeexplore.ieee.org/abstract/document/9398420/authors#authors.
  2. Budgeting in the age of artificial intelligence - new opportunities and challenges. (2022). Accessed. October 5, 2023: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4168230.

Cited by

Sorted by citations

Related

Authors
Narayanan, S., Ramakrishnan, R., Durairaj, E., & Das, A.
Affiliations
All India Institute of Medical Sciences, Madurai (IN)
Corresponding author
Das, Arghya
ORCID
Venue
Cureus
Publisher
Springer Science and Business Media LLC
Published
2023-11-28
Volume
15
Issue
11
Pages
e49604-e49604
ISSN
2168-8184 (ISSN-L)
DOI
10.7759/cureus.49604
PMC
PMC10755136
OpenAlex
W4389150147
Cited by
164
References
49
Citations by year
2026: 38 · 2025: 94 · 2024: 32
FWCI
1.93
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Cureus (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
Keywords
artificial intelligence in medical education, computer-assisted instruction, automated assessment, optical mark recognition, automated grading, intelligent tutoring systems, medical education administration

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-10.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-10.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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