Dataset Information

Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review.

ABSTRACT: BACKGROUND:The use of artificial intelligence (AI) in medicine will generate numerous application possibilities to improve patient care, provide real-time data analytics, and enable continuous patient monitoring. Clinicians and health informaticians should become familiar with machine learning and deep learning. Additionally, they should have a strong background in data analytics and data visualization to use, evaluate, and develop AI applications in clinical practice. OBJECTIVE:The main objective of this study was to evaluate the current state of AI training and the use of AI tools to enhance the learning experience. METHODS:A comprehensive systematic review was conducted to analyze the use of AI in medical and health informatics education, and to evaluate existing AI training practices. PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) guidelines were followed. The studies that focused on the use of AI tools to enhance medical education and the studies that investigated teaching AI as a new competency were categorized separately to evaluate recent developments. RESULTS:This systematic review revealed that recent publications recommend the integration of AI training into medical and health informatics curricula. CONCLUSIONS:To the best of our knowledge, this is the first systematic review exploring the current state of AI education in both medicine and health informatics. Since AI curricula have not been standardized and competencies have not been determined, a framework for specialized AI training in medical and health informatics education is proposed.

SUBMITTER: Sapci AH

PROVIDER: S-EPMC7367541 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review.

Sapci A Hasan AH Sapci H Aylin HA

JMIR medical education 20200630 1

<h4>Background</h4>The use of artificial intelligence (AI) in medicine will generate numerous application possibilities to improve patient care, provide real-time data analytics, and enable continuous patient monitoring. Clinicians and health informaticians should become familiar with machine learning and deep learning. Additionally, they should have a strong background in data analytics and data visualization to use, evaluate, and develop AI applications in clinical practice.<h4>Objective</h4>T ...[more]

PMID: 32602844

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Project description:BackgroundAs the adoption of artificial intelligence (AI) in health care increases, it will become increasingly crucial to involve health care professionals (HCPs) in developing, validating, and implementing AI-enabled technologies. However, because of a lack of AI literacy, most HCPs are not adequately prepared for this revolution. This is a significant barrier to adopting and implementing AI that will affect patients. In addition, the limited existing AI education programs face barriers to development and implementation at various levels of medical education.ObjectiveWith a view to informing future AI education programs for HCPs, this scoping review aims to provide an overview of the types of current or past AI education programs that pertains to the programs' curricular content, modes of delivery, critical implementation factors for education delivery, and outcomes used to assess the programs' effectiveness.MethodsAfter the creation of a search strategy and keyword searches, a 2-stage screening process was conducted by 2 independent reviewers to determine study eligibility. When consensus was not reached, the conflict was resolved by consulting a third reviewer. This process consisted of a title and abstract scan and a full-text review. The articles were included if they discussed an actual training program or educational intervention, or a potential training program or educational intervention and the desired content to be covered, focused on AI, and were designed or intended for HCPs (at any stage of their career).ResultsOf the 10,094 unique citations scanned, 41 (0.41%) studies relevant to our eligibility criteria were identified. Among the 41 included studies, 10 (24%) described 13 unique programs and 31 (76%) discussed recommended curricular content. The curricular content of the unique programs ranged from AI use, AI interpretation, and cultivating skills to explain results derived from AI algorithms. The curricular topics were categorized into three main domains: cognitive, psychomotor, and affective.ConclusionsThis review provides an overview of the current landscape of AI in medical education and highlights the skills and competencies required by HCPs to effectively use AI in enhancing the quality of care and optimizing patient outcomes. Future education efforts should focus on the development of regulatory strategies, a multidisciplinary approach to curriculum redesign, a competency-based curriculum, and patient-clinician interaction.

Project description:BackgroundSince the advent of artificial intelligence (AI) in 1955, the applications of AI have increased over the years within a rapidly changing digital landscape where public expectations are on the rise, fed by social media, industry leaders, and medical practitioners. However, there has been little interest in AI in medical education until the last two decades, with only a recent increase in the number of publications and citations in the field. To our knowledge, thus far, a limited number of articles have discussed or reviewed the current use of AI in medical education.ObjectiveThis study aims to review the current applications of AI in medical education as well as the challenges of implementing AI in medical education.MethodsMedline (Ovid), EBSCOhost Education Resources Information Center (ERIC) and Education Source, and Web of Science were searched with explicit inclusion and exclusion criteria. Full text of the selected articles was analyzed using the Extension of Technology Acceptance Model and the Diffusions of Innovations theory. Data were subsequently pooled together and analyzed quantitatively.ResultsA total of 37 articles were identified. Three primary uses of AI in medical education were identified: learning support (n=32), assessment of students' learning (n=4), and curriculum review (n=1). The main reasons for use of AI are its ability to provide feedback and a guided learning pathway and to decrease costs. Subgroup analysis revealed that medical undergraduates are the primary target audience for AI use. In addition, 34 articles described the challenges of AI implementation in medical education; two main reasons were identified: difficulty in assessing the effectiveness of AI in medical education and technical challenges while developing AI applications.ConclusionsThe primary use of AI in medical education was for learning support mainly due to its ability to provide individualized feedback. Little emphasis was placed on curriculum review and assessment of students' learning due to the lack of digitalization and sensitive nature of examinations, respectively. Big data manipulation also warrants the need to ensure data integrity. Methodological improvements are required to increase AI adoption by addressing the technical difficulties of creating an AI application and using novel methods to assess the effectiveness of AI. To better integrate AI into the medical profession, measures should be taken to introduce AI into the medical school curriculum for medical professionals to better understand AI algorithms and maximize its use.

Project description:BackgroundArtificial intelligence (AI) needs to be accepted and understood by physicians and medical students, but few have systematically assessed their attitudes. We investigated clinical AI acceptance among physicians and medical students around the world to provide implementation guidance.Materials and methodsWe conducted a two-stage study, involving a foundational systematic review of physician and medical student acceptance of clinical AI. This enabled us to design a suitable web-based questionnaire which was then distributed among practitioners and trainees around the world.ResultsSixty studies were included in this systematic review, and 758 respondents from 39 countries completed the online questionnaire. Five (62.50%) of eight studies reported 65% or higher awareness regarding the application of clinical AI. Although, only 10-30% had actually used AI and 26 (74.28%) of 35 studies suggested there was a lack of AI knowledge. Our questionnaire uncovered 38% awareness rate and 20% utility rate of clinical AI, although 53% lacked basic knowledge of clinical AI. Forty-five studies mentioned attitudes toward clinical AI, and over 60% from 38 (84.44%) studies were positive about AI, although they were also concerned about the potential for unpredictable, incorrect results. Seventy-seven percent were optimistic about the prospect of clinical AI. The support rate for the statement that AI could replace physicians ranged from 6 to 78% across 40 studies which mentioned this topic. Five studies recommended that efforts should be made to increase collaboration. Our questionnaire showed 68% disagreed that AI would become a surrogate physician, but believed it should assist in clinical decision-making. Participants with different identities, experience and from different countries hold similar but subtly different attitudes.ConclusionMost physicians and medical students appear aware of the increasing application of clinical AI, but lack practical experience and related knowledge. Overall, participants have positive but reserved attitudes about AI. In spite of the mixed opinions around clinical AI becoming a surrogate physician, there was a consensus that collaborations between the two should be strengthened. Further education should be conducted to alleviate anxieties associated with change and adopting new technologies.

Project description:BACKGROUND:Medical schools in low- and middle-income countries are facing a shortage of staff, limited infrastructure, and restricted access to fast and reliable internet. Offline digital education may be an alternative solution for these issues, allowing medical students to learn at their own time and pace, without the need for a network connection. OBJECTIVE:The primary objective of this systematic review was to assess the effectiveness of offline digital education compared with traditional learning or a different form of offline digital education such as CD-ROM or PowerPoint presentations in improving knowledge, skills, attitudes, and satisfaction of medical students. The secondary objective was to assess the cost-effectiveness of offline digital education, changes in its accessibility or availability, and its unintended/adverse effects on students. METHODS:We carried out a systematic review of the literature by following the Cochrane methodology. We searched seven major electronic databases from January 1990 to August 2017 for randomized controlled trials (RCTs) or cluster RCTs. Two authors independently screened studies, extracted data, and assessed the risk of bias. We assessed the quality of evidence using the Grading of Recommendations, Assessment, Development, and Evaluations criteria. RESULTS:We included 36 studies with 3325 medical students, of which 33 were RCTs and three were cluster RCTs. The interventions consisted of software programs, CD-ROMs, PowerPoint presentations, computer-based videos, and other computer-based interventions. The pooled estimate of 19 studies (1717 participants) showed no significant difference between offline digital education and traditional learning groups in terms of students' postintervention knowledge scores (standardized mean difference=0.11, 95% CI -0.11 to 0.32; small effect size; low-quality evidence). Meta-analysis of four studies found that, compared with traditional learning, offline digital education improved medical students' postintervention skills (standardized mean difference=1.05, 95% CI 0.15-1.95; large effect size; low-quality evidence). We are uncertain about the effects of offline digital education on students' attitudes and satisfaction due to missing or incomplete outcome data. Only four studies estimated the costs of offline digital education, and none reported changes in accessibility or availability of such education or in the adverse effects. The risk of bias was predominantly high in more than half of the included studies. The overall quality of the evidence was low (for knowledge, skills, attitudes, and satisfaction) due to the study limitations and inconsistency across the studies. CONCLUSIONS:Our findings suggest that offline digital education is as effective as traditional learning in terms of medical students' knowledge and may be more effective than traditional learning in terms of medical students' skills. However, there is a need to further investigate students' attitudes and satisfaction with offline digital education as well as its cost-effectiveness, changes in its accessibility or availability, and any resulting unintended/adverse effects.

Project description:BACKGROUND:Positive economic impact is a key decision factor in making the case for or against investing in an artificial intelligence (AI) solution in the health care industry. It is most relevant for the care provider and insurer as well as for the pharmaceutical and medical technology sectors. Although the broad economic impact of digital health solutions in general has been assessed many times in literature and the benefit for patients and society has also been analyzed, the specific economic impact of AI in health care has been addressed only sporadically. OBJECTIVE:This study aimed to systematically review and summarize the cost-effectiveness studies dedicated to AI in health care and to assess whether they meet the established quality criteria. METHODS:In a first step, the quality criteria for economic impact studies were defined based on the established and adapted criteria schemes for cost impact assessments. In a second step, a systematic literature review based on qualitative and quantitative inclusion and exclusion criteria was conducted to identify relevant publications for an in-depth analysis of the economic impact assessment. In a final step, the quality of the identified economic impact studies was evaluated based on the defined quality criteria for cost-effectiveness studies. RESULTS:Very few publications have thoroughly addressed the economic impact assessment, and the economic assessment quality of the reviewed publications on AI shows severe methodological deficits. Only 6 out of 66 publications could be included in the second step of the analysis based on the inclusion criteria. Out of these 6 studies, none comprised a methodologically complete cost impact analysis. There are two areas for improvement in future studies. First, the initial investment and operational costs for the AI infrastructure and service need to be included. Second, alternatives to achieve similar impact must be evaluated to provide a comprehensive comparison. CONCLUSIONS:This systematic literature analysis proved that the existing impact assessments show methodological deficits and that upcoming evaluations require more comprehensive economic analyses to enable economic decisions for or against implementing AI technology in health care.

Dataset Information

Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review.

Publications

Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review.

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