The perceptions of medical physicists towards relevance and impact of artificial intelligence

Exploring clinical specialists' perspectives on the future role of AI: evaluating replacement perceptions, benefits, and drawbacks

BACKGROUND OF STUDY

Over the past few decades, the utilization of Artificial Intelligence (AI) has surged in popularity, and its application in the medical field is witnessing a global increase. Nevertheless, the implementation of AI-based healthcare solutions has been slow in developing nations like Pakistan. This unique study aims to assess the opinion of clinical specialists on the future replacement of AI, its associated benefits, and its drawbacks in form southern region of Pakistan.

MATERIAL AND METHODS

A cross-sectional selective study was conducted from 140 clinical specialists (Surgery = 24, Pathology = 31, Radiology = 35, Gynecology = 35, Pediatric = 17) from the neglected southern Punjab region of Pakistan. The study was analyzed using χ2 - the test of association and the nexus between different factors was examined by multinomial logistic regression.

RESULTS

Out of 140 respondents, 34 (24.3%) believed hospitals were ready for AI, while 81 (57.9%) disagreed. Additionally, 42(30.0%) were concerned about privacy violations, and 70(50%) feared AI could lead to unemployment. Specialists with less than 6 years of experience are more likely to embrace AI (p = 0.0327, OR = 3.184, 95% C.I; 0.262, 3.556) and those who firmly believe that AI knowledge will not replace their future tasks exhibit a lower likelihood of accepting AI (p = 0.015, OR = 0.235, 95% C.I: (0.073, 0.758). Clinical specialists who perceive AI as a technology that encompasses both drawbacks and benefits demonstrated a higher likelihood of accepting its adoption (p = 0.084, OR = 2.969, 95% C.I; 0.865, 5.187).

CONCLUSION

Clinical specialists have embraced AI as the future of the medical field while acknowledging concerns about privacy and unemployment.

Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine

BACKGROUND

The current applications of artificial intelligence (AI) in medicine continue to attract the attention of medical students. This study aimed to identify undergraduate medical students' attitudes toward AI in medicine, explore present AI-related training opportunities, investigate the need for AI inclusion in medical curricula, and determine preferred methods for teaching AI curricula.

METHODS

This study uses a mixed-method cross-sectional design, including a quantitative study and a qualitative study, targeting Palestinian undergraduate medical students in the academic year 2022-2023. In the quantitative part, we recruited a convenience sample of undergraduate medical students from universities in Palestine from June 15, 2022, to May 30, 2023. We collected data by using an online, well-structured, and self-administered questionnaire with 49 items. In the qualitative part, 15 undergraduate medical students were interviewed by trained researchers. Descriptive statistics and an inductive content analysis approach were used to analyze quantitative and qualitative data, respectively.

RESULTS

From a total of 371 invitations sent, 362 responses were received (response rate = 97.5%), and 349 were included in the analysis. The mean age of participants was 20.38 ± 1.97, with 40.11% (140) in their second year of medical school. Most participants (268, 76.79%) did not receive formal education on AI before or during medical study. About two-thirds of students strongly agreed or agreed that AI would become common in the future (67.9%, 237) and would revolutionize medical fields (68.7%, 240). Participants stated that they had not previously acquired training in the use of AI in medicine during formal medical education (260, 74.5%), confirming a dire need to include AI training in medical curricula (247, 70.8%). Most participants (264, 75.7%) think that learning opportunities for AI in medicine have not been adequate; therefore, it is very important to study more about employing AI in medicine (228, 65.3%). Male students (3.15 ± 0.87) had higher perception scores than female students (2.81 ± 0.86) (p < 0.001). The main themes that resulted from the qualitative analysis of the interview questions were an absence of AI learning opportunities, the necessity of including AI in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to AI in medical fields.

CONCLUSION

Medical students lack access to educational opportunities for AI in medicine; therefore, AI should be included in formal medical curricula in Palestine.

Radiology Residents' Perceptions of Artificial Intelligence: Nationwide Cross-Sectional Survey Study

BACKGROUND

Artificial intelligence (AI) is transforming various fields, with health care, especially diagnostic specialties such as radiology, being a key but controversial battleground. However, there is limited research systematically examining the response of "human intelligence" to AI.

OBJECTIVE

This study aims to comprehend radiologists' perceptions regarding AI, including their views on its potential to replace them, its usefulness, and their willingness to accept it. We examine the influence of various factors, encompassing demographic characteristics, working status, psychosocial aspects, personal experience, and contextual factors.

METHODS

Between December 1, 2020, and April 30, 2021, a cross-sectional survey was completed by 3666 radiology residents in China. We used multivariable logistic regression models to examine factors and associations, reporting odds ratios (ORs) and 95% CIs.

RESULTS

In summary, radiology residents generally hold a positive attitude toward AI, with 29.90% (1096/3666) agreeing that AI may reduce the demand for radiologists, 72.80% (2669/3666) believing AI improves disease diagnosis, and 78.18% (2866/3666) feeling that radiologists should embrace AI. Several associated factors, including age, gender, education, region, eye strain, working hours, time spent on medical images, resilience, burnout, AI experience, and perceptions of residency support and stress, significantly influence AI attitudes. For instance, burnout symptoms were associated with greater concerns about AI replacement (OR 1.89; P<.001), less favorable views on AI usefulness (OR 0.77; P=.005), and reduced willingness to use AI (OR 0.71; P<.001). Moreover, after adjusting for all other factors, perceived AI replacement (OR 0.81; P<.001) and AI usefulness (OR 5.97; P<.001) were shown to significantly impact the intention to use AI.

CONCLUSIONS

This study profiles radiology residents who are accepting of AI. Our comprehensive findings provide insights for a multidimensional approach to help physicians adapt to AI. Targeted policies, such as digital health care initiatives and medical education, can be developed accordingly.

Artificial intelligence in medicine: A comprehensive survey of medical doctor's perspectives in Portugal

Artificial Intelligence (AI) is increasingly influential across various sectors, including healthcare, with the potential to revolutionize clinical practice. However, risks associated with AI adoption in medicine have also been identified. Despite the general understanding that AI will impact healthcare, studies that assess the perceptions of medical doctors about AI use in medicine are still scarce. We set out to survey the medical doctors licensed to practice medicine in Portugal about the impact, advantages, and disadvantages of AI adoption in clinical practice. We designed an observational, descriptive, cross-sectional study with a quantitative approach and developed an online survey which addressed the following aspects: impact on healthcare quality of the extraction and processing of health data via AI; delegation of clinical procedures on AI tools; perception of the impact of AI in clinical practice; perceived advantages of using AI in clinical practice; perceived disadvantages of using AI in clinical practice and predisposition to adopt AI in professional activity. Our sample was also subject to demographic, professional and digital use and proficiency characterization. We obtained 1013 valid, fully answered questionnaires (sample representativeness of 99%, confidence level (p< 0.01), for the total universe of medical doctors licensed to practice in Portugal). Our results reveal that, in general terms, the medical community surveyed is optimistic about AI use in medicine and are predisposed to adopt it while still aware of some disadvantages and challenges to AI use in healthcare. Most medical doctors surveyed are also convinced that AI should be part of medical formation. These findings contribute to facilitating the professional integration of AI in medical practice in Portugal, aiding the seamless integration of AI into clinical workflows by leveraging its perceived strengths according to healthcare professionals. This study identifies challenges such as gaps in medical curricula, which hinder the adoption of AI applications due to inadequate digital health training. Due to high professional integration in the healthcare sector, particularly within the European Union, our results are also relevant for other jurisdictions and across diverse healthcare systems.

Psychometric properties of the persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS)

INTRODUCTION

There are numerous cases where artificial intelligence (AI) can be applied to improve the outcomes of medical education. The extent to which medical practitioners and students are ready to work and leverage this paradigm is unclear in Iran. This study investigated the psychometric properties of a Persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) developed by Karaca, et al. in 2021. In future studies, the medical AI readiness for Iranian medical students could be investigated using this scale, and effective interventions might be planned and implemented according to the results.

METHODS

In this study, 502 medical students (mean age 22.66(± 2.767); 55% female) responded to the Persian questionnaire in an online survey. The original questionnaire was translated into Persian using a back translation procedure, and all participants completed the demographic component and the entire MAIRS-MS. Internal and external consistencies, factor analysis, construct validity, and confirmatory factor analysis were examined to analyze the collected data. A P ≤ 0.05 was considered as the level of statistical significance.

RESULTS

Four subscales emerged from the exploratory factor analysis (Cognition, Ability, Vision, and Ethics), and confirmatory factor analysis confirmed the four subscales. The Cronbach alpha value for internal consistency was 0.944 for the total scale and 0.886, 0.905, 0.865, and 0.856 for cognition, ability, vision, and ethics, respectively.

CONCLUSIONS

The Persian version of MAIRS-MS was fairly equivalent to the original one regarding the conceptual and linguistic aspects. This study also confirmed the validity and reliability of the Persian version of MAIRS-MS. Therefore, the Persian version can be a suitable and brief instrument to assess Iranian Medical Students' readiness for medical artificial intelligence.

Attitudes, knowledge, and skills towards artificial intelligence among healthcare students: A systematic review

Background and Aims

This systematic review examined healthcare students' attitudes, knowledge, and skill in Artificial Intelligence (AI).

Methods

On August 3, 2022, studies were retrieved from the PubMed, Embase, Scopus, and Web of Science databases. Preferred Reporting Items for Systematic Reviews and Meta-Analyses recommendations were followed. We included cross-sectional studies that examined healthcare students' knowledge, attitudes, skills, and perceptions of AI in this review. Using the eligibility requirements as a guide, titles and abstracts were screened. Complete texts were then retrieved and independently reviewed per the eligibility requirements. To collect data, a standardized form was used.

Results

Of the 38 included studies, 29 (76%) of healthcare students had a positive and promising attitude towards AI in the clinical profession and its use in he future; however, in nine of the studies (24%), students considered AI a threat to healthcare fields and had a negative attitude towards it. Furthermore, 26 studies evaluated the knowledge of healthcare students about AI. Among these, 18 studies evaluated the level of student knowledge as low (50%). On the other hand, in six of the studies, students' high knowledge of AI was reported, and two of the studies reported average student general knowledge (almost 50%). Of the six studies, four (67%) of the students had very low skills, so they stated that they had never worked with AI.

Conclusion

Evidence from this review shows that healthcare students had a positive and promising attitude towards AI in medicine; however, most students had low knowledge and limited skills in working with AI. Face-to-face instruction, training manuals, and detailed instructions are therefore crucial for implementing and comprehending how AI technology works and raising students' knowledge of the advantages of AI.

Malaysian Medical Students' Attitudes and Readiness Toward AI (Artificial Intelligence): A Cross-Sectional Study

OBJECTIVES

The Malaysian health ministry has started introducing artificial intelligence (AI) technology to aid local healthcare delivery. This study aims to survey Malaysian medical students' attitudes toward AI and evaluate their readiness to work with medical AI technology.

METHODS

An online questionnaire on Google Forms was distributed to all 31 medical schools in Malaysia. The questionnaire consists of 3 sections: the first part surveyed the participants' demographics, the second assessed the participants' attitudes toward AI, and the final part utilizes the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) scale to evaluate their AI readiness.

RESULTS

Three hundred and one students from 17 universities in Malaysia responded to the questionnaire. 87.36% of students agreed that AI will play an essential role in healthcare; 32.55% of students were less likely to consider a career in radiology due to the advancement of AI. The majority of students (71%) felt that teaching in AI will benefit their careers, while 69.44% agreed that all students should receive teaching in AI. Around 44.5% of students felt that they will possess the knowledge required to work with AI upon graduation. On the MAIRS-MS scale, students had a mean score of 21 of 40 for the cognitive factor, 25 of 40 for the ability factor, 10 of 15 for the vision factor, and 11 of 15 for the ethics factor. Overall, Malaysian students had a mean total score of 67±14.8 out of 110.

CONCLUSION

Malaysian medical students have demonstrated awareness of AI and a willingness to learn more about it. More work needs to be done to improve students' AI readiness, particularly their knowledge and application of AI technology. Malaysian universities should start to work on incorporating AI teaching into their curricula.

Artificial intelligence for oral and dental healthcare: Core education curriculum

OBJECTIVES

Artificial intelligence (AI) is swiftly entering oral health services and dentistry, while most providers show limited knowledge and skills to appraise dental AI applications. We aimed to define a core curriculum for both undergraduate and postgraduate education, establishing a minimum set of outcomes learners should acquire when taught about oral and dental AI.

METHODS

Existing curricula and other documents focusing on literacy of medical professionals around AI were screened and relevant items extracted. Items were scoped and adapted using expert interviews with members of the IADR's e-oral health group, the ITU/WHO's Focus Group AI for Health and the Association for Dental Education in Europe. Learning outcome levels were defined and each item assigned to a level. Items were systematized into domains and a curricular structure defined. The resulting curriculum was consented using an online Delphi process.

RESULTS

Four domains of learning outcomes emerged, with most outcomes being on the "knowledge" level: (1) Basic definitions and terms, the reasoning behind AI and the principle of machine learning, the idea of training, validating and testing models, the definition of reference tests, the contrast between dynamic and static AI, and the problem of AI being a black box and requiring explainability should be known. (2) Use cases, the required types of AI to address them, and the typical setup of AI software for dental purposes should be taught. (3) Evaluation metrics, their interpretation, the relevant impact of AI on patient or societal health outcomes and associated examples should be considered. (4) Issues around generalizability and representativeness, explainability, autonomy and accountability and the need for governance should be highlighted.

CONCLUSION

Both educators and learners should consider this core curriculum during planning, conducting and evaluating oral and dental AI education.

CLINICAL SIGNIFICANCE

A core curriculum on oral and dental AI may help to increase oral and dental healthcare providers' literacy around AI, allowing them to critically appraise AI applications and to use them consciously and on an informed basis.

Artificial Intelligence and Positron Emission Tomography Imaging Workflow:: Technologists' Perspective

Artificial intelligence (AI) can enhance the efficiency of medical imaging quality control and clinical documentation, provide clinical decision support, and increase image acquisition and processing quality. A clear understanding of the basic tenets of these technologies and their impact will enable nuclear medicine technologists to train for performing advanced imaging tasks. AI-enabled medical devices' anticipated role and impact on routine nuclear medicine workflow (scheduling, quality control, check-in, radiotracer injection, waiting room, image planning, image acquisition, image post-processing) is reviewed in this article. With the assistance of AI, newly compiled patient imaging data can be customized to encompass personalized risk assessments of patients' disease burden, along with the development of individualized treatment plans. Nuclear medicine technologists will continue to play a crucial role on the medical team, collaborating with patients and radiologists to improve each patient's imaging experience and supervising the performance of integrated AI applications.