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

[ChatGPT and the German board examination for ophthalmology: an evaluation]

PURPOSE

In recent years artificial intelligence (AI), as a new segment of computer science, has also become increasingly more important in medicine. The aim of this project was to investigate whether the current version of ChatGPT (ChatGPT 4.0) is able to answer open questions that could be asked in the context of a German board examination in ophthalmology.

METHODS

After excluding image-based questions, 10 questions from 15 different chapters/topics were selected from the textbook 1000 questions in ophthalmology (1000 Fragen Augenheilkunde 2nd edition, 2014). ChatGPT was instructed by means of a so-called prompt to assume the role of a board certified ophthalmologist and to concentrate on the essentials when answering. A human expert with considerable expertise in the respective topic, evaluated the answers regarding their correctness, relevance and internal coherence. Additionally, the overall performance was rated by school grades and assessed whether the answers would have been sufficient to pass the ophthalmology board examination.

RESULTS

The ChatGPT would have passed the board examination in 12 out of 15 topics. The overall performance, however, was limited with only 53.3% completely correct answers. While the correctness of the results in the different topics was highly variable (uveitis and lens/cataract 100%; optics and refraction 20%), the answers always had a high thematic fit (70%) and internal coherence (71%).

CONCLUSION

The fact that ChatGPT 4.0 would have passed the specialist examination in 12 out of 15 topics is remarkable considering the fact that this AI was not specifically trained for medical questions; however, there is a considerable performance variability between the topics, with some serious shortcomings that currently rule out its safe use in clinical practice.

A Nordic survey on artificial intelligence in the radiography profession - Is the profession ready for a culture change?

INTRODUCTION

The impact of artificial intelligence (AI) on the radiography profession remains uncertain. Although AI has been increasingly used in clinical radiography, the perspectives of the radiography professionals in Nordic countries have yet to be examined. The primary aim was to examine views of Nordic radiographers 'on AI, with focus on perspectives, engagement, and knowledge of AI.

METHODS

Radiographers from Denmark, Norway, Sweden, Iceland, Greenland, and the Faroe Island were invited through social media platforms to participate in an online survey from March to June 2023. The survey encompassed 29-items and included 4 sections a) demographics, b) barriers and enablers on AI, c) perspectives and experiences of AI and d) knowledge of AI in radiography. Edgars Schein's model of organizational culture was employed to analyse Nordic radiographers' perspectives on AI.

RESULTS

Overall, a total of 421 respondents participated in the survey. A majority were positive/somewhat positive towards AI in radiography e.g., 77.9 % (n = 342) thought that AI would have a positive effect on the profession, and 26% thought that AI would reduce the administrative workload. Most radiographers agreed or strongly agreed that clinicians may have access to AI generated reports (76.8 %, n = 297). Nevertheless, a total of 86 (20.1%) agree or somewhat agreed that AI a potential risk for radiography.

CONCLUSION

Nordic radiographers are generally positive towards AI, yet uncertainties regarding its implementation persist. The findings underscore the importance of understanding these challenges for the responsible integration of AI systems. Carefully weighing the expected influence of AI against key incentives will support a seamless integration of AI for the benefit not just of the patients, but also of the radiography profession.

IMPLICATIONS FOR PRACTICE

Understanding incentives factors and barriers can help address uncertainties during implementation of AI in clinical practice.

Artificial intelligence utilization in the healthcare setting: perceptions of the public in the UAE

Understanding the use of AI in healthcare is essential for the successful implementation of AI-driven healthcare solutions. The aim of this study was to evaluate public perception of AI utilization in healthcare settings. A validated questionnaire assessed general perceptions towards AI utilization, the use of AI by physician , and the use of AI by pharmacists . The study included 770 participants. The median perception score indicated an unfavorable attitude. Participants who had lower education level and those with no employment had a significantly lower perception scores than their counterpart. Participants who reported low income and those who visited the pharmacy five to ten times on average had a higher perception than their counterparts did. The reported negative perception necessitates the implementation of education campaigns to improve AI literacy and dispel any misconceptions and concerns, particularly among individuals with low education, high income, unemployment, and frequent pharmacy visits.

The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature

Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes.

Future Implications of Artificial Intelligence in Medical Education

Artificial intelligence has experienced explosive growth in the past year that will have implications in all aspects of our lives, including medicine. In order to train a physician workforce that understands these new advancements, medical educators must take steps now to ensure that physicians are adequately trained in medical school, residency, and fellowship programs to become proficient in the usage of artificial intelligence in medical practice. This manuscript discusses the various considerations that leadership within medical training programs should be mindful of when deciding how to best integrate artificial intelligence into their curricula.