A Systematic Review of the Use of mHealth in Oral Health Education among Older Adults

Performance of Generative Artificial Intelligence in Dental Licensing Examinations

OBJECTIVES

Generative artificial intelligence (GenAI), including large language models (LLMs), has vast potential applications in health care and education. However, it is unclear how proficient LLMs are in interpreting written input and providing accurate answers in dentistry. This study aims to investigate the accuracy of GenAI in answering questions from dental licensing examinations.

METHODS

A total of 1461 multiple-choice questions from question books for the US and the UK dental licensing examinations were input into 2 versions of ChatGPT 3.5 and 4.0. The passing rates of the US and UK dental examinations were 75.0% and 50.0%, respectively. The performance of the 2 versions of GenAI in individual examinations and dental subjects was analysed and compared.

RESULTS

ChatGPT 3.5 correctly answered 68.3% (n = 509) and 43.3% (n = 296) of questions from the US and UK dental licensing examinations, respectively. The scores for ChatGPT 4.0 were 80.7% (n = 601) and 62.7% (n = 429), respectively. ChatGPT 4.0 passed both written dental licensing examinations, whilst ChatGPT 3.5 failed. ChatGPT 4.0 answered 327 more questions correctly and 102 incorrectly compared to ChatGPT 3.5 when comparing the 2 versions.

CONCLUSIONS

The newer version of GenAI has shown good proficiency in answering multiple-choice questions from dental licensing examinations. Whilst the more recent version of GenAI generally performed better, this observation may not hold true in all scenarios, and further improvements are necessary. The use of GenAI in dentistry will have significant implications for dentist-patient communication and the training of dental professionals.

Mobile health in the aging surgical patient

Mobile health includes the use of mobile devices, patient monitoring devices, and digital assistants to improve the delivery of healthcare. Aging surgical patients (ie, 65 years and older) represent a unique patient population that demands increased resources to prepare for surgery and optimize recovery. Mobile health has the potential to improve surgical patient outcomes by increasing the accessibility of personalized care and reducing costs. However, there are some challenges to consider when using mobile health in older surgical patients, such as technological literacy, visual and hearing impairment, and cognitive changes before or after anesthesia. Despite the rapid uptake of mobile health in medical specialties, its application in the surgical field is gradual. The complexity of aging surgical patients requires surgical care teams, surgical leaders, and healthcare policymakers to consider unique solutions, such as mobile health, to address this growing population's needs before and after surgery. This article will discuss the potential benefits and challenges of mobile health among aging surgical patients, as well as opportunities to support these patients and families with customizable tools to meet their preferences and needs.