Implications of ChatGPT in Public Health Dentistry: A Systematic Review

Performance of ChatGPT 3.5 and 4 on U.S. dental examinations: the INBDE, ADAT, and DAT

Purpose

Recent advancements in artificial intelligence (AI), particularly tools such as ChatGPT developed by OpenAI, a U.S.-based AI research organization, have transformed the healthcare and education sectors. This study investigated the effectiveness of ChatGPT in answering dentistry exam questions, demonstrating its potential to enhance professional practice and patient care.

Materials and Methods

This study assessed the performance of ChatGPT 3.5 and 4 on U.S. dental exams - specifically, the Integrated National Board Dental Examination (INBDE), Dental Admission Test (DAT), and Advanced Dental Admission Test (ADAT) - excluding image-based questions. Using customized prompts, ChatGPT's answers were evaluated against official answer sheets.

Results

ChatGPT 3.5 and 4 were tested with 253 questions from the INBDE, ADAT, and DAT exams. For the INBDE, both versions achieved 80% accuracy in knowledge-based questions and 66-69% in case history questions. In ADAT, they scored 66-83% in knowledge-based and 76% in case history questions. ChatGPT 4 excelled on the DAT, with 94% accuracy in knowledge-based questions, 57% in mathematical analysis items, and 100% in comprehension questions, surpassing ChatGPT 3.5's rates of 83%, 31%, and 82%, respectively. The difference was significant for knowledge-based questions (P=0.009). Both versions showed similar patterns in incorrect responses.

Conclusion

Both ChatGPT 3.5 and 4 effectively handled knowledge-based, case history, and comprehension questions, with ChatGPT 4 being more reliable and surpassing the performance of 3.5. ChatGPT 4's perfect score in comprehension questions underscores its trainability in specific subjects. However, both versions exhibited weaker performance in mathematical analysis, suggesting this as an area for improvement.

Comparison of the Performance of ChatGPT, Claude and Bard in Support of Myopia Prevention and Control

Purpose

 Chatbots, which are based on large language models, are increasingly being used in public health. However, the effectiveness of chatbot responses has been debated, and their performance in myopia prevention and control has not been fully explored. This study aimed to evaluate the effectiveness of three well-known chatbots-ChatGPT, Claude, and Bard-in responding to public health questions about myopia.

Methods

 Nineteen public health questions about myopia (including three topics of policy, basics and measures) were responded individually by three chatbots. After shuffling the order, each chatbot response was independently rated by 4 raters for comprehensiveness, accuracy and relevance.

Results

 The study's questions have undergone reliable testing. There was a significant difference among the word count responses of all 3 chatbots. From most to least, the order was ChatGPT, Bard, and Claude. All 3 chatbots had a composite score above 4 out of 5. ChatGPT scored the highest in all aspects of the assessment. However, all chatbots exhibit shortcomings, such as giving fabricated responses.

Conclusion

 Chatbots have shown great potential in public health, with ChatGPT being the best. The future use of chatbots as a public health tool will require rapid development of standards for their use and monitoring, as well as continued research, evaluation and improvement of chatbots.

Assessment of Patient Satisfaction and Oral Health-Related Quality of Life Following Full Mouth Rehabilitation with Implant-Supported Prostheses

Background

The concept of success is typically established by the researcher or the practitioner rather than the patient, who has the greatest stake in the outcome of the prosthodontics therapy.

Aim

To assess patient satisfaction and oral health-related quality of life (OHRQoL) in following full mouth rehabilitation with implant-supported prostheses (ISP).

Methods and Materials

Thirty-two patients who underwent full mouth rehabilitation with ISP were included in this study. The Oral Health Impact Profile-14 (OHIP-14) questionnaire was used for the assessment of patient satisfaction and OHRQoL following full mouth rehabilitation with ISP.

Results

The functional limitation among study participants before, 1 month, and 3 months after ISP was 2.1 ± 1.3, 1.5 ± 0.8, and 1.4 ± 0.9, respectively. There was a significant reduction in functional limitation, psychological disability, and social disability before and 3 months after ISP.

Conclusion

There is a significant improvement in patient satisfaction and OHRQoL in following full mouth rehabilitation with ISP.

Art or Artifact: Evaluating the Accuracy, Appeal, and Educational Value of AI-Generated Imagery in DALL·E 3 for Illustrating Congenital Heart Diseases

Artificial Intelligence (AI), particularly AI-Generated Imagery, has the potential to impact medical and patient education. This research explores the use of AI-generated imagery, from text-to-images, in medical education, focusing on congenital heart diseases (CHD). Utilizing ChatGPT's DALL·E 3, the research aims to assess the accuracy and educational value of AI-created images for 20 common CHDs. In this study, we utilized DALL·E 3 to generate a comprehensive set of 110 images, comprising ten images depicting the normal human heart and five images for each of the 20 common CHDs. The generated images were evaluated by a diverse group of 33 healthcare professionals. This cohort included cardiology experts, pediatricians, non-pediatric faculty members, trainees (medical students, interns, pediatric residents), and pediatric nurses. Utilizing a structured framework, these professionals assessed each image for anatomical accuracy, the usefulness of in-picture text, its appeal to medical professionals, and the image's potential applicability in medical presentations. Each item was assessed on a Likert scale of three. The assessments produced a total of 3630 images' assessments. Most AI-generated cardiac images were rated poorly as follows: 80.8% of images were rated as anatomically incorrect or fabricated, 85.2% rated to have incorrect text labels, 78.1% rated as not usable for medical education. The nurses and medical interns were found to have a more positive perception about the AI-generated cardiac images compared to the faculty members, pediatricians, and cardiology experts. Complex congenital anomalies were found to be significantly more predicted to anatomical fabrication compared to simple cardiac anomalies. There were significant challenges identified in image generation. Based on our findings, we recommend a vigilant approach towards the use of AI-generated imagery in medical education at present, underscoring the imperative for thorough validation and the importance of collaboration across disciplines. While we advise against its immediate integration until further validations are conducted, the study advocates for future AI-models to be fine-tuned with accurate medical data, enhancing their reliability and educational utility.

Creating and Testing a New Computer Vision System for Detecting Dental Problems in Orthodontic Patients

Aim

The research project focuses on the creation and assessment of an innovative computer vision system designed to identify dental irregularities in individuals undergoing orthodontic treatment.

Materials and Methods

To establish the computer vision system, a comprehensive dataset of dental images was collected, encompassing various orthodontic cases. The system's algorithm was trained to recognize patterns indicative of common dental anomalies, such as malocclusions, spacing issues, and misalignments. Rigorous testing and refinement of the algorithm were conducted to enhance its accuracy and reliability.

Results

The validation of the system was carried out using the dental records and images of the 40 patients. The computer vision system's performance was evaluated against assessments made by experienced orthodontists. The results demonstrated a commendable level of concurrence between the system's automated detections and the orthodontists' evaluations, suggesting its potential as a valuable diagnostic tool.

Conclusion

In conclusion, the development and validation of this novel computer vision system exhibit promising outcomes in its ability to automatically detect dental anomalies in orthodontic patients.

Artificial intelligence in oral health surveillance among under-served communities

A sizable percentage of the population in India still does not have easy access to dental facilities. Therefore, it is of interest to document the role of artificial intelligence (AI) in oral surveillance of underserved communities. Available data shows that AI makes it possible to screen, diagnose, track, prioritize, and monitor dental patients remotely via smart devices. As a result, dentists won't have to deal with simple situations that only require standard treatments; freeing them up to focus on more complicated cases. Additionally, this would allow dentists to reach a broader, more underprivileged population in difficult-to-reach places. AI fracture recognition and categorization performance has shown promise in preliminary testing. Methods for detecting aberrations are frequently employed in public health practise and research continues to be focused on them.

Effect of herbal mouthrinsein dental ultrasonic scalers among Indians

The use of herbal mouthrinse is gaining momentum in recent years. Therefore, it is of interest to evaluate the effect of 2 herbal mouthrinse (curcumin, cinnamon) in comparison with2 conventional mouthrinse (povidone iodine, chlorhexidine) when used as coolant in dental ultrasonic scalers. Hence, 200 participants were included in this study. Analysis of gingival index, periodontal index at baseline and one month follow up was completed. The inhibitory effects of both conventional and herbal mouth rinse in gingival health are similar. However, cinnamon and curcumin owing to its minimal adverse effects and low cost is useful as an alternative to chlorhexidine for reducing bacterial load in dental aerosols produced due to ultrasonic scalers.

A search-based geographic metadata curation pipeline to refine sequencing institution information and support public health

Background

The National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) has amassed a vast reservoir of genetic data since its inception in 2007. These public data hold immense potential for supporting pathogen surveillance and control. However, the lack of standardized metadata and inconsistent submission practices in SRA may impede the data's utility in public health.

Methods

To address this issue, we introduce the Search-based Geographic Metadata Curation (SGMC) pipeline. SGMC utilized Python and web scraping to extract geographic data of sequencing institutions from NCBI SRA in the Cloud and its website. It then harnessed ChatGPT to refine the sequencing institution and location assignments. To illustrate the pipeline's utility, we examined the geographic distribution of the sequencing institutions and their countries relevant to polio eradication and categorized them.

Results

SGMC successfully identified 7,649 sequencing institutions and their global locations from a random selection of 2,321,044 SRA accessions. These institutions were distributed across 97 countries, with strong representation in the United States, the United Kingdom and China. However, there was a lack of data from African, Central Asian, and Central American countries, indicating potential disparities in sequencing capabilities. Comparison with manually curated data for U.S. institutions reveals SGMC's accuracy rates of 94.8% for institutions, 93.1% for countries, and 74.5% for geographic coordinates.

Conclusion

SGMC may represent a novel approach using a generative AI model to enhance geographic data (country and institution assignments) for large numbers of samples within SRA datasets. This information can be utilized to bolster public health endeavors.