Can large language models provide secondary reliable opinion on treatment options for dermatological diseases?

OBJECTIVE: To investigate the consistency and reliability of medication recommendations provided by ChatGPT for common dermatological conditions, highlighting the potential for ChatGPT to offer second opinions in patient treatment while also delineating possible limitations. MATERIALS AND METHODS: In this mixed-methods study, we used survey questions in April 2023 for drug recommendations generated by ChatGPT with data from secondary databases, that is, Taiwan's National Health Insurance Research Database and an US medical center database, and validated by dermatologists. The methodology included preprocessing queries, executing them multiple times, and evaluating ChatGPT responses against the databases and dermatologists. The ChatGPT-generated responses were analyzed statistically in a disease-drug matrix, considering disease-medication associations (Q-value) and expert evaluation. RESULTS: ChatGPT achieved a high 98.87% dermatologist approval rate for common dermatological medication recommendations. We evaluated its drug suggestions using the Q-value, showing that human expert validation agreement surpassed Q-value cutoff-based agreement. Varying cutoff values for disease-medication associations, a cutoff of 3 achieved 95.14% accurate prescriptions, 5 yielded 85.42%, and 10 resulted in 72.92%. While ChatGPT offered accurate drug advice, it occasionally included incorrect ATC codes, leading to issues like incorrect drug use and type, nonexistent codes, repeated errors, and incomplete medication codes. CONCLUSION: ChatGPT provides medication recommendations as a second opinion in dermatology treatment, but its reliability and comprehensiveness need refinement for greater accuracy. In the future, integrating a medical domain-specific knowledge base for training and ongoing optimization will enhance the precision of ChatGPT's results.

Facial Mimicry and Doctor-Patient Satisfaction: The Feasibility of Artificial Empathy in a Clinical Video Data.

Good nonverbal communication between doctor and patient is essential for achieving a successful and therapeutic doctor-patient relationship. Increasing evidence has shown that nonverbal communication mimicry, particularly facial mimicry, where one mirrors another's facial expressions, is linked to empathy and emotion recognition. Empathy is also the key driver of patient satisfaction. This study explores how facial expressions and facial mimicry influence doctor-patient satisfaction during a clinical encounter. We used a facial emotion recognition-based artificial empathy model to analyze 315 recorded clinical video data of doctors and patients in a dermatology outpatient clinic. The results show a significant negative correlation between patients' emotions of sadness and neutral and doctor satisfaction, but no correlation between the duration of doctors mimicking patient emotions and patient satisfaction. These findings provide valuable insights into the future design of systems that can further enhance clinician awareness to maintain communication skills in the search for better doctor-patient satisfaction.

Emotion recognition in doctor-patient interactions from real-world clinical video database: Initial development of artificial empathy.

BACKGROUND AND OBJECTIVE: The promising use of artificial intelligence (AI) to emulate human empathy may help a physician engage with a more empathic doctor-patient relationship. This study demonstrates the application of artificial empathy based on facial emotion recognition to evaluate doctor-patient relationships in clinical practice. METHODS: A prospective study used recorded video data of doctor-patient clinical encounters in dermatology outpatient clinics, Taipei Municipal Wanfang Hospital, and Taipei Medical University Hospital collected from March to December 2019. Two cameras recorded the facial expressions of four doctors and 348 adult patients during regular clinical practice. Facial emotion recognition was used to analyze the basic emotions of doctors and patients with a temporal resolution of 1 second. In addition, a physician-patient satisfaction questionnaire was administered after each clinical session, and two standard patients gave impartial feedback to avoid bias. RESULTS: Data from 326 clinical session videos showed that (1) Doctors expressed more emotions than patients (t [326] > = 2.998, p < = 0.003), including anger, happiness, disgust, and sadness; the only emotion that patients showed more than doctors was surprise (t [326] = -4.428, p < .001) (p < .001). (2) Patients felt happier during the latter half of the session (t [326] = -2.860, p = .005), indicating a good doctor-patient relationship. CONCLUSIONS: Artificial empathy can offer objective observations on how doctors' and patients' emotions change. With the ability to detect emotions in 3/4 view and profile images, artificial empathy could be an accessible evaluation tool to study doctor-patient relationships in practical clinical settings.

SlimMe, a Chatbot With Artificial Empathy for Personal Weight Management: System Design and Finding.

As the obesity rate continues to increase persistently, there is an urgent need to develop an effective weight loss management strategy. Nowadays, the development of artificial intelligence (AI) and cognitive technologies coupled with the rapid spread of messaging platforms and mobile technology with easier access to internet technology offers professional dietitians an opportunity to provide extensive monitoring support to their clients through a chatbot with artificial empathy. This study aimed to design a chatbot with artificial empathic motivational support for weight loss called "SlimMe" and investigate how people react to a diet bot. The SlimMe infrastructure was built using Dialogflow as the natural language processing (NLP) platform and LINE mobile messenger as the messaging platform. We proposed a text-based emotion analysis to simulate artificial empathy responses to recognize the user's emotion. A preliminary evaluation was performed to investigate the early-stage user experience after a 7-day simulation trial. The result revealed that having an artificially empathic diet bot for weight loss management is a fun and exciting experience. The use of emoticons, stickers, and GIF images makes the chatbot response more interactive. Moreover, the motivational support and persuasive messaging features enable the bot to express more empathic and engaging responses to the user. In total, there were 1,007 bot responses from 892 user input messages. Of these, 67.38% (601/1,007) of the chatbot-generated responses were accurate to a relevant user request, 21.19% (189/1,007) inaccurate responses to a relevant request, and 10.31% (92/1,007) accurate responses to an irrelevant request. Only 1.12% (10/1,007) of the chatbot does not answer. We present the design of an artificially empathic diet bot as a friendly assistant to help users estimate their calorie intake and calories burned in a more interactive and engaging way. To our knowledge, this is the first chatbot designed with artificial empathy features, and it looks very promising in promoting long-term weight management. More user interactions and further data training and validation enhancement will improve the bot's in-built knowledge base and emotional intelligence base.

Social media sentiment analysis to monitor the performance of vaccination coverage during the early phase of the national COVID-19 vaccine rollout.

BACKGROUND AND OBJECTIVE: Social media sentiment analysis based on Twitter data can facilitate real-time monitoring of COVID-19 vaccine-related concerns. Thus, the governments can adopt proactive measures to address misinformation and inappropriate behaviors surrounding the COVID-19 vaccine, threatening the success of the national vaccination campaign. This study aims to identify the correlation between COVID-19 vaccine sentiments expressed on Twitter and COVID-19 vaccination coverage, case increase, and case fatality rate in Indonesia. METHODS: We retrieved COVID-19 vaccine-related tweets collected from Indonesian Twitter users between October 15, 2020, to April 12, 2021, using Drone Emprit Academic (DEA) platform. We collected the daily trend of COVID-19 vaccine coverage and the rate of case increase and case fatality from the Ministry of Health (MoH) official website and the KawalCOVID19 database, respectively. We identified the public sentiments, emotions, word usage, and trend of all filtered tweets 90 days before and after the national vaccination rollout in Indonesia. RESULTS: Using a total of 555,892 COVID-19 vaccine-related tweets, we observed the negative sentiments outnumbered positive sentiments for 59 days (65.50%), with the predominant emotion of anticipation among 90 days of the beginning of the study period. However, after the vaccination rollout, the positive sentiments outnumbered negative sentiments for 56 days (62.20%) with the growth of trust emotion, which is consistent with the positive appeals of the recent news about COVID-19 vaccine safety and the government's proactive risk communication. In addition, there was a statistically significant trend of vaccination sentiment scores, which strongly correlated with the increase of vaccination coverage (r = 0.71, P<.0001 both first and second doses) and the decreasing of case increase rate (r = -0.70, P<.0001) and case fatality rate (r = -0.74, P<.0001). CONCLUSIONS: Our results highlight the utility of social media sentiment analysis as government communication strategies to build public trust, affecting individual willingness to get vaccinated. This finding will be useful for countries to identify and develop strategies for speed up the vaccination rate by monitoring the dynamic netizens' reactions and expression in social media, especially Twitter, using sentiment analysis.

How Can Research on Artificial Empathy Be Enhanced by Applying Deepfakes?

We propose the idea of using an open data set of doctor-patient interactions to develop artificial empathy based on facial emotion recognition. Facial emotion recognition allows a doctor to analyze patients' emotions, so that they can reach out to their patients through empathic care. However, face recognition data sets are often difficult to acquire; many researchers struggle with small samples of face recognition data sets. Further, sharing medical images or videos has not been possible, as this approach may violate patient privacy. The use of deepfake technology is a promising approach to deidentifying video recordings of patients' clinical encounters. Such technology can revolutionize the implementation of facial emotion recognition by replacing a patient's face in an image or video with an unrecognizable face-one with a facial expression that is similar to that of the original. This technology will further enhance the potential use of artificial empathy in helping doctors provide empathic care to achieve good doctor-patient therapeutic relationships, and this may result in better patient satisfaction and adherence to treatment.

Voice-based control system for smart hospital wards: a pilot study of patient acceptance.

BACKGROUND: The smart hospital's concept of using the Internet of Things (IoT) to reduce human resources demand has become more popular in the aging society. OBJECTIVE: To implement the voice smart care (VSC) system in hospital wards and explore patient acceptance via the Technology Acceptance Model (TAM). METHODS: A structured questionnaire based on TAM was developed and validated as a research tool. Only the patients hospitalized in the VSC wards and who used it for more than two days were invited to fill the questionnaire. Statistical variables were analyzed using SPSS version 24.0. A total of 30 valid questionnaires were finally obtained after excluding two incomplete questionnaires. Cronbach's α values for all study constructs were above 0.84. RESULT: We observed that perceived ease of use on perceived usefulness, perceived usefulness on user satisfaction and attitude toward using, and attitude toward using on behavioral intention to use had statistical significance (p < .01), respectively. CONCLUSION: We have successfully developed the VSC system in a Taiwanese academic medical center. Our study indicated that perceived usefulness was a crucial factor, which means the system function should precisely meet the patients' demands. Additionally, a clever system design is important since perceived ease of use positively affects perceived usefulness. The insight generated from this study could be beneficial to hospitals when implementing similar systems to their wards.

Chemopreventive Effects of Concomitant or Individual Use of Statins, Aspirin, Metformin, and Angiotensin Drugs: A Study Using Claims Data of 23 Million Individuals.

Despite previous studies on statins, aspirin, metformin, and angiotensin-converting-enzyme inhibitors (ACEIs)/angiotensin II receptor blockers (ARBs), little has been studied about all their possible combinations for chemoprevention against cancers. This study aimed to comprehensively analyze the composite chemopreventive effects of all the combinations. In this case-control study, health records were retrieved from claims databases of Taiwan's Health and Welfare Data Science Center. Eligible cases were matched at a 1:4 ratio with controls for age and sex. Both cases and controls were categorized into 16 exposure groups based on medication use. A total of 601,733 cancer cases were identified. Cancer risks (denoted by adjusted odds ratio; 99% confidence interval) were found to be significantly decreased: overall risk of all cancers in statin-alone (0.864; 0.843, 0.886), aspirin-alone (0.949; 0.939, 0.958), and ACEIs/ARBs (0.982; 0.978, 0.985) users; prostate (0.924; 0.889, 0.962) and female breast (0.967; 0.936, 1.000) cancers in metformin-alone users; gastrointestinal, lung, and liver cancers in aspirin and/or ACEIs/ARBs users; and liver cancer (0.433; 0.398, 0.471) in statin users. In conclusion, the results found no synergistic effect of multiple use of these agents on cancer prevention. Use of two (statins and aspirin, statins and metformin, statins and ACEIs/ARBs, and aspirin and ACEIS/ARBs) showed chemopreventive effects in some combinations, while the use of four, in general, did not.

Social Media Data Analytics for Outbreak Risk Communication: Public Attention on the "New Normal" During the COVID-19 Pandemic in Indonesia.

BACKGROUND: After two months of implementing a partial lockdown, the Indonesian government had announced the "New Normal" policy to prevent a further economic crash in the country. This policy received many critics, as Indonesia still experiencing a fluctuated number of infected cases. Understanding public perception through effective risk communication can assist the government in relaying an appropriate message to improve people's compliance and to avoid further disease spread. OBJECTIVE: This study observed how risk communication using social media platforms like Twitter could be adopted to measure public attention on COVID-19 related issues "New Normal". METHOD: From May 21 to June 18, 2020, we archived all tweets related to COVID-19 containing keywords: "#NewNormal", and "New Normal" using Drone Emprit Academy (DEA) engine. DEA search API collected all requested tweets and described the cumulative tweets for trend analysis, word segmentation, and word frequency. We further analyzed the public perception using sentiment analysis and identified the predominant tweets using emotion analysis. RESULT: We collected 284,216 tweets from 137,057 active users. From the trend analysis, we observed three stages of the changing trend of the public's attention on the "New Normal". Results from the sentiment analysis indicate that more than half of the population (52%) had a "positive" sentiment towards the "New Normal" issues while only 41% of them had a "negative" perception. Our study also demonstrated the public's sentiment trend has gradually shifted from "negative" to "positive" due to the influence of both the government actions and the spread of the disease. A more detailed analysis of the emotion analysis showed that the majority of the public emotions (77.6%) relied on the emotion of "trust", "anticipation", and "joy". Meanwhile, people were also surprised (8.62%) that the Indonesian government progressed to the "New Normal" concept despite a fluctuating number of cases. CONCLUSION: Our findings offer an opportunity for the government to use Twitter in the process of quick decision-making and policy evaluation during uncertain times in response to the COVID-19 pandemic.

Alumni's perception of public health informatics competencies: lessons from the Graduate Program of Public Health, Faculty of Medicine, Universitas Gadjah Mada, Indonesia.

Public health informatics has been defined as the systematic application of information and computer science and technology to public health practice, research, and learning [1]. Unfortunately, limited reports exist concerning to the capacity building strategies to improve public health informatics workforce in limited-resources setting. In Indonesia, only three universities, including Universitas Gadjah Mada (UGM), offer master degree program on related public health informatics discipline. UGM started a new dedicated master program on Health Management Information Systems in 2005, under the auspice of the Graduate Program of Public Health at the Faculty of Medicine. This is the first tracer study to the alumni aiming to a) identify the gaps between curriculum and the current jobs and b) describe their perception on public health informatics competencies. We distributed questionnaires to 114 alumni with 36.84 % response rate. Despite low response rate, this study provided valuable resources to set up appropriate competencies, curriculum and capacity building strategies of public health informatics workforce in Indonesia.