The use of generative artificial intelligence (AI) chatbots, such as ChatGPT and YouChat, has increased enormously since their release in late 2022. Concerns have been raised over the potential of chatbots to facilitate cheating in education settings, including essay writing and exams. In addition, multiple publishers have updated their editorial policies to prohibit chatbot authorship on publications. This article highlights another potentially concerning issue; the strong propensity of chatbots in response to queries requesting medical and scientific information and its underlying references, to generate plausible looking but inaccurate responses, with the chatbots also generating nonexistent citations. As an example, a number of queries were generated and, using two popular chatbots, demonstrated that both generated inaccurate outputs. The authors thus urge extreme caution, because unwitting application of inconsistent and potentially inaccurate medical information could have adverse outcomes.
Aim: Mobile health applications (MHAs) have been rapidly designed and urgently need evaluation. Existing evaluation methods, such as platform, development, and subjective overall user observations, are mostly based on application (app) design. This study aimed to evaluate the functionality and feasibility of an MHA to train cognitive function in youth at clinical high risk (CHR) for psychosis with a tool that allows a comprehensive user experience evaluation of mobile apps from multiple dimensions.
Methods: Eighty participants with CHR for psychosis were recruited and randomly assigned to the intervention and the group control. Participants in the intervention group used the Specific Memory Attention Resource and Training (SMART) app for three months. MHA’s functionality and feasibility were measured by the mobile app rating scale (MARS) and qualitative tools.
Results: Participants in the SMART group report that the form and design of this app are simple to operate, and the content is trustworthy. They reported improvement in cognitive function and more motivation to seek help to improve their cognitive function. They also pointed out areas of improvement.
Conclusions: SMART usability and functionality were measured by a multidimensional tool. It shows promise in improving CHR memory and attention and demonstrates appropriate usability and functionality.
Aim: After the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and the realization of mass vaccination against the virus, the availability of a reliable, rapid, and easy-to-use system for registering the individual anti-S1 antibody titer could facilitate the personalized assessment of the need for booster vaccine doses and the reduction of social distancing and other measures.
Methods: The biosensor system is based on immobilized engineered SK-N-SH neuroblastoma cells, bearing the S1 protein, and it can detect immunoglobulin G (IgG) antibodies against the SARS-CoV-2 S1 spike antigen. A disposable electrode strip bearing the engineered mammalian cells is connected to a customized read-out potentiometric device with real-time data transmission to a wireless fidelity (WiFi)-connected smartphone. Blood samples from past-infected individuals and individuals vaccinated against SARS-CoV-2 were used for validation.
Results: In the present study, a smartphone application (app), capable of analyzing data regarding the levels of anti-S1 antibodies in blood is introduced. The app works in conjunction with a portable, ultra-rapid, and sensitive biosensor transmitting real-time measurements to the smartphone. Both historical and current individual data can be encoded by using the app, resulting in a widely accepted quick response (QR) code, which can then be constantly updated to match a person’s status.
Conclusions: This novel system could be utilized for the eventual development of a coronavirus disease 2019 (COVID-19) electronic passport, which could be further employed to improve the population-wide, cross-country surveillance of vaccination efficiency, as well as facilitate the implementation of cross-border digital health services in a user-friendly and secure way.
Aim: This study investigates the potential of artificial intelligence (AI) in revolutionizing healthcare insurance claim processing in the USA. It aims to determine the most effective machine learning (ML) model for predicting health insurance claims, leading to cost savings for insurance companies.
Methods: Six ML algorithms were used to predict health insurance claims, and their performance was evaluated using various metrics. The algorithms examined include support vector machine (SVM), decision tree (DT), random forest (RF), linear regression (LR), extreme gradient boosting (XGBoost), and k-nearest neighbors (KNN). The research involves a performance assessment that encompasses key metrics. Additionally, a feature importance analysis is conducted to illuminate the critical variables that exert influence on the prediction of insurance claims.
Results: The findings demonstrate that the XGBoost and RF models outperformed the other algorithms, displaying the highest R-squared values of 79% and 77% and the lowest prediction errors. The feature importance analysis underscores the pivotal role of variables such as smoking habits, body mass index (BMI), and blood pressure levels in the domain of insurance claim prediction. These results emphasize the degree to which these variables should be included in the formulation of insurance policies and pricing strategies.
Conclusions: This study supports the transformative potential of AI, with specific emphasis on the XGBoost model, in extending the precision and efficiency of healthcare insurance claim processing. The identification of key variables and the mitigation of prediction errors not only signal the potential for substantial cost savings but also affirm the potential to integrate AI into healthcare insurance processes. This research supports the value of the utilization of AI as an emerging tool for process optimization and data-informed decision-making within the healthcare insurance domain.