OBJECTIVE: This study aimed to assess the practicality and trustworthiness of explainable artificial intelligence (XAI) methods used for explaining clinical predictive models.
This study aimed to investigate the accuracy, reliability, and readability of A-Eye Consult, ChatGPT-4.0, Google Gemini and Copilot AI large language models (LLMs) in responding to patient questions about endophthalmitis. The LLMs' responses to 25 ...
Mitigation of racism in artificial intelligence (AI) is needed to improve health outcomes, yet no consensus exists on how this might be achieved. At an international conference in 2022, experts gathered to discuss strategies for reducing bias in he...
The Internet of Medical Things (IoMT) is transforming healthcare systems, but concerns about device integrity and sensitive data are growing. The study aims to develop a framework for evaluating and prioritizing integrity schemes in healthcare for I...
To develop and test an NLP algorithm that accurately detects the presence of information reported from DXA scans containing femoral neck T-scores of the patients scanned. A rule-based NLP algorithm that iteratively built a collection of regular exp...
Machine learning-based analytics over uni-modal medical data has shown considerable promise and is now routinely deployed in diagnostic procedures. However, patient data consists of diverse types of data. By exploiting such data, multimodal approach...
Artificial intelligence (AI) can enhance life experiences and present challenges for people with disabilities. This study aims to investigate the relationship between AI and disability, exploring the potential benefits and challenges of using AI fo...
Due to changes in lifestyle, bariatric surgery is expanding worldwide. However, this surgery has numerous complications, and early identification of these complications could be essential in assisting patients to have a higher-quality surgery. Machi...
: Diarrhea is a major cause of mortality and morbidity in under-5 children globally, especially in developing countries like Ethiopia. Limited research has used machine learning to predict childhood diarrhea. This study aimed to compare the predictiv...
Addressing the challenge of cost-effective asthma diagnosis amidst diverse symptom patterns among patients, this study aims to develop a machine learning-based asthma prediction tool for self-detection of asthma. Data from 6,665 participants in the...