Aim: In lung cancer research, AI has been trained to read chest radiographs, which has led to improved health outcomes. However, the use of AI in healthcare settings is not without its own set of drawbacks, with bias being primary among them. This study seeks to investigate AI bias in diagnosing and treating lung cancer patients. The research objectives of this study are threefold: 1) To determine which features of patient datasets are most susceptible to AI bias; 2) to then measure the extent of such bias; and 3) from the findings generated, offer recommendations for overcoming the pitfalls of AI in lung cancer therapy for the delivery of more accurate and equitable healthcare.

Methods: We created a synthetic database consisting of 50 lung cancer patients using a large language model (LLM). We then used a logistic regression model to detect bias in AI-informed treatment plans.

Results: The empirical results from our synthetic patient data illustrate AI bias along the lines of (1) patient demographics (specifically, age) and (2) disease classification/histology. As it concerns patient age, the model exhibited an accuracy rate of 82.7% for patients < 60 years compared to 85.7% for patients ≥ 60 years. Regarding disease type, the model was less adept in identifying treatment categories for adenocarcinoma (accuracy rate: 83.7%) than it was in predicting treatment categories for squamous cell carcinoma (accuracy rate: 92.3%).

Conclusions: We address the implications of such results in terms of how they may exacerbate existing health disparities for certain patient populations. We conclude by outlining several strategies for addressing AI bias, including generating a more robust training dataset, developing software tools to detect bias, making the model’s code open access and soliciting user feedback, inviting oversight from an ethics review board, and augmenting patient datasets by synthesizing the underrepresented data.

Background: Social media has become ubiquitous; its uses reach beyond connecting individuals or organizations. Many biomedical researchers have found social media to be a useful tool in recruiting patients for clinical studies, crowdsourcing for cross-sectional studies, and even as a method of intervention. Social media usefulness in biomedical research has largely been in population health and non-surgical specialties, however, its usefulness in surgical specialties should not be overlooked. Specifically in plastic surgery, social media use to understand patient perceptions, identify populations, and provide care has become an important part of clinical practice.

Methods: A scoping review was performed utilizing PubMed and Medline databases, and articles were screened for the use of social media as a method of recruitment to a clinical trial, as crowdsourcing (i.e., recruitment for a cross-sectional or survey-based study), or as a method of intervention.

Results: A total of 28 studies were included, which focused on majority females between 18–34 years old. Despite the ability of the internet and social media to connect people worldwide, nearly all the studies focused on the researchers’ home countries. The studies largely focused on social media’s effect on self-esteem and acceptance of cosmetic surgery, but other notable trends were analyses of patient perceptions of a disease, or surgical outcomes as reported in social media posts.

Discussion: Overall, social media can be a useful tool for plastic surgeons looking to recruit patients for a survey-based study or crowdsourcing of information.

Aim: This study aims to evaluate the accuracy and readability of responses generated by two large language models (LLMs) (ChatGPT-4 and Gemini) to frequently asked questions by lay persons (the general public) about signs and symptoms, risk factors, screening, diagnosis, treatment, prevention, and survival in relation to oral cancer.

Methods: The accuracy of each response given in the two LLMs was rated by four oral cancer experts, blinded to the source of the responses. The accuracy was rated as 1: complete, 2: correct but insufficient, 3: includes correct and incorrect/outdated information, and 4: completely incorrect. Frequency, mean scores for each question, and overall were calculated. Readability was analyzed using the Flesch Reading Ease and the Flesch-Kincaid Grade Level (FKGL) tests.

Results: The mean accuracy scores for ChatGPT-4 responses ranged from 1.00 to 2.00, with an overall mean score of 1.50 (SD 0.36), indicating that responses were usually correct but sometimes insufficient. Gemini responses had mean scores ranging from 1.00 to 1.75, with an overall mean score of 1.20 (SD 0.27), suggesting more complete responses. The Mann-Whitney U test revealed a statistically significant difference between the models’ scores (p = 0.02), with Gemini outperforming ChatGPT-4 in terms of completeness and accuracy. ChatGPT generally produces content at a lower grade level (average FKGL: 10.3) compared to Gemini (average FKGL: 12.3) (p = 0.004).

Conclusions: Gemini provides more complete and accurate responses to questions about oral cancer that lay people may seek answers to compared to ChatGPT-4, although its responses were less readable. Further improvements in model training and evaluation consistency are needed to enhance the reliability and utility of LLMs in healthcare settings.

This research provided an in-depth analysis of endoscopic capsules as an innovative application of the Internet of Things (IoT) in healthcare. The study revealed the importance of these systems in advancing gastrointestinal diagnostics due to their non-invasive nature and ability to provide comprehensive internal imaging. The work systematically investigated the device’s technical design, power management strategies, communication protocols, and how it performs its secure and efficient operations. Findings from this analysis highlighted the transformative impact of these capsules despite current constraints, such as battery limitations and procedural costs. Ultimately, this wide review confirmed that endoscopic capsules redefine medical diagnostics, fusing patient comfort with innovative technology. Moreover, as developments continue, these devices have promising potential to shape the future of intelligent, interconnected healthcare solutions.

Somalia’s healthcare system faces significant challenges, including limited infrastructure, a shortage of healthcare professionals (2.5 physicians per 10,000 people), and geographic disparities in access to care, leading to only 35% of the population having access to basic health services. Despite these, Somalia is embracing digital health technologies to address these challenges and to improve healthcare delivery. Telehealth platforms such as Baano and SomDoctor provide remote consultations and specialized care to overcome geographical barriers. mHealth solutions, including Hello! Caafi, leverages Somalia’s expanding telecommunications network to deliver healthcare information and services. The development of an electronic immunization registry demonstrated the role of digital health records in streamlining health services and improving data accuracy. Despite the potential benefits, challenges persist, including limited and unreliable Internet access (27.6% penetration rate), and the need to ensure data privacy and security. Capacity building and digital literacy enhancement among healthcare providers and populations are crucial. Learning from successful digital health initiatives in African countries that have effectively used digital health technologies for medical supply delivery and for improved healthcare access is essential. The roadmap for Somalia emphasizes government leadership, public-private partnerships, context-specific solutions, and investment in digital infrastructure, capacity building, and data privacy measures. This perspective explores current digital health innovations in Somalia and their potential impact on healthcare access and quality, outlining a roadmap for establishing a sustainable digital health ecosystem.

The health sector in Yemen has experienced significant challenges due to prolonged conflict and suboptimal governance, making the development of digital health (DH) crucial. This study highlights the urgent need for the strategic implementation of health interventions in a country where fully functional healthcare facilities, low-income levels, damaged infrastructure, and suboptimal governance limit the effectiveness of traditional interventions. It discusses the prioritized step for advancing DH as a root issue that needs to be addressed first and highlights the importance of effective and efficient management of available resources. The development of telecommunication infrastructure is a fundamental pillar for advancing DH in the country. This comes along with consideration of effective management of the available resources and collaborative efforts among all parties, which are critically important to remove restrictions and constraints relevant to the administrative division and fragmentation of the healthcare system and objectively ensure universal coverage of telecommunications and healthcare services nationwide. By leveraging DH technologies (DHTs), Yemen can overcome these obstacles and revolutionize healthcare delivery. Implementing DHTs and related projects can ensure equitable access to high-quality healthcare services, particularly for impoverished individuals. However, the success of these initiatives relies on a well-established supportive policy and regulatory framework, improved public communication systems, targeted strategies, community engagement, and collaboration between medical service providers and community healthcare workers. Awareness campaigns, workshops, research collaborations, and engagement with international organizations are highly recommended to address challenges and foster the growth and development of DH in Yemen.