ChatGPT has gained attention worldwide. In the medical education field, ChatGPT, or any similar large language model, provides a convenient way for students to access information and practice their skills. ChatGPT can simulate patient interactions, allowing students to practice their diagnostic and communication skills in a safe and controlled environment. ChatGPT can also answer questions and provide explanations for complex medical concepts. There have been attempts involving ChatGPT to assist researchers in writing journal articles. Due to its capabilities, ChatGPT has the potential to be abused and sparking ethical concerns. Unwise researchers can now employ ChatGPT to write academic articles. Similarly, irresponsible students, might utilize ChatGPT to cheat during exams. We investigated whether ChatGPT, in its current state, can answer Indonesian medical doctor examination problems. Among the 3 problems that we have picked, ChatGPT can only correctly answer 1 question. We also examine whether ChatGPT-generated abstracts can fool professionals and educators. We have brought 6 abstracts, 3 of which are taken from actual published papers, while the other 3 were generated by ChatGPT. We recruited 12 participants with either a medical doctor (M.D.), a doctorate (Ph.D.), or an M.D., Ph.D. degree from various institutions in Indonesia. Surprisingly, 4 of the participants couldn't guess a single abstract correctly, 6 could only identify 1 abstract accurately, one correctly guessed 2 abstracts, and one correctly identified 3 abstracts. Therefore, it is safe to say that ChatGPT, in its current state, has been able to fool professionals and educators.

Artificial intelligence (AI) has shown promising potential to transform various fields, including medical education. Recently, the rapid advancement of AI has led to many ``new'' discoveries that caught everyone's attention. Among those discoveries are introduced by OpenAI (e.g., ChatGPT, Dall-E, and the most recent, GPT-4). The integration of AI tools, such as ChatGPT and Dall-E, can offer a new dimension to medical education by creating an interactive and engaging learning experience. In this article, we explore the potential benefits of ChatGPT and Dall-E in medical education and provide practical utilization examples of those tools. For starters, ChatGPT, or in this sense, any other similar large language models, can simulate patient interactions in a safe environment, allowing medical learners to practice their communication skills and diagnosis techniques. Furthermore, it can assist medical students and researchers in reading and writing academic articles by accurately summarizing the key points of a given topic and generating an indistinguishable In addition, ChatGPT can also create problems for medical assignments and exam practice. In this article, we also discussed ChatGPT's capability to answer standard medical assignment problems. Dall-E, on the other hand, can generate dummy copyright-free and consent-free medical images (e.g., x-ray and electrocardiogram (ECG) graphs), allowing medical learners to practice and enhance their interpretation skills. Incorporating AI-based tools into medical education can provide a new approach to teaching and learning, bridging the gap between theory and practice, and unlocking new avenues for learning and discoveries for both, the students and the instructors. It can also offer a cost-effective solution to simulate real-world scenarios that would otherwise require significant resources and time. In summary, this article concludes that AI-based tools have the potential to revolutionize medical education, empowering medical learners with the skills and knowledge necessary to excel in their field.

Diabetic foot ulcers are a common complication that occurs in approximately 15 percent of patients with diabetes mellitus. Over 60% of diabetic foot ulcers are caused by underlying neuropathy. Former studies on diabetic animals with foot wounds found that vibration platforms significantly accelerate wound healing by catalyzing epithelization, promoting angiogenesis, and enhancing muscle bulk. This result suggests that there is evidence that vibrations may accelerate diabetic neuropathic ulcer healing in human patients. However, to the best of our knowledge, the effect of vibration on the enhancements of diabetic foot ulcer healing in human patients is rarely investigated. Hence, in this work, we conducted an experimental study with human subjects to investigate whether vibration therapy, as a complement to the standard wound treatment, can accelerate the wound healing rate of diabetic neuropathic foot ulcers. In this prospective experimental study, 80 participants diagnosed with Wagner grades I–III diabetic neuropathic foot ulcers were randomly distributed to experimental (n = 40) and control groups (n = 40). Patients in the intervention group received standard wound treatment and vibration wound therapy (VWT), whereas patients in the control group retrieved only standard wound treatment. The results (p = 0.024, α = 0.05) show notable differences in the median healing rate between the intervention group (25 days, 95% CI: 20.3–29.7) and control group (33 days, 95% CI: 25.6–40.4), with the effect-size r, Cohen’s d, Glass’s Δ, and Hedges’ g, respectively, being 0.810, 2.764, 2.311, and 2.772. Moreover, the nitric oxide (NO) level, wound closure area, and wound healing score after intervention significantly differed between the two groups (p < 0.05), putting the intervention group on a higher level than the control group. Furthermore, positive associations were found between the NO level and wound healing closure rates. These findings suggested that VWT enhances diabetic neuropathic foot ulcer healing in terms of healing rate, wound closure area, healing score, and elevated NO level. Considering that no clinically adverse effects were found in the patients induced with vibration intervention, VWT can be regarded as a complementary therapy to the existing ones to accelerate the healing of DFUs.

Healthcare and medical fields are among the areas of AI applications that require serious attention when it comes to ethical and trustworthy AI. In the medical field, artificial intelligence is applied in laboratory research, regulation-policy making, and clinical intervention. Several factors need to be seriously accounted for in the AI-empowered healthcare system, including algorithm transparency, bias mitigation, domain-expert involvement, privacy and data protection, and informed consent. In this article, we summarized the applications of AI in the field of medical and healthcare sectors. We also presented the possible ethical issues in AI applications in mentioned sectors. These issues include transparency and safety, informed consent and right to information, algorithmic fairness and biases, and data privacy and sharing regulation. We then present the basic principles of AI ethics and the existing AI guidelines, especially for the fields of medicine and healthcare. Based on the existing issues and guidelines, we believe that there are still many factors that need to be perfected, such as how we can generalize these guidelines worldwide and how these guidelines can be legally-binding. Detailed clauses in the guideline and law enforcement are another issue that should be addressed such that no loophole can be addressed. Ethical consideration is critical in AI applications, especially in the medical and healthcare fields. Until these issues can be totally addressed, we believe that AI applications in healthcare should be very strictly monitored.

Noncommunicable diseases (NCDs), including cancer, diabetes, hypertension, cardiovascular diseases, and chronic respiratory conditions, are the leading global causes of morbidity and mortality. Depression frequently co-occurs with these conditions, and may significantly reduce medication adherence, thereby worsening health outcomes. This narrative review examines the relationship between depression and medication adherence in patients with NCDs. It also highlights the current challenges in managing this comorbidity and explores potential strategies for improving adherence outcomes. Most studies have reported a significant negative association between depression and medication adherence in diverse NCD populations. Depressive symptoms impair motivation, memory, and executive functioning, which are essential for the maintenance of treatment regimens. However, inconsistencies across studies have been observed due to variability in the measurement of depression and adherence, study design, and control of confounding factors. Despite evidence from high-income countries supporting integrated care models such as collaborative care and cognitive behavioral therapy, implementation of these models in low- and middle-income countries remains limited. Emerging strategies, including task shifting, digital health tools (e.g., mobile health apps and telemedicine), and community-based support systems, offer promising avenues for intervention. Addressing this issue requires integrated and scalable interventions tailored to local contexts. Future research should focus on longitudinal and interventional studies, particularly in resource-limited settings, to inform policies and practices.

Since the end of the second artificial intelligence (AI) winter in 1993, AI popularity has been skyrocketing. Catalyzed by the rapid advancements of supporting technologies such as computational power and storage capacity, AI has been widely developed for various sectors of human lives. Although there is a common global consensus stating that the development of AI should consider ethical principles, until when this paper was written, the debates on how we can define an AI as ‘ethical’, how the AI ethical guidelines should be formulated, who has the rights to determine the ethical aspects of AI, and how can we reinforce the guidelines to the AI developer/operator is still going on. In this article, we summarized the research and studies related to AI development from the ethical perspective that have been conducted within the last 10 years. We discussed several cases of AI development misconduct and ‘unethical’ AI, such as biased algorithms and privacy breaches during data gathering.