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We propose a novel framework that addresses the deficiencies of Randomized clinical trial data subgroup analysis while it transforms ObservAtional Data to be used as if they were randomized, thus paving the road for precision medicine. Our approach counters the effects of unobserved confounding in observational data through a two-step process that adjusts predicted outcomes under treatment. These adjusted predictions train decision trees, optimizing treatment assignments for patient subgroups based on their characteristics, enabling intuitive treatment recommendations. Implementing this framework on gastrointestinal stromal tumors (GIST) data, including genetic sub-cohorts, showed that our tree recommendations outperformed current guidelines in an external cohort. Furthermore, we extended the application of this framework to RCT data from patients with extremity sarcomas. Despite initial trial indications of universal treatment necessity, our framework identified a subset of patients who may not require treatment. Once again, we successfully validated our recommendations in an external cohort. [ABSTRACT FROM AUTHOR]

This study investigated Vietnamese people's behavior and future intention to purchase medicines and functional foods online and their associated factors. From March to May 2023, a Google Form questionnaire was distributed via social networks and online platforms. Convenience and snowballing sampling methods were employed to recruit 1,305 Vietnamese people. In the past year, 50.3% of participants purchased at least one kind of medicine and/or functional foods online (medicines: 27.6%, functional foods: 45.1%). Among 656 buyers, nearly a third bought these products more than three times and only 5.9% felt dissatisfied with their previous experiences. This purchasing behavior was more prevalent among females, those married, having higher education levels, usually shopping online, having longer time of Internet use per day, and seeking health information on the Internet (p < 0.001). In addition, 77.5% of participants intended to purchase these products online in the future and 61.2% would introduce this kind of online purchase to other people. The purchase intention was significantly associated with the participants' previous experiences, area, contracting chronic diseases, and using the Internet for self-medication, while factors associated with the introducing intention included their education level, occupation, and previous experiences in online purchases (p < 0.001). [ABSTRACT FROM AUTHOR]

Objectives: This study aims to assess the impact of Traditional Chinese Medicine (TCM) on dementia patients, utilizing real-world data. Specifically, it seeks to evaluate how TCM influences clinical outcomes by examining changes in the Clinical Dementia Rating (CDR) and Mini-Mental State Examination (MMSE) scores, as well as its effect on medical expenses over a two-year period. Data from a multi-center research database spanning from 2004 to 2021 will be used to achieve these objectives, addressing the current gap in empirical data concerning intuitive outcomes and cognitive function assessments. Methods: Propensity score matching was adopted to improve comparability among the intervention and control groups. Due to repeated dependent variable measurements, the generalized estimating equation was used to control for socio-demographic characteristics, regional characteristics, and Western medicine treatments for dementia. Results: After propensity score matching, a total of 441 research subjects were included: 90 in the TCM intervention group and 351 in the non-TCM intervention group. The results of multivariate regression analysis showed that compared with the non-TCM intervention group, the MMSE scores in the TCM intervention group increased by 0.608 points each year. The annual change in CDR scores in the TCM intervention group was 0.702 times that of the non-TCM utilization group. After TCM intervention, annual outpatient expenses increased by US$492.2, hospitalization expenses increased by US$324.3, and total medical expenses increased by US$815.9, compared with the non-intervention group. Conclusions: TCM interventions significantly decelerate cognitive decline in dementia patients, evidenced by slower reductions in MMSE scores and mitigated increases in CDR scores. However, these benefits are accompanied by increased medical expenses, particularly for outpatient care. Future healthcare strategies should balance the cognitive benefits of TCM with its economic impact, advocating for its inclusion in dementia care protocols. [ABSTRACT FROM AUTHOR]

The present article aims to describe the different steps on how to design, develop and conduct quantitative and qualitative Spirituality and Health (S/H) studies from a Brazilian perspective, discussing definitions and instruments, and proposing a "how-to guide" for those interested in this field of research. A narrative review of the literature has been conducted by experts in the field of S/H aiming to develop a "how-to-guide". Spirituality is a very complex concept that has several challenges in the current scientific literature, including the lack of consensus in the definitions, the numerous dimensions assessed, the diverse instruments for measurement, the criticisms from other scholars, the great diversity of religious and cultural traditions and the growing number of "spiritual but not religious" individuals. This review supports that S/H studies may follow the high scientific standards, using consolidated research procedures and solid methods for both qualitative and quantitative research. Some peculiarities of the research methods for this field are discussed. S/H research is still under development, and there are several future directions for S/H studies, aiming to minimize previous criticisms and generating stronger evidence. [ABSTRACT FROM AUTHOR]

Eugenia flavescens is a species cultivated in Brazil for food purposes. Given the potential application of essential oils from plants of the genus Eugenia, this study was carried out to investigate the chemical composition, acute toxicity, antioxidant, antinociceptive, gastroprotective activities, and possible mechanisms of action of the essential oil from the leaves of E. flavescens (EOEf). The EOEf was extracted by hydrodistillation, and the chemical composition was obtained using gas chromatography–mass spectrometry. The antioxidant activity was evaluated, as well as the acute toxicity and the antinociceptive and anti-inflammatory effects in mice. In addition, the gastroprotective effect was investigated using an acute gastric lesion model, considering possible mechanisms of action. The major components found in the EOEf were guaiol (19.97%), germacrene B (12.53%), bicyclogermacrene (11.11%), and E-caryophyllene (7.53%). The EOEf did not cause signs of toxicity in the acute oral toxicity test and showed in vitro antioxidant activity with IC50 ranging from 247.29 to 472.39 µg/mL in the tests ABTS and DPPH. In the nociceptive test, EOEf showed a 72.05% reduction in nociception at a dose of 100 mg/kg. In evaluating the anti-inflammatory effect, the essential oil inhibited paw edema by 95.50% and 97.69% at doses of 50 and 100 mg/kg. The results showed that EOEf has a gastroprotective effect, acting through the sulfhydryl compounds (–SH), nitric oxide (NO), and synthesis PGE2 pathways. The results suggested that EOEf is a promising source of constituents with antioxidant, antinociceptive, anti-inflammatory, and gastroprotective properties with application in the food and pharmaceutical industries. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is increasingly relied upon by clinicians for making diagnostic and treatment decisions, playing an important role in imaging, diagnosis, risk analysis, lifestyle monitoring, and health information management. While research has identified biases in healthcare AI systems and proposed technical solutions to address these, we argue that effective solutions require human engagement. Furthermore, there is a lack of research on how to motivate the adoption of these solutions and promote investment in designing AI systems that align with values such as transparency and fairness from the outset. Drawing on insights from psychological theories, we assert the need to understand the values that underlie decisions made by individuals involved in creating and deploying AI systems. We describe how this understanding can be leveraged to increase engagement with de-biasing and fairness-enhancing practices within the AI healthcare industry, ultimately leading to sustained behavioral change via autonomy-supportive communication strategies rooted in motivational and social psychology theories. In developing these pathways to engagement, we consider the norms and needs that govern the AI healthcare domain, and we evaluate incentives for maintaining the status quo against economic, legal, and social incentives for behavior change in line with transparency and fairness values. [ABSTRACT FROM AUTHOR]

The integration of Artificial Intelligence (AI)-driven predictive maintenance (PM) into the management of medical 3D printing machines promises a revolutionary advancement in operational efficiency and reliability. Traditional maintenance approaches often fail to optimize machine uptime and reduce costs effectively. AI and Machine Learning (ML) techniques, however, enable a proactive maintenance strategy by predicting potential failures and facilitating timely interventions. This ensures continuous high-quality production, which is critical for patient safety and effective medical care. AI-driven PM employs various methods, including supervised and unsupervised learning, neural networks, and natural language processing, to analyze vast datasets and perform intelligent diagnostics. The incorporation of advanced technologies such as digital twins, generative AI, collaborative robots, blockchain, and the Industrial Internet of Things (IIoT) further enhances fault detection, transparency, and efficiency in maintenance processes. The benefits of this approach include minimized downtime, cost savings, improved safety, and increased equipment reliability. As AI technology advances, its role in PM will become more sophisticated, establishing new benchmarks for the operation and management of 3D printing machines. This proactive maintenance strategy not only improves performance but also provides a competitive advantage in the healthcare sector. [ABSTRACT FROM AUTHOR]

The value of generative AI tools in higher education has received considerable attention. Although there are many proponents of its value as a learning tool, many are concerned with the issues regarding academic integrity and its use by students to compose written assessments. This study evaluates and compares the output of three commonly used generative AI tools, ChatGPT, Bing and Bard. Each AI tool was prompted with an essay question from undergraduate (UG) level 4 (year 1), level 5 (year 2), level 6 (year 3) and postgraduate (PG) level 7 biomedical sciences courses. Anonymised AI generated output was then evaluated by four independent markers, according to specifed marking criteria and matched to the Frameworks for Higher Education Qualifcations (FHEQ) of UK level descriptors. Percentage scores and ordinal grades were given for each marking criteria across AI generated papers, inter-rater reliability was calculated using Kendall's coefcient of concordance and generative AI performance ranked. Across all UG and PG levels, ChatGPT performed better than Bing or Bard in areas of scientifc accuracy, scientifc detail and context. All AI tools performed consistently well at PG level compared to UG level, although only ChatGPT consistently met levels of high attainment at all UG levels. ChatGPT and Bing did not provide adequate references, while Bing falsifed references. In conclusion, generative AI tools are useful for providing scientifc information consistent with the academic standards required of students in written assignments. These fndings have broad implications for the design, implementation and grading of written assessments in higher education. [ABSTRACT FROM AUTHOR]

Nanoparticles have unique characteristics with applications in different fields of industry and medicine. There are several methods for synthesizing zinc oxide nanoparticles, mostly according economic and environmental criteria. On this contribution, it is presented a review on zinc oxide nanoparticles synthesized by "green route" methods and their biomedical advantages such as anticancer and antibacterial properties. To obtain this type of environment-friendly nanoparticles with different morphologies, zinc salts are used as precursor and as reductants. Plants as biological media are used as well, that is because naturally occurs within their systems reduction processes and/or phytochemical extracts of some part of plants. Thankfully to these green routes, the exposure and final disposal of toxic substances that involve conventional chemical methods are mitigated. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence technologies in image analysis tasks is rapidly developing every year, thanks to which the possibilities of using artificial neural networks in the diagnosis of various diseases have reached a completely new level. The results of thousands of studies using neural networks in ophthalmology show diagnostic accuracy results comparable and sometimes even superior to those achieved by a human performer. However, the transparency of the results obtained and the adaptability of the operation of such systems remain low. In the future, the use of convolutional neuro-fuzzy networks in image analysis tasks will probably improve the results. The work examines the existing experience in using neuro-fuzzy networks for problems of classification of medical images and demonstrates the possibilities of practical implementation of a neuro-fuzzy network for problems of classification of glaucoma ophthalmic images. [ABSTRACT FROM AUTHOR]

Copyright of Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rheumatoid arthritis (RA) is a worldwide public health problem. Interventions to delay or prevent the onset of RA have attracted much attention in recent years, and researchers are now exploring various prevention strategies. At present, there is still no unified consensus for RA prevention, but targeting therapeutic windows and implementing interventions for at-risk individuals are extremely important. Due to the limited number of clinical trials on pharmacologic interventions, further studies are needed to explore and establish optimal intervention regimens and effective measures to prevent progression to RA. In this review, we introduce the RA disease process and risk factors, and present research on the use of both Western and Chinese medicine from clinical perspectives regarding RA prevention. Furthermore, we describe several complete and ongoing clinical studies on the use of Chinese herbal formulae for the prevention of RA. [ABSTRACT FROM AUTHOR]

Copyright of Die Urologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Using literature data, a model was developed to predict the kinetics of drug' desorption from hydroxypropyl-methylcellulose-based tablets at different water stirring rates. When the tablet comes into contact with a gastric juice, the polymer swells to form a gel-like shell, which prevents the transfer of gastric juice into the tablet and determines the rate of drug release into the human body. We used an equation that describes the kinetic dependences of drug desorption in the coordinate system of a conditional time equal to the square root of the observation time. Dependence of the equation coefficients on the stirring speed was established, which made it possible to obtain a model for predicting the kinetics of drug desorption from the time of contact with water and the water stirring speed. [ABSTRACT FROM AUTHOR]

Autonomous nanomedicine, a burgeoning field within nanotechnology and biomedical sciences, is poised to revolutionize healthcare by eliminating the need for external intervention in targeted applications within the body. This article elucidates the promise and challenges of autonomous nanomedicine, emphasizing its ability to overcome the limitations of traditional methods such as chemotherapy and radiotherapy. Central to its efficacy are nano-sized carriers, which autonomously navigate the body to deliver therapeutic agents with precision and control. By integrating automated nanoscale tools into disease detection processes, this technology offers swift and personalized assessments, reshaping disease management paradigms. To advance the clinical translation of autonomous nanomedicine, rigorous preclinical studies are imperative. However, challenges persist in ensuring reproducibility and safety, hindering progress in clinical trials. This article examines current studies with potential clinical translation, shedding light on the regulatory and ethical considerations crucial for its safe implementation. As the field progresses, maintaining a balance between innovation and safety remains paramount for harnessing the full potential of autonomous nanomedicine while safeguarding patient well-being. [ABSTRACT FROM AUTHOR]

Objectives: This review explores the potential applications of large language models (LLMs) such as ChatGPT, GPT-3.5, and GPT-4 in the medical field, aiming to encourage their prudent use, provide professional support, and develop accessible medical AI tools that adhere to healthcare standards. Methods: This paper examines the impact of technologies such as OpenAI's Generative Pre-trained Transformers (GPT) series, including GPT-3.5 and GPT-4, and other large language models (LLMs) in medical education, scientific research, clinical practice, and nursing. Specifically, it includes supporting curriculum design, acting as personalized learning assistants, creating standardized simulated patient scenarios in education; assisting with writing papers, data analysis, and optimizing experimental designs in scientific research; aiding in medical imaging analysis, decision-making, patient education, and communication in clinical practice; and reducing repetitive tasks, promoting personalized care and self-care, providing psychological support, and enhancing management efficiency in nursing. Results: LLMs, including ChatGPT, have demonstrated significant potential and effectiveness in the aforementioned areas, yet their deployment in healthcare settings is fraught with ethical complexities, potential lack of empathy, and risks of biased responses. Conclusion: Despite these challenges, significant medical advancements can be expected through the proper use of LLMs and appropriate policy guidance. Future research should focus on overcoming these barriers to ensure the effective and ethical application of LLMs in the medical field. [ABSTRACT FROM AUTHOR]

This study explored the enablers and obstacles to the integration of traditional medicine and mainstream medicine in mental health services in West Africa. This study is a systematic review conducted in accordance with the relevant parts of the Preferred Reporting Items for Systematic reviews and Meta-analyses. Keywords searches were done in databases, and other reference lists were also searched. The Rainbow model of integrated care and a thematic analysis framework were used to account for the factors influencing the integration of traditional medicine and mainstream medicine in mental health services in West Africa. A total of 12 studies met the eligibility criteria after the evaluation of 6413 articles from databases and reference lists. The themes of: policy and implementation; different conceptualisation of mental health/referrals; trust issues, and education and training, were enablers or obstacles of integration depending on how they worked to facilitate or hinder integration. There was an indication of little integration of TM and MM at the macro, meso and micro levels in mental health services in West Africa. Though the study does cover all the West African states evenly, it is recommended that policy-makers and stakeholders interested in integration should ensure integration activities, especially policies, cut across all the levels of the rainbow model of integrated care and are planned and aligned at the macro, meso and micro levels instead of using ad hoc measures, informal initiatives or placing TM services in MM mental health services, which do not amount to integration. [ABSTRACT FROM AUTHOR]

Introduction: Autologous platelet-rich plasma (PRP) therapy has emerged as a promising regenerative treatment modality, offering potential improvements in healing outcomes through its rich content of growth factors and cytokines. We evaluated the effectiveness of PRP therapy in the management of complex wounds, using a decade-long retrospective analysis of treatments conducted at a tertiary care center from 2010 to 2020. The study introduces and assesses the efficacy of the Sandeep's Technique for Assisted Regeneration of Skin (STARS) in enhancing wound healing and quality of life for patients with complex wounds. Materials and methods: A prospective interventional study was conducted, involving two phases: the development and initial testing of PRP therapy (2010–2015) and the application and evaluation of the STARS protocol (2015–2020). The study included patients with complex wounds, utilizing autologous PRP prepared through a double spin centrifuge technique. Outcome measures included wound-healing rates, infection management, and complication rates, compared to conventional treatment methods. Results: The study treated 500 wounds in 432 patients with autologous PRP, noting significant improvements in wound-healing rates, 97.7% had infection control without antibiotics (even in MRSA cases), and all had a good pain control. Histopathological examinations confirmed collagen-rich healing with minimal scarring. The STARS protocol demonstrated the potential of PRP therapy in accelerating wound healing, reducing the need for additional surgical interventions, and enhancing patient outcomes. Conclusion: PRP therapy, particularly when administered following the STARS protocol, represents a safe, effective, and patient-friendly approach for the management of complex wounds. This study supports the integration of PRP therapy into regenerative care strategies, suggesting a shift toward more innovative and efficacious treatments in wound management. [ABSTRACT FROM AUTHOR]

To evaluate the response capabilities, in a public healthcare system otolaryngology job competition examination, of ChatGPT 3.5 and an internet-connected GPT-4 engine (Microsoft Copilot) with the real scores of otolaryngology specialists as the control group. In September 2023, 135 questions divided into theoretical and practical parts were input into ChatGPT 3.5 and an internet-connected GPT-4. The accuracy of AI responses was compared with the official results from otolaryngologists who took the exam, and statistical analysis was conducted using Stata 14.2. Copilot (GPT-4) outperformed ChatGPT 3.5. Copilot achieved a score of 88.5 points, while ChatGPT scored 60 points. Both AIs had discrepancies in their incorrect answers. Despite ChatGPT's proficiency, Copilot displayed superior performance, ranking as the second-best score among the 108 otolaryngologists who took the exam, while ChatGPT was placed 83rd. A chat powered by GPT-4 with internet access (Copilot) demonstrates superior performance in responding to multiple-choice medical questions compared to ChatGPT 3.5. [ABSTRACT FROM AUTHOR]

Despite pronouncements about the inevitable diffusion of artificial intelligence and autonomous technologies, in practice, it is human behavior, not technology in a vacuum, that dictates how technology seeps into—and changes—societies. To better understand how human preferences shape technological adoption and the spread of AI-enabled autonomous technologies, we look at representative adult samples of US public opinion in 2018 and 2020 on the use of four types of autonomous technologies: vehicles, surgery, weapons, and cyber defense. By focusing on these four diverse uses of AI-enabled autonomy that span transportation, medicine, and national security, we exploit the inherent variation between these AI-enabled autonomous use cases. We find that those with familiarity and expertise with AI and similar technologies were more likely to support all of the autonomous applications we tested (except weapons) than those with a limited understanding of the technology. Individuals that had already delegated the act of driving using ride-share apps were also more positive about autonomous vehicles. However, familiarity cut both ways; individuals are also less likely to support AI-enabled technologies when applied directly to their life, especially if technology automates tasks they are already familiar with operating. Finally, we find that familiarity plays little role in support for AI-enabled military applications, for which opposition has slightly increased over time. [ABSTRACT FROM AUTHOR]

Depression is a multifactorial disease with unknown etiology affecting globally. It's the second most significant reason for infirmity in 2020, affecting about 50 million people worldwide, with 80% living in developing nations. Recently, a surge in depression research has been witnessed, resulting in a multitude of emerging techniques developed for prediction, evaluation, detection, classification, localization, and treatment. The main purpose of this study is to determine the volume of depression research conducted on different aspects such as genetics, proteins, hormones, oxidative stress, inflammation, mitochondrial dysfunction, and associations with other mental disorders like anxiety and stress using traditional and medical intelligence (medical with AI). In addition, it also designs a comprehensive survey on detection, treatment planning, and genetic predisposition, along with future recommendations. This work is designed through different methods, including a systematic mapping process, literature review, and network visualization. In addition, we also used VOSviewer software and some authentic databases such as Google Scholar, Scopus, PubMed, and Web of Science for data collection, analysis, and designing comprehensive picture of the study. We analyzed 60 articles related to medical intelligence, including 47 from machine learning with 513,767 subjects (mean ± SD = 10,931.212 ± 35,624.372) and 13 from deep learning with 37,917 subjects (mean ± SD = 3159.75 ± 6285.57). Additionally, we also found that stressors impact the brain's cognitive and autonomic functioning, resulting in increased production of catecholamine, decreased cholinergic and glucocorticoid activity, with increased cortisol. These factors lead to chronic inflammation and hinder the brain's normal functioning, leading to depression, anxiety, and cardiovascular disorders. In the brain, reactive oxygen species (ROS) production is increased by IL-6 stimulation and mitochondrial cytochrome c oxidase is inhibited by nitric oxide, a potent inhibitor. Proteins, lipids, oxidative phosphorylation enzymes, and mtDNA are further disposed to oxidative impairment in the mitochondria. Consequently, mitochondrial dysfunction exacerbates oxidative stress, impairs mitochondrial DNA (mtDNA) or deletions of mtDNA, increases intracellular Ca2+ levels, changes in fission/fusion and mitochondrial morphology, and lastly leads to neuronal death. This study highlights the multidisciplinary approaches to depression with different aspects using traditional and medical intelligence. It will open a new way for depression research through new emerging technologies. [ABSTRACT FROM AUTHOR]

The hippocampus is one of the most commonly studied brain regions in the context of depression. The volume of the hippocampus is significantly reduced in patients with depression, which severely disrupts hippocampal neuroplasticity. However, antidepressant therapies that target hippocampal neuroplasticity have not been identified as yet. Chinese medicine (CM) can slow the progression of depression, potentially by modulating hippocampal neuroplasticity. Xiaoyaosan (XYS) is a CM formula that has been clinically used for the treatment of depression. It is known to protect Gan (Liver) and Pi (Spleen) function, and may exert its antidepressant effects by regulating hippocampal neuroplasticity. In this review, we have summarized the association between depression and aberrant hippocampal neuroplasticity. Furthermore, we have discussed the researches published in the last 30 years on the effects of XYS on hippocampal neuroplasticity in order to elucidate the possible mechanisms underlying its therapeutic action against depression. The results of this review can aid future research on XYS for the treatment of depression. [ABSTRACT FROM AUTHOR]

The adoption of machine learning (ML) and, more specifically, deep learning (DL) applications into all major areas of our lives is underway. The development of trustworthy AI is especially important in medicine due to the large implications for patients' lives. While trustworthiness concerns various aspects including ethical, transparency and safety requirements, we focus on the importance of data quality (training/test) in DL. Since data quality dictates the behaviour of ML products, evaluating data quality will play a key part in the regulatory approval of medical ML products. We perform a systematic review following PRISMA guidelines using the databases Web of Science, PubMed and ACM Digital Library. We identify 5408 studies, out of which 120 records fulfil our eligibility criteria. From this literature, we synthesise the existing knowledge on data quality frameworks and combine it with the perspective of ML applications in medicine. As a result, we propose the METRIC-framework, a specialised data quality framework for medical training data comprising 15 awareness dimensions, along which developers of medical ML applications should investigate the content of a dataset. This knowledge helps to reduce biases as a major source of unfairness, increase robustness, facilitate interpretability and thus lays the foundation for trustworthy AI in medicine. The METRIC-framework may serve as a base for systematically assessing training datasets, establishing reference datasets, and designing test datasets which has the potential to accelerate the approval of medical ML products. [ABSTRACT FROM AUTHOR]

Multimorbidity, i.e., two or more non-communicable diseases (NCDs), is an escalating challenge for society. Venous thromboembolism (VTE) is a common cardiovascular disease and it is unknown which multimorbidity clusters associates with VTE. Our aim was to examine the association between different common disease clusters of multimorbidity and VTE. The study is an extended (1997–2015) cross-sectional Swedish study using the National Patient Register and the Multigeneration Register. A total of 2,694,442 Swedish-born individuals were included in the study. Multimorbidity was defined by 45 NCDs. A principal component analysis (PCA) identified multimorbidity disease clusters. Odds ratios (OR) for VTE were calculated for the different multimorbidity disease clusters. There were 16% (n = 440,742) of multimorbid individuals in the study population. Forty-four of the individual 45 NCDs were associated with VTE. The PCA analysis identified nine multimorbidity disease clusters, F1-F9. Seven of these multimorbidity clusters were associated with VTE. The adjusted OR for VTE in the multimorbid patients was for the first three clusters: F1 (cardiometabolic diseases) 3.44 (95%CI 3.24–3.65), F2 (mental disorders) 2.25 (95%CI 2.14–2.37) and F3 (digestive system diseases) 4.35 (95%CI 3.63–5.22). There was an association between multimorbidity severity and OR for VTE. For instance, the occurrence of at least five diseases was in F1 and F2 associated with ORs for VTE: 8.17 (95%CI 6.32–10.55) and 6.31 (95%CI 4.34–9.17), respectively. In this nationwide study we have shown a strong association between VTE and different multimorbidity disease clusters that might be useful for VTE prediction. [ABSTRACT FROM AUTHOR]

Background: In 2019, the International Olympic Committee published the first Olympic and Paralympic Model Formulary (OPF), which defined the standardised set of medications required at every Olympic and Paralympic Games for the treatment of athletes. This study aimed to test the OPF to determine whether it meets the clinical needs of the athlete population with respect to medications used for pain and/or inflammation (PI), and to present a revised set of essential PI medications for the OPF based on prevalence of athlete use. Medication-use data of athletes at the Tokyo 2020 and Beijing 2022 Olympic Games (n = 6155) from three sources were used to establish prevalence of PI medicine use and to revise the OPF: (i) doping control forms, (ii) pharmacy dispensing reports, and (iii) injection declaration forms. This revised list was further validated through (iv) medication importation declarations by teams (n = 156), and (v) survey of team physicians (n = 382). Results: Overall prevalence of PI medication use was 36.7%, with higher use by female athletes (female: 44.1%; male: 30.0%; p < 0.001), with non-steroidal anti-inflammatory drugs being the most used class (27%). Use of medications with safety risks were identified, including nimesulide, piroxicam and metamizole. A revised list of 48 PI medications was recommended for the OPF. Conclusion: The research led to a revised set of essential medications for the treatment of pain and inflammation to be available for athletes at the Olympic Games, which would lead to a 7% improvement in the numbers of athletes who could have their exact PI medication requirements met by the OPF. Key Points: Medicines are provided at the Olympic Games for prescribing to athletes by international team physicians, with those most commonly prescribed being for treatment of pain and inflammation due to musculoskeletal sports injury. This study presents an optimal list of 48 essential medicines for the treatment of pain and inflammation to be available at every summer and winter Olympic Games, selected based on prevalence of athlete use, avoidance of harms, and reflecting the preferred treatment options of international team physicians. The results of this study will inform the selection of medicines to be available through the medical services within the athlete villages and competition venues at future Olympic Games. The implementation of this formulary is expected to result in 7% more athletes having access to their preferred choice of treatment for pain and inflammation due to sports injury. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to explore the characteristics of drug recall announcements issued over six years by the SFDA in Saudi Arabia. Additionally, to examine the patterns of voluntary drug recall requests by pharmaceutical companies (both innovator and generic) in response to product defects. Methods: A retrospective data analysis was conducted on drug recall announcements issued by the SFDA between 2017 and December 2022. The study included recalls of registered and unregistered drugs posted on the SFDA Drugs Circulars and Withdrawal webpage. Descriptive analysis was performed on relevant variables: recall year, therapeutic class, recall type, pharmaceutical company type, recall reasons and voluntary or involuntary product defect reports. Results: During the study period, a total of 371 products were recalled, with the majority being involuntary recalls (82.4%). About two-thirds of the recalls (66.0%) were related to registered products. The most common reasons for recalls were non-compliance with the manufacturer's specifications (33.2%), contamination (23.7%), and violations (20.5%). A total of 109 pharmaceutical companies were associated with the recalled products, with (85.3%) being generic pharmaceutical companies. The majority of innovator pharmaceutical companies (68.8%) requested voluntary drug recalls of defective products. Innovator pharmaceutical companies requested voluntary recalls more often than generic pharmaceutical companies. Conclusion: The study findings highlight the most frequent causes of drug recalls and the patterns of voluntary recall requests by pharmaceutical companies. Non-compliance with manufacturer's specifications was the most common reason for recalls. Significantly, more innovative pharmaceutical companies request voluntary recalls for product defects compared to generic pharmaceutical companies. [ABSTRACT FROM AUTHOR]

An improved organic semiconductor structure has been developed by using bulk heterostructures, i.e., p-i-n junctions, for effective wireless stimulation of cells, including neurons. Measures of photovoltage generated in response to irradiation of structures with brief pulses of red light with a safe radiation intensity were studied. The effectiveness of organic p-i-n semiconductor structures was confirmed by photostimulation of a neuroblast cell line. The results obtained support the conclusion that p-i-n organic semiconductor device technology can provide an alternative to modern silicon semiconductors in the field of medicine combined with bioelectronics for neurostimulation. [ABSTRACT FROM AUTHOR]

Copyright of Die Ophthalmologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Artificial Intelligence, specifically advanced language models such as ChatGPT, have the potential to revolutionize various aspects of healthcare, medical education, and research. In this narrative review, we evaluate the myriad applications of ChatGPT in diverse healthcare domains. We discuss its potential role in clinical decision-making, exploring how it can assist physicians by providing rapid, data-driven insights for diagnosis and treatment. We review the benefits of ChatGPT in personalized patient care, particularly in geriatric care, medication management, weight loss and nutrition, and physical activity guidance. We further delve into its potential to enhance medical research, through the analysis of large datasets, and the development of novel methodologies. In the realm of medical education, we investigate the utility of ChatGPT as an information retrieval tool and personalized learning resource for medical students and professionals. There are numerous promising applications of ChatGPT that will likely induce paradigm shifts in healthcare practice, education, and research. The use of ChatGPT may come with several benefits in areas such as clinical decision making, geriatric care, medication management, weight loss and nutrition, physical fitness, scientific research, and medical education. Nevertheless, it is important to note that issues surrounding ethics, data privacy, transparency, inaccuracy, and inadequacy persist. Prior to widespread use in medicine, it is imperative to objectively evaluate the impact of ChatGPT in a real-world setting using a risk-based approach. [ABSTRACT FROM AUTHOR]

Microbial life is not restricted to any particular setting. Over the past several decades, it has been evident that microbial populations can exist in a wide range of environments, including those with extremes in temperature, pressure, salinity, and pH. Bacteria and Archaea are the two most reported types of microbes that can sustain in extreme environments, such as hot springs, ice caves, acid drainage, and salt marshes. Some can even grow in toxic waste, organic solvents, and heavy metals. These microbes are called extremophiles. There exist certain microorganisms that are found capable of thriving in two or more extreme physiological conditions simultaneously, and are regarded as polyextremophiles. Extremophiles possess several physiological and molecular adaptations including production of extremolytes, ice nucleating proteins, pigments, extremozymes and exopolysaccharides. These metabolites are used in many biotechnological industries for making biofuels, developing new medicines, food additives, cryoprotective agents etc. Further, the study of extremophiles holds great significance in astrobiology. The current review summarizes the diversity of microorganisms inhabiting challenging environments and the biotechnological and therapeutic applications of the active metabolites obtained as a response to stress conditions. Bioprospection of extremophiles provides a progressive direction with significant enhancement in economy. Moreover, the introduction to omics approach including whole genome sequencing, single cell genomics, proteomics, metagenomics etc., has made it possible to find many unique microbial communities that could be otherwise difficult to cultivate using traditional methods. These findings might be capable enough to state that discovery of extremophiles can bring evolution to biotechnology. [ABSTRACT FROM AUTHOR]

The match rate of medical schools in the U.S. has been extensively studied from the perspective of applicants and influential factors. A method to objectively estimate the efficiency of a medical school's match rate has not been described in the literature. Such a method constitutes a significant improvement opportunity for medical schools via benchmarking best practices. This research fills the gap and proposes a bootstrap data envelopment analysis (DEA) framework to assess the residency match rate efficiency of medical schools. The efficacy of the proposed method is confirmed when benchmarking the Texas allopathic medical schools to representative samples of allopathic medical schools in the United States. The model allows to determine the statistical significance of differences in the residency match rate efficiency between groups of medical schools. The proposed bootstrap DEA approach is used to estimate the real efficiency's density function of 40 medical schools in the U.S. over the 2018–2020 period. The aggregate efficiency estimation showed that the medical schools are performing at a high competitive level; they have experienced a slight decline in scale efficiencies and have preserved high managerial performance. The study measured four groups: Texas medical schools, top ten ranked, middle ten ranked, and bottom ten ranked U.S. medical schools. The overall major improvement opportunity for medical schools is the scale of operations. Results confirm that medical schools are shown to be efficient in training future physicians. [ABSTRACT FROM AUTHOR]

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Understanding historical fiction as a hybrid space, both presenting the past and embracing the culture it is written in allows insights into the problems women face within the patriarchal society by setting them at a temporal distance. This paper explores the connections between witches, history, and medical practices in Amy McKay's The witches of New York. It first looks at how the novel queers history by introducing a female perspective embedded in memory-sharing. The paper then moves on to show how the novel contrasts gendered medicinal practices and segues into explaining how this contrast is superficial. The above serves as the ground for my key claim that, although the novel seeks to present an alternative to what it depicts as male-centric Western medical practices harmful to women, it fails to offer an alternative way of thinking. Even if it relies on the postfeminist discourse by seemingly presenting empowered feminine characters, these, under closer inspection, are shown to reinforce conservative notions of femininity through idealization of the figure of the female healer and gendered tropes of care. The paper concludes with the assertion that, although the novel aims to offer a meaningful alternative to the prevailing system of medical practice through its centering on female experiences, it cannot escape patriarchal discourses. [ABSTRACT FROM AUTHOR]

Increasing population and healthcare costs make changes in the healthcare system necessary. This article deals with ChatGPT's perspective on the future role of radiologists in the AI-driven hospital. This perspective will be augmented by further considerations by the author. AI-based imaging technologies and chatbots like ChatGPT can help improve radiologists' performance and workflow in the future AI-driven hospital. Although basic radiological examinations could be delivered without needing a radiologist, sophisticated imaging procedures will still need the expert opinion of a radiologist. [ABSTRACT FROM AUTHOR]

Background: Bow Hunter syndrome (BHS) is a rare, but important cause of posterior circulation stroke. It is also known as Rotational vertebral artery syndrome and is caused by transient dynamic vertebro-basilar insufficiency on movement of the neck in the presence of certain soft tissue or bone anomalies in the cranio-vertebral region. Case presentation: We present a case of Bow hunter syndrome in an 18-year-old adult male who presented with vomiting, occipital headache and loss of balance, with findings of posterior circulation stroke on imaging. Medical causes of young stroke, including vasculitis and clotting disorders were ruled out, following which a diagnostic conventional angiography and CT angiography was performed. The cause of vascular compromise in our case was the presence of vertebral anomalies, in particular, the presence of a partial ponticulus posticus with formation of an incomplete arcuate foramen. He was treated with cervical spine immobilisation and C1–C2 fixation. In addition, our patient had a single posterior inferior cerebellar artery (PICA) on the side of the dynamic insufficiency, which lead to bilateral cerebellar infarcts. Our case is unique because it demonstrates a combination of osseous and vascular developmental anomalies resulting in posterior circulation stroke. Conclusions: Though uncommon, BHS should be considered in the list of differentials in otherwise unexplained cases of posterior circulation stroke. Conventional angiography with dynamic manoeuvres is the modality of choice for documenting the rotational vertebral artery occlusion. [ABSTRACT FROM AUTHOR]

Deep learning approaches for clinical predictions based on magnetic resonance imaging data have shown great promise as a translational technology for diagnosis and prognosis in neurological disorders, but its clinical impact has been limited. This is partially attributed to the opaqueness of deep learning models, causing insufficient understanding of what underlies their decisions. To overcome this, we trained convolutional neural networks on structural brain scans to differentiate dementia patients from healthy controls, and applied layerwise relevance propagation to procure individual-level explanations of the model predictions. Through extensive validations we demonstrate that deviations recognized by the model corroborate existing knowledge of structural brain aberrations in dementia. By employing the explainable dementia classifier in a longitudinal dataset of patients with mild cognitive impairment, we show that the spatially rich explanations complement the model prediction when forecasting transition to dementia and help characterize the biological manifestation of disease in the individual brain. Overall, our work exemplifies the clinical potential of explainable artificial intelligence in precision medicine. [ABSTRACT FROM AUTHOR]

Introduction: The emergence of artificial intelligence (AI) chat programs has opened two distinct paths, one enhancing interaction and another potentially replacing personal understanding. Ethical and legal concerns arise due to the rapid development of these programs. This paper investigates academic discussions on AI in medicine, analyzing the context, frequency, and reasons behind these conversations. Methods: The study collected data from the Web of Science database on articles containing the keyword "ChatGPT" published from January to September 2023, resulting in 786 medically related journal articles. The inclusion criteria were peer-reviewed articles in English related to medicine. Results: The United States led in publications (38.1%), followed by India (15.5%) and China (7.0%). Keywords such as "patient" (16.7%), "research" (12%), and "performance" (10.6%) were prevalent. The Cureus Journal of Medical Science (11.8%) had the most publications, followed by the Annals of Biomedical Engineering (8.3%). August 2023 had the highest number of publications (29.3%), with significant growth between February to March and April to May. Medical General Internal (21.0%) was the most common category, followed by Surgery (15.4%) and Radiology (7.9%). Discussion: The prominence of India in ChatGPT research, despite lower research funding, indicates the platform's popularity and highlights the importance of monitoring its use for potential medical misinformation. China's interest in ChatGPT research suggests a focus on Natural Language Processing (NLP) AI applications, despite public bans on the platform. Cureus' success in publishing ChatGPT articles can be attributed to its open-access, rapid publication model. The study identifies research trends in plastic surgery, radiology, and obstetric gynecology, emphasizing the need for ethical considerations and reliability assessments in the application of ChatGPT in medical practice. Conclusion: ChatGPT's presence in medical literature is growing rapidly across various specialties, but concerns related to safety, privacy, and accuracy persist. More research is needed to assess its suitability for patient care and implications for non-medical use. Skepticism and thorough review of research are essential, as current studies may face retraction as more information emerges. [ABSTRACT FROM AUTHOR]

Religion and spirituality are powerful social forces in contemporary South Africa. Traditional Health Practitioners (THPs) are commonly consulted for both spiritual and medical ailments as a first line of care. Many studies have assessed African traditional health seeking behaviors but few have examined beliefs, practices, and behaviors of THPs themselves. This study sought to explore spiritual worldviews among South African THPs. Semi-structured in-depth interviews were conducted with 18 THPs in Johannesburg, South Africa between January and May, 2022. Interviews were transcribed and translated into English. Data were managed using NVivo 12 software and thematically analyzed. The majority of THPs interviewed indicated that initiation as a THP was almost always preceded by a sickness accompanied by dreams/visions that represented an "ancestral calling" to become a healer. Most THPs also trained as both sangomas—who healed according to traditional beliefs—and prophets—who healed according to Christian beliefs. This reflects a syncretic relationship between traditional African beliefs and Christianity. However, not all churches are accepting of traditional beliefs and subsequently these THPs are members only at non-Pentecostal AIC churches who blend both African and Christian practices. Similar to these forms of religious pluralism melding Christianity and traditional beliefs, many THPs also often practice medical pluralism, mixing Western treatments with traditional practices/medicines. THPs are able to adapt elements of Western and African beliefs into healing practices that span multiple religious and medical fields. Thus, collaborative and decentralized healthcare services may be highly acceptable among such a pluralistic community. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the reliability and validity of the Artificial Intelligence Performance Instrument (AIPI). Methods: Medical records of patients consulting in otolaryngology were evaluated by physicians and ChatGPT for differential diagnosis, management, and treatment. The ChatGPT performance was rated twice using AIPI within a 7-day period to assess test–retest reliability. Internal consistency was evaluated using Cronbach's α. Internal validity was evaluated by comparing the AIPI scores of the clinical cases rated by ChatGPT and 2 blinded practitioners. Convergent validity was measured by comparing the AIPI score with a modified version of the Ottawa Clinical Assessment Tool (OCAT). Interrater reliability was assessed using Kendall's tau. Results: Forty-five patients completed the evaluations (28 females). The AIPI Cronbach's alpha analysis suggested an adequate internal consistency (α = 0.754). The test–retest reliability was moderate-to-strong for items and the total score of AIPI (rs = 0.486, p = 0.001). The mean AIPI score of the senior otolaryngologist was significantly higher compared to the score of ChatGPT, supporting adequate internal validity (p = 0.001). Convergent validity reported a moderate and significant correlation between AIPI and modified OCAT (rs = 0.319; p = 0.044). The interrater reliability reported significant positive concordance between both otolaryngologists for the patient feature, diagnostic, additional examination, and treatment subscores as well as for the AIPI total score. Conclusions: AIPI is a valid and reliable instrument in assessing the performance of ChatGPT in ear, nose and throat conditions. Future studies are needed to investigate the usefulness of AIPI in medicine and surgery, and to evaluate the psychometric properties in these fields. [ABSTRACT FROM AUTHOR]

The transformation and implementation of clinical practice guidelines for integrated traditional Chinese medicine (TCM) and Western medicine (WM) is crucial to the adoption of medical science and technological findings and is an important way for TCM to be made available to the world. First, clinical practice guidelines (CPGs) of TCM and WM integration in recent years was analyzed to clarify the current situation and problems in the existing guidelines according to the following four perspectives: (1) perspective of TCM and WM integration in guidelines, (2) diagnosis Using integrated TCM and WM, (3) integration of TCM and WM treatment, (4) promoting TCM and WM integration. Secondly, the information and quality evaluation of CPGs for integrated Chinese and Western medicine in 2020–2022 were analyzed to explore the degree and methods of integration of Chinese and Western medicine guidelines. And last this study aimed to lay a foundation for the further establishment of Chinese characteristic, repeatable, and calculable clinical practice guidelines of TCM and WM integration. [ABSTRACT FROM AUTHOR]

Background: Medical interaction and exploration techniques are the most important tools that medical students have to acquire in the subject of psychiatry and psychotherapy. The new digital technologies currently available, such as virtual reality (VR), as important supplements can contribute to a significant improvement in the teaching of psychiatric-psychopathological learning content as well as, in particular, the technique of ascertaining the psychiatric history and diagnosis. Objective: Evaluation of the Bochum Avatar Exploration Project (AVEX) as part of the curricular course in medical studies at the Ruhr University Bochum for its possibilities to convey learning content and techniques of anamnesis and diagnosis in the subject of psychiatry and psychotherapy. Methods: In AVEX, a total of 87 medical students in the clinical study section have so far been able to enter into a dialogue with "mentally ill" avatars and gain experience with VR technology as a learning and teaching method in the subject of psychiatry and psychotherapy. Results: Despite the limited possibilities for interaction with the digital avatars, it is possible to achieve a substantial transfer of learning content in psychiatry; however, the students must be well supported by the lecturers. Conclusion: The AVEX project already shows promising possibilities for supplementing the teaching of medical students, even if the fit of questions and replies in dialogue with the virtual avatars still needs to be improved. As advances in the linguistic communication of emotions and the visual effects of the avatar representation can be predicted, the significance of this technology will continue to increase. [ABSTRACT FROM AUTHOR]

Objective: This paper offers a mini-review of OpenAI's language model, ChatGPT, detailing its mechanisms, applications in healthcare, and comparisons with other large language models (LLMs). Methods: The underlying technology of ChatGPT is outlined, focusing on its neural network architecture, training process, and the role of key elements such as input embedding, encoder, decoder, attention mechanism, and output projection. The advancements in GPT-4, including its capacity for internet connection and the integration of plugins for enhanced functionality are discussed. Results: ChatGPT can generate creative, coherent, and contextually relevant sentences, making it a valuable tool in healthcare for patient engagement, medical education, and clinical decision support. Yet, like other LLMs, it has limitations, including a lack of common sense knowledge, a propensity for hallucination of facts, a restricted context window, and potential privacy concerns. Conclusion: Despite the limitations, LLMs like ChatGPT offer transformative possibilities for healthcare. With ongoing research in model interpretability, common-sense reasoning, and handling of longer context windows, their potential is vast. It is crucial for healthcare professionals to remain informed about these technologies and consider their ethical integration into practice. [ABSTRACT FROM AUTHOR]

This paper discusses the objective reasons for the growing need for qualified medical services, especially in the context of pandemics, wars, disasters, and shortages of medical staff. The directions of development of robotics in medicine are analyzed, leading to the conclusion that the use of intelligent collaborative (i.e., interacting with humans) robotics in medicine is possible and appropriate. Robot assistants can perform many auxiliary operations, which will help relieve the workload of middle and junior medical staff, and, if necessary, replace them when performing such operations. The article outlines some of the main directions in which the authors believe the development of collaborative medical robotics should occur. [ABSTRACT FROM AUTHOR]

Animals are frequently utilized as a teaching-learning tool in multiple educational settings. It is, therefore, important to understand what students think about this topic, in particular medical and veterinary students as "life caregivers" and competent people for a dynamic and responsible social intervention. In this context, this research aims to characterize and disseminate a set of issues related to animal welfare/wellbeing in higher education in the North of Portugal, particularly as regards the teaching of students of the Integrated Master in Medicine (MIM) and Veterinary Medicine (MIMV). After ethical approval, a survey was delivered on paper to 180 undergraduate MIM (n = 100) and MIMV (n = 80) students. After collecting 139 questionnaires partially or fully completed, with varying response rates for each question, it was concluded that most of the students consider that animal experimentation is ethically acceptable when the benefits balance the harms and assuming that refinement of animal procedures is warranted; they also agree to the establishment, maintenance and performance of animal procedures solely for educational purposes as a way of ensuring optimal acquisition of theoretical knowledge, attitudes and behaviors and technical skills. Nevertheless, it is the responsibility of teachers to explore and implement pedagogical methodologies thar are equally effective but more humane and compassionate towards sentient living beings. [ABSTRACT FROM AUTHOR]

This article, titled "Two Perspectives," explores the themes of life, medicine, relationships, and conversation. It is written in the form of two chapters, each presenting a different perspective. The first chapter reflects on the beauty and gratitude of existence, even in the face of limitations and the inability to fully experience the world. The second chapter contemplates the transience of life and the act of sparing someone from the burdens and inevitable disappointments of existence. The article concludes by stating that the data mentioned in the article is available within the article itself or its supplementary materials. [Extracted from the article]

The study designed as a randomized controlled trial included 140 pregnant women. Pregnant women in both groups were administered VAS and Childbirth Comfort Questionnaire (CCQ) Approximately two hours after delivery, Postpartum Comfort Questionnaire (PPCQ) was administered, and on the postpartum 15th day, the Scale for Traumatic Childbirth Perception (STCP) was applied. VAS scores and STCP scores of the pregnant women in the intervention group were found to be significantly lower compared to the scores of the women in the control group, while their CCQ and PPCQ scores were determined to be significantly higher. [ABSTRACT FROM AUTHOR]

In 2021, a large urban university-based hospital transitioned to a new two-floor emergency department. Despite the new environment, there were usability and workflow challenges with the space. The authors of this paper created a multidisciplinary, human-centered design collaborative of clinicians, university faculty, and students in an effort to increase emergency department efficiency. After thorough design-research and clinician-focused collaboration, the authors and design team identified the need to improve medical supply retrieval time, which directly impacts patient care and clinician satisfaction. The primary interventions consisted of a redesign that is as follows: (a) created standardized icons related to organ system, (b) increased visibility of supply labels, and (c) reorganized supplies based on usage data. Although a successful project, it was not without several barriers discussed in this article, including design researcher and clinician-level setting and engagement, academic/institutional policies, and conflicting schedules. In addition, the lessons learned from implementing human-centered design concepts into clinical workflow sets forth future research opportunities and inspiration for other institutions to collaborate. [ABSTRACT FROM AUTHOR]

One of the major barriers to using large language models (LLMs) in medicine is the perception they use uninterpretable methods to make clinical decisions that are inherently different from the cognitive processes of clinicians. In this manuscript we develop diagnostic reasoning prompts to study whether LLMs can imitate clinical reasoning while accurately forming a diagnosis. We find that GPT-4 can be prompted to mimic the common clinical reasoning processes of clinicians without sacrificing diagnostic accuracy. This is significant because an LLM that can imitate clinical reasoning to provide an interpretable rationale offers physicians a means to evaluate whether an LLMs response is likely correct and can be trusted for patient care. Prompting methods that use diagnostic reasoning have the potential to mitigate the "black box" limitations of LLMs, bringing them one step closer to safe and effective use in medicine. [ABSTRACT FROM AUTHOR]

Blockchain and machine learning are two rapidly growing technologies that are increasingly being used in various industries. Blockchain technology provides a secure and transparent method for recording transactions, while machine learning enables data-driven decision-making by analyzing large amounts of data. In recent years, researchers and practitioners have been exploring the potential benefits of combining these two technologies. In this study, we cover the fundamentals of blockchain and machine learning and then discuss their integrated use in finance, medicine, supply chain, and security, including a literature review and their contribution to the field such as increased security, privacy, and decentralization. Blockchain technology enables secure and transparent decentralized record-keeping, while machine learning algorithms can analyze vast amounts of data to derive valuable insights. Together, they have the potential to revolutionize industries by enhancing efficiency through automated and trustworthy processes, enabling data-driven decision-making, and strengthening security measures by reducing vulnerabilities and ensuring the integrity of information. However, there are still some important challenges to be handled prior to the common use of blockchain and machine learning such as security issues, strategic planning, information processing, and scalable workflows. Nevertheless, until the difficulties that have been identified are resolved, their full potential will not be achieved. [ABSTRACT FROM AUTHOR]