Hasil untuk "Medical technology"

W. McGaghie, S. Issenberg, E. Petrusa et al.

R. Hillestad, J. Bigelow, A. Bower et al.

A. Marro, T. Bandukwala, Walter H Mak

Igor Radanovic, R. Likić

Sonia Katyal

Twenty-five years ago, Joel Reidenberg argued that technology itself, not just law and regulation, imposes rules on communities in the Information Society. System design choices like network architecture and configurations create regulatory norms he termed "Lex Informatica"-referencing the merchant-driven medieval "Lex Mercatoria" that emerged independent of sovereign control. Today we face different challenges requiring us to revisit Reidenberg's insights and examine the consequences of that earlier era. While Lex Informatica provided a framework for analyzing the internet's birth, we now confront the aftereffects of decades of minimal or absent regulation. Critical questions emerge: When technological social norms develop outside clear legal restraints, who benefits and who suffers? This new era demands infrastructural reform focused on the interplay between public and private regulation and self-regulation, weighing both costs and benefits. Rather than showcasing the promise of yesterday's internet age, today's events reveal the pitfalls of information libertarianism and underscore the urgent need for new approaches to information regulation. This Issue presents articles from two symposiums-one on Lex Informatica and another on race and technology law. Their conversation is now essential. Together, these papers demonstrate what I call the "Lex Reformatica" of today's digital age. This collection shows why scholars, lawyers, and legislators must return to Reidenberg's foundational work and update its trajectory toward a reform-focused approach designed for our current era.

S. Radenkovic, M. Dugic, I. Radojevic

The answers on the current status and future development of Quantum Science and Technology are presented.

Anas Zafar, Leema Krishna Murali, Ashish Vashist

Recent work shows that text-only reinforcement learning with verifiable rewards (RLVR) can match or outperform image-text RLVR on multimodal medical VQA benchmarks, suggesting current evaluation protocols may fail to measure causal visual dependence. We introduce a counterfactual evaluation framework using real, blank, and shuffled images across four medical VQA benchmarks: PathVQA, PMC-VQA, SLAKE, and VQA-RAD. Beyond accuracy, we measure Visual Reliance Score (VRS), Image Sensitivity (IS), and introduce Hallucinated Visual Reasoning Rate (HVRR) to detect cases where models generate visual claims despite producing image-invariant answers. Our findings reveal that RLVR improves accuracy while degrading visual grounding: text-only RLVR achieves negative VRS on PathVQA (-0.09), performing better with mismatched images, while image-text RLVR reduces image sensitivity to 39.8% overall despite improving accuracy. On VQA-RAD, both variants achieve 63% accuracy through different mechanisms: text-only RLVR retains 81% performance with blank images, while image-text RLVR shows only 29% image sensitivity. Models generate visual claims in 68-74% of responses, yet 38-43% are ungrounded (HVRR). These findings demonstrate that accuracy-only rewards enable shortcut exploitation, and progress requires grounding-aware evaluation protocols and training objectives that explicitly enforce visual dependence.

Anuraag A. Vazirani, Odhran O'Donoghue, D. Brindley et al.

The lack of interoperability in Britain’s medical records systems precludes the realisation of benefits generated by increased spending elsewhere in healthcare. Growing concerns regarding the security of online medical data following breaches, and regarding regulations governing data ownership, mandate strict parameters in the development of efficient methods to administrate medical records. Furthermore, consideration must be placed on the rise of connected devices, which vastly increase the amount of data that can be collected in order to improve a patient’s long-term health outcomes. Increasing numbers of healthcare systems are developing Blockchain-based systems to manage medical data. A Blockchain is a decentralised, continuously growing online ledger of records, validated by members of the network. Traditionally used to manage cryptocurrency records, distributed ledger technology can be applied to various aspects of healthcare. In this manuscript, we focus on how Electronic Medical Records in particular can be managed by Blockchain, and how the introduction of this novel technology can create a more efficient and interoperable infrastructure to manage records that leads to improved healthcare outcomes, while maintaining patient data ownership and without compromising privacy or security of sensitive data.

Robert A. Winn, K. Watson

J. Sutherland, J. Belec, A. Sheikh et al.

R. Biran, D. Pond

&NA; Blood contact with biomaterials triggers activation of multiple reactive mechanisms that can impair the performance of implantable medical devices and potentially cause serious adverse clinical events. This includes thrombosis and thromboembolic complications due to activation of platelets and the coagulation cascade, activation of the complement system, and inflammation. Numerous surface coatings have been developed to improve blood compatibility of biomaterials. For more than thirty years, the anticoagulant drug heparin has been employed as a covalently immobilized surface coating on a variety of medical devices. This review describes the fundamental principles of non‐eluting heparin coatings, mechanisms of action, and clinical applications with focus on those technologies which have been commercialized. Because of its extensive publication history, there is emphasis on the CARMEDA® BioActive Surface (CBAS® Heparin Surface), a widely used commercialized technology for the covalent bonding of heparin. Graphical abstract Figure. No caption available.

Jing Mo, Qiling Cai, Shanshan Chen et al.

Summary: A growing body of research suggests that inhibition of autophagy may be a novel means of treating cancer and suppressing drug resistance. Therefore, a series of drugs derived from the Erythrina crista-galli Linn were screened in this study. Among them, the pterocarpan erythrabyssin II (EL-19) is a potent late-stage autophagy inhibitor, which could effectively block the fusion of autophagosome and lysosome, leading to the accumulation of autophagic substrates in both ovarian cancer A2780 and A2780/DDP cells. EL-19 did not impair the lysosomal pH and lysosomal enzyme activity. In addition, cell studies, and organoid experiments showed that EL-19 inhibited the value addition of A2780 and A2780/DDP cells, suppressed ovarian cancer organoid activity and induced apoptosis, and blocked cisplatin-induced protective autophagy in A2780/DDP cells. Combination therapy with DDP superior anti-tumor outcomes compared to monotherapies in animal models. In summary, EL-19 may be developed as an anticancer agent by blocking chemotherapy-induced protective autophagy.

Francisca Ogochukwu Onukansi, Collins Chibueze Anokwuru, Stanley Chinedu Eneh et al.

Abstract Traditional medicine (TM) has been a cornerstone of healthcare across various cultures, especially in Africa, where it has played an integral role in the management of diseases such as malaria. Despite the popularity and historical significance of TM, scientific validation remains a key challenge, hindering its widespread acceptance in modern healthcare systems. This study explores the potential of traditional African medicine, particularly in the context of Nigeria, as a vital resource in the fight against malaria. Drawing on the success of plants like Artemisia annua in the development of modern anti-malarial drugs, the research emphasizes the need for comprehensive investment in TM research. With Nigeria facing the highest malaria burden globally, the research advocates for increased funding, scientific investigations into the efficacy of traditional remedies, and enhanced regulation of herbal medicine. The paper also highlights the growing trust and reliance on herbal remedies in rural areas of Nigeria and the importance of ensuring their safety through pharmacological testing. This study examines these issues through an analysis of existing literature, historical applications, and documented successes of herbal treatments. By integrating traditional medicine into national health systems, Nigeria could unlock new strategies for combating malaria and other infectious diseases, advancing toward sustainable health outcomes.

Elizabeth Dyer, Barbara Swartzlander, M. Gugliucci

Objective The project adopted technology that teaches medical and other health professions students to be empathetic with older adults, through virtual reality (VR) software that allows them to simulate being a patient with age-related diseases, and to familiarize medical students with information resources related to the health of older adults. Methods The project uses an application that creates immersive VR experiences for training of the workforce for aging services. Users experience age-related conditions such as macular degeneration and high-frequency hearing loss from the patient’s perspective. Librarians and faculty partner to integrate the experience into the curriculum, and students go to the library at their convenience to do the VR assignment. Results The project successfully introduced an innovative new teaching modality to the medical, physician assistant, physical therapy, and nursing curricula. Results show that VR enhanced students’ understanding of age-related health problems and increased their empathy for older adults with vision and hearing loss or Alzheimer’s disease. Conclusion VR immersion training is an effective teaching method to help medical and health professions students develop empathy and is a budding area for library partnerships. As the technology becomes more affordable and accessible, it is important to develop best practices for using VR in the library.

Joshua Hatherley

Artificial intelligence (AI) is expected to revolutionise the practice of medicine. Recent advancements in the field of deep learning have demonstrated success in variety of clinical tasks: detecting diabetic retinopathy from images, predicting hospital readmissions, aiding in the discovery of new drugs, etc. AI’s progress in medicine, however, has led to concerns regarding the potential effects of this technology on relationships of trust in clinical practice. In this paper, I will argue that there is merit to these concerns, since AI systems can be relied on, and are capable of reliability, but cannot be trusted, and are not capable of trustworthiness. Insofar as patients are required to rely on AI systems for their medical decision-making, there is potential for this to produce a deficit of trust in relationships in clinical practice.

Abid Haleem, M. Javaid, I. Khan

Abstract Background/objectives In the current scenario, artificial intelligence (AI) is going to change almost all the areas of the medical field. The need is to study the research carried out in this technology and identify its different applications in the medical field. Methods Studies and research articles on “artificial intelligence” and “artificial intelligence in the medical field” have identified significant applications of AI. This article explores and shows how AI helps to solve challenging problems in the medical field through extensive research and development. Results The study identified five significant technologies as being used in AI in the medical field and the process of implementing AI. Finally, this article identifies ten primary applications of AI in the medical field, along with a brief description. AI provides a productive clinical decision to improve patient outcomes. Conclusions Different technologies are adopted and experimented with increasing automation in the medical field. Nowadays, AI is being introduced in the medical field to keep a medical record in digital format and conduct patient checkup using smart technologies. It provides solutions, especially in targeted treatments, uniquely composed drugs and personalised therapies. AI is an innovative technology that helps to guide the surgeon during medication, treatment and operation. The main application of this technology is for better decision-making for complicated cases. It can also help to track, detect, investigate and control the infection in the hospital. This technology develops and optimises online patient appointment platforms. In future, it will be helpful in all medical areas to serve humanity.

C. Andrews, Michael K. Southworth, J. Silva et al.

Seyyed Mohsen Azizi, N. Roozbahani, Alireza Khatony

Blended learning is a new approach to improving the quality of medical education. Acceptance of blended learning plays an important role in its effective implementation. Therefore, the purpose of this study was to investigate and determine the factors that might affect students’ intention to use blended learning. In this cross-sectional, correlational study, the sample consisted of 225 Iranian medical sciences students. The theoretical framework for designing the conceptual model was the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Venkatesh et al. (2012) proposed UTAUT2 as a framework to explain a person’s behavior while using technology. Data were analyzed using SPSS-18 and AMOS-23 software. Structural equation modeling technique was used to test the hypotheses. The validity and reliability of the model constructs were acceptable. Performance Expectance (PE), Effort Expectance (EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), Price Value (PV) and Habit (HT) had a significant effect on the students’ behavioral intention to use blended learning. Additionally, behavioral intention to use blended learning had a significant effect on the students’ actual use of blended learning (β = 0.645, P ≤ 0.01). The study revealed that the proposed framework based on the UTAUT2 had good potential to identify the factors influencing the students’ behavioral intention to use blended learning. Universities can use the results of this study to design and implement successful blended learning courses in medical education.

Ryuji Hamamoto, K. Suvarna, Masayoshi Yamada et al.

Simple Summary Artificial intelligence (AI) technology has been advancing rapidly in recent years and is being implemented in society. The medical field is no exception, and the clinical implementation of AI-equipped medical devices is steadily progressing. In particular, AI is expected to play an important role in realizing the current global trend of precision medicine. In this review, we introduce the history of AI as well as the state of the art of medical AI, focusing on the field of oncology. We also describe the current status of the use of AI for drug discovery in the oncology field. Furthermore, while AI has great potential, there are still many issues that need to be resolved; therefore, we would provide details on current medical AI problems and potential solutions. Abstract In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, “precision medicine,” a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.

Xu Cheng, Fulong Chen, Dong Xie et al.