Exponential growth of systematic reviews assessing artificial intelligence studies in medicine: challenges and opportunities.
Ontology highlight
ABSTRACT:
SUBMITTER: von Groote T
PROVIDER: S-EPMC9238033 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Clustered regularly interspaced short palindromic repeat (CRISPR)-based genome editing (GED) technologies have unlocked exciting possibilities for understanding genes and improving medical treatments. On the other hand, Artificial intelligence (AI) helps genome editing achieve more precision, efficiency, and affordability in tackling various diseases, like Sickle cell anemia or Thalassemia. AI models have been in use for designing guide RNAs (gRNAs) for CRISPR-Cas systems. Tools like DeepCRISPR, CRISTA, and DeepHF have the capability to predict optimal guide RNAs (gRNAs) for a specified target sequence. These predictions take into account multiple factors, including genomic context, Cas protein type, desired mutation type, on-target/off-target scores, potential off-target sites, and the potential impacts of genome editing on gene function and cell phenotype. These models aid in optimizing different genome editing technologies, such as base, prime, and epigenome editing, which are advanced techniques to introduce precise and programmable changes to DNA sequences without relying on the homology-directed repair pathway or donor DNA templates. Furthermore, AI, in collaboration with genome editing and precision medicine, enables personalized treatments based on genetic profiles. AI analyzes patients' genomic data to identify mutations, variations, and biomarkers associated with different diseases like Cancer, Diabetes, Alzheimer's, etc. However, several challenges persist, including high costs, off-target editing, suitable delivery methods for CRISPR cargoes, improving editing efficiency, and ensuring safety in clinical applications. This review explores AI's contribution to improving CRISPR-based genome editing technologies and addresses existing challenges. It also discusses potential areas for future research in AI-driven CRISPR-based genome editing technologies. The integration of AI and genome editing opens up new possibilities for genetics, biomedicine, and healthcare, with significant implications for human health.
| S-EPMC10800897 | biostudies-literature
Project description:As artificial intelligence (AI) systems begin to make their way into clinical radiology practice, it is crucial to assure that they function correctly and that they gain the trust of experts. Toward this goal, approaches to make AI "interpretable" have gained attention to enhance the understanding of a machine learning algorithm, despite its complexity. This article aims to provide insights into the current state of the art of interpretability methods for radiology AI. This review discusses radiologists' opinions on the topic and suggests trends and challenges that need to be addressed to effectively streamline interpretability methods in clinical practice. Supplemental material is available for this article. © RSNA, 2020 See also the commentary by Gastounioti and Kontos in this issue.
| S-EPMC7259808 | biostudies-literature
Project description:BackgroundArtificial intelligence (AI) applications that utilize machine learning are on the rise in clinical research and provide highly promising applications in specific use cases. However, wide clinical adoption remains far off. This review reflects on common barriers and current solution approaches.SummaryKey challenges are abbreviated as the RISE criteria: Regulatory aspects, Interpretability, interoperability, and the need for Structured data and Evidence. As reoccurring barriers of AI adoption, these concepts are delineated and complemented by points to consider and possible solutions for effective and safe use of AI applications.Key messagesThere is a fraction of AI applications with proven clinical benefits and regulatory approval. Many new promising systems are the subject of current research but share common issues for wide clinical adoption. The RISE criteria can support preparation for challenges and pitfalls when designing or introducing AI applications into clinical practice.
| S-EPMC7768160 | biostudies-literature
Project description:Recently, there has been great interest in developing Artificial Intelligence (AI) enabled computer-aided diagnostics solutions for the diagnosis of skin cancer. With the increasing incidence of skin cancers, low awareness among a growing population, and a lack of adequate clinical expertise and services, there is an immediate need for AI systems to assist clinicians in this domain. A large number of skin lesion datasets are available publicly, and researchers have developed AI solutions, particularly deep learning algorithms, to distinguish malignant skin lesions from benign lesions in different image modalities such as dermoscopic, clinical, and histopathology images. Despite the various claims of AI systems achieving higher accuracy than dermatologists in the classification of different skin lesions, these AI systems are still in the very early stages of clinical application in terms of being ready to aid clinicians in the diagnosis of skin cancers. In this review, we discuss advancements in the digital image-based AI solutions for the diagnosis of skin cancer, along with some challenges and future opportunities to improve these AI systems to support dermatologists and enhance their ability to diagnose skin cancer.
| S-EPMC8290363 | biostudies-literature
Project description:Artificial metalloenzymes (ArMs) result from the incorporation of an abiotic metal cofactor within a protein scaffold. From the earliest techniques of transition metals adsorbed on silk fibers, the field of ArMs has expanded dramatically over the past 60 years to encompass a range of reaction classes and inspired approaches: Assembly of the ArMs has taken multiple forms with both covalent and supramolecular anchoring strategies, while the scaffolds have been intuitively selected and evolved, repurposed, or designed in silico. Herein, we discuss some of the most prominent recent examples of ArMs to highlight the challenges and opportunities presented by the field.
| S-EPMC6661864 | biostudies-literature
Project description: Not available
| S-EPMC9123184 | biostudies-literature
Project description:Personalized medicine is health care that tailors interventions to individual variation in risk and treatment response. Although medicine has long strived to achieve this goal, advances in genomics promise to facilitate this process. Relevant to present-day practice is the use of genomic information to classify individuals according to disease susceptibility or expected responsiveness to a pharmacologic treatment and to provide targeted interventions. A symposium at the annual meeting of the Society for Medical Decision Making on 23 October 2007 highlighted the challenges and opportunities posed in translating advances in molecular medicine into clinical practice. A panel of US experts in medical practice, regulatory policy, technology assessment, and the financing and organization of medical innovation was asked to discuss the current state of practice and research on personalized medicine as it relates to their own field. This article reports on the issues raised, discusses potential approaches to meet these challenges, and proposes directions for future work. The case of genetic testing to inform dosing with warfarin, an anticoagulant, is used to illustrate differing perspectives on evidence and decision making for personalized medicine.
| S-EPMC4598076 | biostudies-other
Project description:Artificial intelligence (AI) has witnessed rapid advances in the healthcare domain in recent years, especially in the emergency field, where AI is likely to radically reshape medical service delivery. Although AI has substantial potential to enhance diagnostic accuracy and operational efficiency in hospitals, research on its applications in Emergency Department building design remains relatively scarce. Therefore, this study aims to investigate Emergency Department facility design by identifying the challenges and opportunities of using AI. Two systematic literature reviews are combined, one in AI and the other in sensors, to explore their potential application to support decision-making, resource optimisation and patient monitoring. These reviews have then informed a discussion on integrating AI sensors in contemporary Emergency Department designs for use in China to support the evidence base on resuscitation units, emergency operating rooms and Emergency Department Intensive Care Unit (ED-ICU) design. We hope to inform the strategic implementation of AI sensors and how they might transform Emergency Department design to support medical staff and enhance the patient experience.
| S-EPMC10991761 | biostudies-literature
Project description:Artificial intelligence-powered medical technologies are rapidly evolving into applicable solutions for clinical practice. Deep learning algorithms can deal with increasing amounts of data provided by wearables, smartphones, and other mobile monitoring sensors in different areas of medicine. Currently, only very specific settings in clinical practice benefit from the application of artificial intelligence, such as the detection of atrial fibrillation, epilepsy seizures, and hypoglycemia, or the diagnosis of disease based on histopathological examination or medical imaging. The implementation of augmented medicine is long-awaited by patients because it allows for a greater autonomy and a more personalized treatment, however, it is met with resistance from physicians which were not prepared for such an evolution of clinical practice. This phenomenon also creates the need to validate these modern tools with traditional clinical trials, debate the educational upgrade of the medical curriculum in light of digital medicine as well as ethical consideration of the ongoing connected monitoring. The aim of this paper is to discuss recent scientific literature and provide a perspective on the benefits, future opportunities and risks of established artificial intelligence applications in clinical practice on physicians, healthcare institutions, medical education, and bioethics.
| S-EPMC7012990 | biostudies-literature
Project description:Blood vessel development is one of the most prominent steps in regenerative medicine due to the restoration of blood flow to the ischemic tissues and providing the rapid vascularization in clinical-sized tissue-engineered grafts. However, currently tissue engineering technique is restricted because of the inadequate in vitro/in vivo tissue vascularization. Some challenges like as transportation in large scale, distribution of the nutrients and poor oxygen diffusion limit the progression of vessels in smaller than clinically relevant dimensions as well in vivo integration. In this regard, the scholars attempted to promote the vascularization process relied on the stem cells (SCs), growth factors as well as exosomes and interactions of biomaterials with all of them to enable the emergence of ideal microenvironment which is needed for treatment of unhealthy organs or tissue regeneration and formation of new blood vessels. Thus, in the present review we aim to describe these approaches, advances, obstacles and opportunities as well as their application in regeneration of heart as a prominent angiogenesis-dependent organ.
| S-EPMC7539317 | biostudies-literature