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Annales de Biologie Clinique 2003Application fields of RT-PCR (reverse transcription-polymerase chain reaction) in clinical diagnosis comprises the assessment of viral load for RNA viruses and the... (Review)
Review
Application fields of RT-PCR (reverse transcription-polymerase chain reaction) in clinical diagnosis comprises the assessment of viral load for RNA viruses and the analysis of gene transcription products. RT-PCR is also helpful when large genes have to be sequenced. Developments of quantitative approaches using real-time PCR recently led to a major widening of RT-PCR applications in clinical diagnosis. However, RT reaction is delicate due to its lack of reproducibility and to RNA lability and frequent contamination by DNA. In some cases additional difficulties come from the need to obtain a specific amplification in the presence of homologous sequences which might be present in higher amounts than the sequence of interest. These caveats have to be taken into account, when designing the RT protocol, and when choosing PCR primers and internal and/or external references. This review is aimed at helping the experimental setup of a RT-PCR based assay according to the objectives.
Topics: Clinical Medicine; Diagnostic Techniques and Procedures; Humans; Reverse Transcriptase Polymerase Chain Reaction
PubMed: 14711604
DOI: No ID Found -
Medical Science Monitor : International... Jun 2021Artificial intelligence (AI) in clinical medicine includes physical robotics and devices and virtual AI and machine learning. Concerns have been raised regarding ethical...
Artificial intelligence (AI) in clinical medicine includes physical robotics and devices and virtual AI and machine learning. Concerns have been raised regarding ethical issues for the use of AI in surgery, including guidance for surgical decisions, patient confidentiality, and the need for support from controlled clinical trials to use these methods so that clinical guidelines can be developed. The most common applications for virtual AI include disease diagnosis, health monitoring and digital patient consultations, clinical training, patient data management, drug development, and personalized medicine. In September 2020, the CONSORT-A1 extension was developed with 14 additional items that should be reported for AI studies that include clear descriptions of the AI intervention, skills required, study setting, inputs and outputs of the AI intervention, analysis of errors, and the human and AI interactions. This Editorial aims to present current applications and challenges of AI in clinical medicine and the importance of the new 2020 CONSORT-AI study guidelines.
Topics: Artificial Intelligence; Clinical Medicine; Ethics, Clinical; Humans; Practice Guidelines as Topic; Research Design; Surgical Procedures, Operative
PubMed: 34176921
DOI: 10.12659/MSM.933675 -
Chinese Medical Journal Mar 2016To review theories and technologies of big data mining and their application in clinical medicine. (Review)
Review
OBJECTIVE
To review theories and technologies of big data mining and their application in clinical medicine.
DATA SOURCES
Literatures published in English or Chinese regarding theories and technologies of big data mining and the concrete applications of data mining technology in clinical medicine were obtained from PubMed and Chinese Hospital Knowledge Database from 1975 to 2015.
STUDY SELECTION
Original articles regarding big data mining theory/technology and big data mining's application in the medical field were selected.
RESULTS
This review characterized the basic theories and technologies of big data mining including fuzzy theory, rough set theory, cloud theory, Dempster-Shafer theory, artificial neural network, genetic algorithm, inductive learning theory, Bayesian network, decision tree, pattern recognition, high-performance computing, and statistical analysis. The application of big data mining in clinical medicine was analyzed in the fields of disease risk assessment, clinical decision support, prediction of disease development, guidance of rational use of drugs, medical management, and evidence-based medicine.
CONCLUSION
Big data mining has the potential to play an important role in clinical medicine.
Topics: Bayes Theorem; Clinical Medicine; Data Mining; Decision Support Systems, Clinical; Decision Trees; Evidence-Based Medicine; Fuzzy Logic; Humans; Neural Networks, Computer; Pattern Recognition, Automated
PubMed: 26960378
DOI: 10.4103/0366-6999.178019 -
Current Medical Science Dec 2021The application of artificial intelligence (AI) technology in the medical field has experienced a long history of development. In turn, some long-standing points and...
The application of artificial intelligence (AI) technology in the medical field has experienced a long history of development. In turn, some long-standing points and challenges in the medical field have also prompted diverse research teams to continue to explore AI in depth. With the development of advanced technologies such as the Internet of Things (IoT), cloud computing, big data, and 5G mobile networks, AI technology has been more widely adopted in the medical field. In addition, the in-depth integration of AI and IoT technology enables the gradual improvement of medical diagnosis and treatment capabilities so as to provide services to the public in a more effective way. In this work, we examine the technical basis of IoT, cloud computing, big data analysis and machine learning involved in clinical medicine, combined with concepts of specific algorithms such as activity recognition, behavior recognition, anomaly detection, assistant decision-making system, to describe the scenario-based applications of remote diagnosis and treatment collaboration, neonatal intensive care unit, cardiology intensive care unit, emergency first aid, venous thromboembolism, monitoring nursing, image-assisted diagnosis, etc. We also systematically summarize the application of AI and IoT in clinical medicine, analyze the main challenges thereof, and comment on the trends and future developments in this field.
Topics: Algorithms; Artificial Intelligence; Big Data; Clinical Medicine; Cloud Computing; Humans; Internet of Things; Machine Learning
PubMed: 34939144
DOI: 10.1007/s11596-021-2486-z -
Journal of Clinical Epidemiology Oct 2021Clinical epidemiology, the "basic science for clinical medicine"[1], has changed substantially over the last 50 years, moving its focus from clinician driven research...
Clinical epidemiology, the "basic science for clinical medicine"[1], has changed substantially over the last 50 years, moving its focus from clinician driven research and clinical settings to large cohorts and trials, NIH funding, and practice guidelines. The COVID-19 pandemic created major challenges for clinicians who needed to make urgent decisions about the management a new disease and for researchers who needed to understand the clinical syndrome and the questions of greatest importance to the pandemic response. Addressing these challenges reunited clinicians and researchers in collaborative efforts to inform decisions about disease risk, prevention, prognosis and treatment, at least in part because of the shared sense of the need to ration scarce resources, the rapid evolution of understanding of the clinical syndrome, the recognition of widespread uncertainty, and the emphasis on the common good over individual credit. Only time will tell whether the experience during COVID-19 will revive the original practice of clinical epidemiology as "the application by a physician who provides direct patient care, of epidemiologic and biometric methods to the study of diagnostic and therapeutic process in order to effect an improvement in health"[2].
Topics: COVID-19; Clinical Medicine; Epidemiology; Forecasting; Humans
PubMed: 34284101
DOI: 10.1016/j.jclinepi.2021.07.009 -
Anales de Pediatria Jul 2018
Topics: Clinical Medicine; Genome, Human; Humans; Molecular Diagnostic Techniques
PubMed: 29753559
DOI: 10.1016/j.anpedi.2018.04.009 -
Sensors (Basel, Switzerland) Sep 2018This paper reviews the theories and applications of electromagnetic⁻acoustic (EMA) techniques (covering light-induced photoacoustic, microwave-induced thermoacoustic,... (Review)
Review
This paper reviews the theories and applications of electromagnetic⁻acoustic (EMA) techniques (covering light-induced photoacoustic, microwave-induced thermoacoustic, magnetic-modulated thermoacoustic, and X-ray-induced thermoacoustic) belonging to the more general area of electromagnetic (EM) hybrid techniques. The theories cover excitation of high-power EM field (laser, microwave, magnetic field, and X-ray) and subsequent acoustic wave generation. The applications of EMA methods include structural imaging, blood flowmetry, thermometry, dosimetry for radiation therapy, hemoglobin oxygen saturation (SO₂) sensing, fingerprint imaging and sensing, glucose sensing, pH sensing, etc. Several other EM-related acoustic methods, including magnetoacoustic, magnetomotive ultrasound, and magnetomotive photoacoustic are also described. It is believed that EMA has great potential in both pre-clinical research and medical practice.
Topics: Acoustics; Biomedical Research; Clinical Medicine; Electromagnetic Phenomena; Humans; Lasers; Magnetics; Microwaves; Ultrasonography; X-Rays
PubMed: 30248969
DOI: 10.3390/s18103203 -
Clinical Medicine (London, England) Mar 2023Established as a medical specialty in 1987, palliative medicine approaches middle age facing existential questions of identity, purpose and vision. Time has weakened... (Review)
Review
Established as a medical specialty in 1987, palliative medicine approaches middle age facing existential questions of identity, purpose and vision. Time has weakened strong foundations laid by Dame Cicely Saunders in research, education and clinical excellence. Clinical knowledge gaps are wide, and widening. Palliative medicine research is underfunded and underrepresented in discourse. Despite huge advances in modern medicine, there is still clinical uncertainty about simple interventions, such as whether artificial hydration at the end of life is helpful or harmful. Where good quality data do exist, the pace of change is slow, if change is happening at all. Trial design often fails to assess the holistic impact of interventions, using primary endpoints that are inconsistent with outcomes most valued to the patient. Recent years have seen a rapid expansion in innovation and investment in digital technologies, embraced by many in palliative medicine. Experience shows that caution must be applied where the evidence base is sparse. While as a specialty we must remain forward looking and progressive in our mindset, it cannot be assumed that these new interventions alone will provide the solutions to the old problems that exist in palliative medicine.This review summarises the key points presented in the Palliative Medicine section of the RCP Clinical Medicine Conference, 2022.
Topics: Middle Aged; Humans; Palliative Medicine; Clinical Decision-Making; Uncertainty; Evidence-Based Medicine; Clinical Medicine; Palliative Care
PubMed: 36806204
DOI: 10.7861/clinmed.2022-0336 -
Nature Communications Sep 2021Ben Glocker (an expert in machine learning for medical imaging, Imperial College London), Mirco Musolesi (a data science and digital health expert, University College...
Ben Glocker (an expert in machine learning for medical imaging, Imperial College London), Mirco Musolesi (a data science and digital health expert, University College London), Jonathan Richens (an expert in diagnostic machine learning models, Babylon Health) and Caroline Uhler (a computational biology expert, MIT) talked to Nature Communications about their research interests in causality inference and how this can provide a robust framework for digital medicine studies and their implementation, across different fields of application.
Topics: Biomedical Research; Causality; Clinical Medicine; Computational Biology; Humans; Machine Learning
PubMed: 34526509
DOI: 10.1038/s41467-021-25743-9 -
Journal of the American Board of Family... 2008
Topics: Clinical Medicine; Family; Family Practice; Health Services Research; Humans; Patient-Centered Care; Primary Health Care; United States
PubMed: 18772289
DOI: 10.3122/jabfm.2008.05.080141