Studies in big data, Journal Year: 2023, Volume and Issue: unknown, P. 323 - 345
Published: Jan. 1, 2023
Language: Английский
Current Research in Biotechnology, Journal Year: 2023, Volume and Issue: 7, P. 100164 - 100164
Published: Nov. 22, 2023
The medicine and healthcare sector has been evolving advancing very fast. advancement initiated shaped by the applications of data-driven, robust, efficient machine learning (ML) to deep (DL) technologies. ML in medical is developing quickly, causing rapid progress, reshaping medicine, improving clinician patient experiences. technologies evolved into data-hungry DL approaches, which are more robust dealing with data. This article reviews some critical data-driven aspects intelligence field. In this direction, illustrated recent progress science using two categories: firstly, development data uses and, secondly, Chabot particularly on ChatGPT. Here, we discuss ML, DL, transition requirements from DL. To science, illustrate prospective studies image data, newly interpretation EMR or EHR, big personalized dataset shifts artificial (AI). Simultaneously, recently developed DL-enabled ChatGPT technology. Finally, summarize broad role significant challenges for implementing healthcare. overview paradigm shift will benefit researchers immensely.
Language: Английский
Citations
65
Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(9)
Published: Aug. 8, 2024
Abstract The fusion of blockchain and artificial intelligence (AI) marks a paradigm shift in healthcare, addressing critical challenges securing electronic health records (EHRs), ensuring data privacy, facilitating secure transmission. This study provides comprehensive analysis the adoption AI within spotlighting their role fortifying security transparency leading trajectory for promising future realm healthcare. Our study, employing PRISMA model, scrutinized 402 relevant articles, narrative to explore review includes architecture blockchain, examines applications with without integration, elucidates interdependency between blockchain. major findings include: (i) it protects transfer, digital records, security; (ii) enhances EHR COVID-19 transmission, thereby bolstering healthcare efficiency reliability through precise assessment metrics; (iii) addresses like security, decentralized computing, forming robust tripod. revolutionize by EHRs, enhancing security. Private reflects sector’s commitment improved accessibility. convergence promises enhanced disease identification, response, overall efficacy, key sector challenges. Further exploration advanced features integrated enhance outcomes, shaping global delivery guaranteed innovation.
Language: Английский
Citations
7
Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(12)
Published: Oct. 21, 2024
Abstract Artificial intelligence (AI) and other disruptive technologies can potentially improve healthcare across various disciplines. Its subclasses, artificial neural networks, deep learning, machine excel in extracting insights from large datasets improving predictive models to boost their utility accuracy. Though research this area is still its early phases, it holds enormous potential for the diagnosis, prognosis, treatment of urological diseases, such as bladder cancer. The long-used nomograms classic forecasting approaches are being reconsidered considering AI’s capabilities. This review emphasizes coming integration into settings while critically examining most recent significant literature on subject. study seeks define status AI future, with a special emphasis how transform cancer diagnosis treatment.
Language: Английский
Citations
3
Frontiers in Public Health, Journal Year: 2024, Volume and Issue: 12
Published: April 10, 2024
Artificial intelligence has led to significant developments in the healthcare sector, as other sectors and fields. In light of its significance, present study delves into exploring deep learning, a branch artificial intelligence.
Language: Английский
Citations
1
Published: March 14, 2024
A branch of AI known as "deep learning" sifts through mountains data in search patterns and predictions using a machine learning approach called an artificial neural network. Rapid progress widespread, successful application across many fields have been its hallmarks recent years. significant obstacle to healthcare transformation is the useful information from complicated, high-dimensional, diverse biological data. This research aims give concise synopsis developments deep learning, survey studies conducted on COVID-19, some realworld applications deep-learning(DL) strategies' for COVID19 diagnosis, prognosis, therapy management. DL has potential enhance quality speed drug development by analyzing medicinal imaging data, lab test results, other pertinent make diagnoses, assess course prognosis illnesses, even provide healing recommendations drug-utilize regimens. In addition, it may guide policymakers creating effective control preventative strategies. Additionally, we evaluate present state learning's capabilities shortcomings terms COVID-19 treatment accuracy, covering issues such dearth phenotypically plentiful necessity deeper models that are easier understand work with. We wrap up talking about how get beyond obstacles standing way future clinical uses.
Language: Английский
Citations
1
Digital Health, Journal Year: 2024, Volume and Issue: 10
Published: Jan. 1, 2024
Objective The enabling and derailing factors for using artificial intelligence (AI)-based applications to improve patient care in the United Arab Emirates (UAE) from physicians’ perspective are investigated. Factors accelerate adoption of AI-based UAE identified aid implementation. Methods A qualitative, inductive research methodology was employed, utilizing semi-structured interviews with 12 physicians practicing UAE. collected data were analyzed NVIVO software grounded theory used thematic analysis. Results This study specific deployment AI transform First, must control be fully trained engaged testing phase. Second, healthcare systems need connected, outcomes easily interpretable by physicians. Third, reimbursement should settled insurance or government. Fourth, patients aware accept technology before use it avoid negative consequences doctor–patient relationship. Conclusions conducted determine their understanding improving through applications. importance involving as accountable agents tools is highlighted. Public awareness regarding improved drive public acceptance.
Language: Английский
Citations
1
Artificial intelligence, Journal Year: 2024, Volume and Issue: unknown
Published: Dec. 19, 2024
Breast cancer is a significant transnational health concern, requiring effective timely detection methods to improve patient’s treatment result and reduce mortality rates. While conventional screening like mammography, ultrasound, MRI have proven efficacy, they possess limitations, such as false-positive results discomfort. In recent years, machine learning (ML) deep (DL) techniques demonstrated potential in transforming breast through the analysis of imaging data. This review systematically explores advancements research applications for detecting cancer. Through systematic existing literature, we identify trends, challenges, opportunities development deployment ML DL models diagnosis. We highlight crucial role early enhancing patient outcomes lowering Furthermore, impact technologies on clinical procedure, outcomes, healthcare delivery detection. By identifying evaluating studies detection, aim provide valuable insights researchers, clinicians, policymakers, stakeholders interested leveraging advanced computational enhance
Language: Английский
Citations
0
Published: Oct. 18, 2023
Chronic Obstructive Pulmonary Disease (COPD) is a prevalent respiratory condition that requires accurate assessment for effective management. The paper proposes novel approach leverages the combined power of CNNs and LSTM networks COPD through multimodal analysis. objective study to enhance accuracy reliability diagnosis by exploiting synergy between using comprehensive dataset comprising lung function measurements, clinical history, imaging data. Existing systems often rely on single-modal analysis, limiting effectiveness diagnosis. In contrast, our proposed integrates multiple modalities, including data, capture more representation disease. Experimental evaluation showcases superior performance model, achieving an above 95 % outperforming existing in terms precision, recall, Fl-score. fusion enables model extract relevant features temporal dependencies, enhancing overall performance. These findings highlight potential analysis reliable early detection COPD. research contributes improving management treatment outcomes debilitating condition.
Language: Английский
Citations
1
Studies in big data, Journal Year: 2023, Volume and Issue: unknown, P. 323 - 345
Published: Jan. 1, 2023
Language: Английский
Citations
0