Clinical Applications Of Artificial Intelligence Research Articles

Clinical Applications of Artificial Intelligence in Medical Imaging and Image Processing—A Review

Clinical Applications of Artificial Intelligence in Medical Imaging and Image Processing—A Review

Exploring the potential of machine learning in gynecological care: a review.

Gynecological health remains a critical aspect of women's overall well-being, with profound implications for maternal and reproductive outcomes. This comprehensive review synthesizes the current state of knowledge on four pivotal aspects of gynecological health: preterm birth, breast cancer and cervical cancer and infertility treatment. Machine learning (ML) has emerged as a transformative technology with the potential to revolutionize gynecology and women's healthcare. The subsets of AI, namely, machine learning (ML) and deep learning (DL) methods, have aided in detecting complex patterns from huge datasets and using such patterns in making predictions. This paper investigates how machine learning (ML) algorithms are employed in the field of gynecology to tackle crucial issues pertaining to women's health. This paper also investigates the integration of ultrasound technology with artificial intelligence (AI) during the initial, intermediate, and final stages of pregnancy. Additionally, it delves into the diverse applications of AI throughout each trimester.This review paper provides an overview of machine learning (ML) models, introduces natural language processing (NLP) concepts, including ChatGPT, and discusses the clinical applications of artificial intelligence (AI) in gynecology. Additionally, the paper outlines the challenges in utilizing machine learning within the field of gynecology.

Promises, Pitfalls, and Clinical Applications of Artificial Intelligence in Pediatrics.

Artificial intelligence (AI) broadly describes a branch of computer science focused on developing machines capable of performing tasks typically associated with human intelligence. Those who connect AI with the world of science fiction may meet its growing rise with hesitancy or outright skepticism. However, AI is becoming increasingly pervasive in our society, from algorithms helping to sift through airline fares to substituting words in emails and SMS text messages based on user choices. Data collection is ongoing and is being leveraged by software platforms to analyze patterns and make predictions across multiple industries. Health care is gradually becoming part of this technological transformation, as advancements in computational power and storage converge with the rapid expansion of digitized medical information. Given the growing and inevitable integration of AI into health care systems, it is our viewpoint that pediatricians urgently require training and orientation to the uses, promises, and pitfalls of AI in medicine. AI is unlikely to solve the full array of complex challenges confronting pediatricians today; however, if used responsibly, it holds great potential to improve many aspects of care for providers, children, and families. Our aim in this viewpoint is to provide clinicians with a targeted introduction to the field of AI in pediatrics, including key promises, pitfalls, and clinical applications, so they can play a more active role in shaping the future impact of AI in medicine.

Enabling the clinical application of artificial intelligence in genomics: a perspective of the AMIA Genomics and Translational Bioinformatics Workgroup.

Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association-Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space. A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members. Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of larger datasets is crucial to emulating the success AI is achieving in other domains. It is important that AI methods do not exacerbate existing socio-economic, racial, and ethnic disparities. Genomic data standards are critical to effectively scale such technologies across institutions. With so much uncertainty, complexity and novelty in genomics and medicine, and with an evolving regulatory environment, the current focus should be on using these technologies in an interface with clinicians that emphasizes the value each brings to clinical decision-making.

Announcement: Call for Papers: Focus Issue on Rapidly Evolving Clinical Applications of Artificial Intelligence

Announcement: Call for Papers: Focus Issue on Rapidly Evolving Clinical Applications of Artificial Intelligence

Physician views of artificial intelligence in otolaryngology and rhinology: A mixed methods study

AbstractObjectiveThe study aimed to investigate otolaryngologists' knowledge, trust, acceptance, and concerns with clinical applications of artificial intelligence (AI).MethodsThis study used mixed methods with survey and semistructured interviews. Survey was e‐mailed to American Rhinologic Society members, of which a volunteer sample of 86 members responded. Nineteen otolaryngologists were purposefully recruited and interviewed until thematic saturation was achieved.ResultsSeventy‐six respondents (10% response rate) completed the majority of the survey: 49% worked in academic settings and 43% completed residency 10 or fewer years ago. Of 19 interviewees, 58% worked in academic settings, and 47% completed residency 10 or fewer years ago. Familiarity: Only 8% of survey respondents reported having AI training in residency, although 72% had familiarity with general AI concepts; 0 interviewees had personal experience with AI in clinical settings. Expected uses: Of the surveyed otolaryngologists, 82% would use an AI‐based clinical decision aid and 74% were comfortable with AI proposing treatment recommendations. However, only 44% of participants would trust AI to identify malignancy and 53% to interpret radiographic images. Interviewees trusted AI for simple tasks, such as labeling septal deviation, more than complex ones, such as identifying tumors. Factors influencing AI adoption: 89% of survey participants would use AI if it improved patient satisfaction, 78% would be willing to use AI if experts and studies validated the technologies, and 73% would only use AI if it increased efficiency. Sixty‐one percent of survey respondents expected AI incorporation into clinical practice within 5 years. Interviewees emphasized that AI adoption depends on its similarity to their clinical judgment and to expert opinion. Concerns included nuanced or complex cases, poor design or accuracy, and the personal nature of physician‐patient relationships.ConclusionFew physicians have experience with AI technologies but expect rapid adoption in the clinic, highlighting the urgent need for clinical education and research. Otolaryngologists are most receptive to AI “augmenting” physician expertise and administrative capacity, with respect for physician autonomy and maintaining relationships with patients.Level of EvidenceLevel VI, descriptive or qualitative study.

Review of artificial intelligence clinical applications in Nuclear Medicine.

This paper provides an in-depth analysis of the clinical applications of artificial intelligence (AI) in Nuclear Medicine, focusing on three key areas: neurology, cardiology, and oncology. Beginning with neurology, specifically Alzheimer's disease and Parkinson's disease, the paper examines reviews on diagnosis and treatment planning. The same pattern is followed in cardiology studies. In the final section on oncology, the paper explores the various AI applications in multiple cancer types, including lung, head and neck, lymphoma, and pancreatic cancer.

Retracted: Clinical Application of Artificial Intelligence: Auto-Discerning the Effectiveness of Lidocaine Concentration Levels in Osteosarcoma Femoral Tumor Segment Resection.

[This retracts the article DOI: 10.1155/2022/7069348.].

Utility of deep learning for the diagnosis of cochlear malformation on temporal bone CT.

Diagnosis of cochlear malformation on temporal bone CT images is often difficult. Our aim was to assess the utility of deep learning analysis in diagnosing cochlear malformation on temporal bone CT images. A total of 654 images from 165 temporal bone CTs were divided into the training set (n = 534) and the testing set (n = 120). A target region that includes the area of the cochlear was extracted to create a diagnostic model. 4 models were used: ResNet10, ResNet50, SE-ResNet50, and DenseNet121. The testing data set was subsequently analyzed using these models and by 4 doctors. The areas under the curve was 0.91, 0.94, 0.93, and 0.73 in ResNet10, ResNet50, SE-ResNet50, and DenseNet121. The accuracy of ResNet10, ResNet50, and SE-ResNet50 is better than chief physician. Deep learning technique implied a promising prospect for clinical application of artificial intelligence in the diagnosis of cochlear malformation based on CT images.

Announcement: Call for Papers: Focus Issue on Rapidly Evolving Clinical Applications of Artificial Intelligence

Announcement: Call for Papers: Focus Issue on Rapidly Evolving Clinical Applications of Artificial Intelligence

Clinical applications of artificial intelligence in Radiology: prostate magnetic resonance imaging

Clinical applications of artificial intelligence in Radiology: prostate magnetic resonance imaging

A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging.

Artificial intelligence (AI) has been a topic of substantial interest for radiologists in recent years. Although many of the first clinical applications were in the neuro, cardiothoracic, and breast imaging subspecialties, the number of investigated and real-world applications of body imaging has been increasing, with more than 30 FDA-approved algorithms now available for applications in the abdomen and pelvis. In this manuscript, we explore some of the fundamentals of artificial intelligence and machine learning, review major functions that AI algorithms may perform, introduce current and potential future applications of AI in abdominal imaging, provide a basic understanding of the pathways by which AI algorithms can receive FDA approval, and explore some of the challenges with the implementation of AI in clinical practice.

Announcement: Call for Papers: Focus Issue on Rapidly Evolving Clinical Applications of Artificial Intelligence

Announcement: Call for Papers: Focus Issue on Rapidly Evolving Clinical Applications of Artificial Intelligence

Announcement: Call for Papers: Focus Issue on Rapidly Evolving Clinical Applications of Artificial Intelligence

Announcement: Call for Papers: Focus Issue on Rapidly Evolving Clinical Applications of Artificial Intelligence

[[PET] 6. Overview of Clinical Applications of Artificial Intelligence in PET].

[[PET] 6. Overview of Clinical Applications of Artificial Intelligence in PET].

Development of an artificial intelligence algorithm for adjuvant chemotherapy based on a nationwide registry of patients with gastric cancer by the Japanese Gastric Cancer Association (JGCA).

4063 Background: Recent marked advances in machine learning have led to expectations of the clinical application of artificial intelligence (AI) to support medical care. Methods: A survival analysis model which consisted of 31 covariates was adopted for AI algorithms using machine learning and these were constructed using clinical data sets for training. The performance of AI algorithms was evaluated in order to determine the optimal chemotherapy including surgery alone without any adjuvant chemotherapy with the highest survival rate for each patient. This involved using clinical data for verification to compare survival of an AI-recommended treatment group, for which therapy recommended by AI was actually administered, with an AI-deprecated group, for which therapy recommended by AI was not administered. Results: The clinical characteristics of 23653 patients and treatment are described in the Table from 2011 to 2018 in a nationwide registry of gastric cancer patients in Japan by the Japanese Gastric Cancer Association and were made available for this study. S-1 monotherapy was used the most frequently of all adjuvant chemotherapy in this study. We used the "restricted mean overall survival time" (RMST) of all the patients as metrics. The RMST in the AI-recommended and the AI-deprecated groups were 51.4 and 47.5 months, respectively. Patient data for the verification were matched baseline characteristics by the propensity score. This model predicted effectively overall survival after gastrectomy. The RMST of over 80 years old patients were 43.9 in the AI-recommended and 38.3 months in the AI-deprecated, respectively. This AI algorithm recommended adjuvant S-1 more frequently for patients with higher age, male, American Society of Anesthesiologists – physical status 2, Eastern Cooperative Oncology Group performance status 1, pT3/pT4, pN2/pN3, more than the upper normal limit of preoperative blood urea nitrogen, macroscopic types 2/3, differentiated adenocarcinoma, Roux-en-Y reconstruction, and Clavien-Dindo classification grade II/III. The RMST for pStage IIA/IIB/IIIA/IIIB/IIIC were 55.7/53.9/50.3/45.7/42.2 months in the AI-recommended and 55.1/50.7/43.9/36.9/32.0 months in the deprecated. This AI algorithm showed a higher survival rate in pStage III patients particularly. Conclusions: The AI algorithm could readily be integrated into clinical practice to choose adequate adjuvant chemotherapy for each patient based on each patient's baseline data because it trained and verified the nationwide registry has good predictive performance. [Table: see text]

[Legal regulation of clinical application of artificial intelligence].

With the wide application of artificial intelligence (AI) technology in clinical practice, more and more legal problems need to be solved. At present, although the legal status of AI is still controversial in academic and practical circles, its infringement risk in clinical diagnosis and surgery cannot be avoided. On the basis of the distinction between strong and weak AI liability subjects, those who meet the requirements of infringement, damage consequence, causal relationship, subjective fault, etc., can constitute tort liability, but the existence of exemption causes can also exempt liability. In addition to the ex post accountability of tort liability, it is also necessary to establish a complete administrative legal regulation system. At present, China needs to explore and establish the classification registration system, compulsory insurance system and reserve system of AI as soon as possible, so as to strengthen the legal regulation of the whole process of AI clinical application before, during and after the event.

Clinical applications of artificial intelligence in liver imaging.

This review outlines the current status and challenges of the clinical applications of artificial intelligence in liver imaging using computed tomography or magnetic resonance imaging based on a topic analysis of PubMed search results using latent Dirichlet allocation. LDA revealed that "segmentation," "hepatocellular carcinoma and radiomics," "metastasis," "fibrosis," and "reconstruction" were current main topic keywords. Automatic liver segmentation technology using deep learning is beginning to assume new clinical significance as part of whole-body composition analysis. It has also been applied to the screening of large populations and the acquisition of training data for machine learning models and has resulted in the development of imaging biomarkers that have a significant impact on important clinical issues, such as the estimation of liver fibrosis, recurrence, and prognosis of malignant tumors. Deep learning reconstruction is expanding as a new technological clinical application of artificial intelligence and has shown results in reducing contrast and radiation doses. However, there is much missing evidence, such as external validation of machine learning models and the evaluation of the diagnostic performance of specific diseases using deep learning reconstruction, suggesting that the clinical application of these technologies is still in development.

Clinical applications of artificial intelligence in radiology.

The rapid growth of medical imaging has placed increasing demands on radiologists. In this scenario, artificial intelligence (AI) has become an attractive partner, one that may complement case interpretation and may aid in various non-interpretive aspects of the work in the radiological clinic. In this review, we discuss interpretative and non-interpretative uses of AI in the clinical practice, as well as report on the barriers to AI's adoption in the clinic. We show that AI currently has a modest to moderate penetration in the clinical practice, with many radiologists still being unconvinced of its value and the return on its investment. Moreover, we discuss the radiologists' liabilities regarding the AI decisions, and explain how we currently do not have regulation to guide the implementation of explainable AI or of self-learning algorithms.

The Clinical Application of Artificial Intelligence Assisted Contrast-Enhanced Ultrasound on BI-RADS Category 4 Breast Lesions

To propose a novel deep learning method incorporating multiple regions based on contrast-enhanced ultrasound and grayscale ultrasound, evaluate its performance in reducing false positives for Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions, and compare its diagnostic performance with that of ultrasound experts. This study enrolled 163 breast lesions in 161 women from November 2018 to March 2021. Contrast-enhanced ultrasound and conventional ultrasound were performed before surgery or biopsy. A novel deep learning model incorporating multiple regions based on contrast-enhanced ultrasound and grayscale ultrasound was proposed for minimizing the number of false-positive biopsies. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were compared between the deep learning model and ultrasound experts. The AUC, sensitivity, specificity, and accuracy of the deep learning model in BI-RADS category 4 lesions were 0.910, 91.5%, 90.5%, and 90.8%, respectively, compared with those of ultrasound experts were 0.869, 89.4%, 84.5%, and 85.9%, respectively. The novel deep learning model we proposed had a diagnostic accuracy comparable to that of ultrasound experts, showing the potential to be clinically useful in minimizing the number of false-positive biopsies.