Artificial intelligence in the diagnosis and treatment of acute appendicitis: a narrative review

Hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths worldwide. This review explores the recent progress in the application of artificial intelligence (AI) in radiological diagnosis of HCC. The...

Machine learning (ML) and deep learning (DL) have been broadly used in our daily lives in different ways. Early detection of COVID-19 built on chest Computerized tomography CT empowers suitable management of patients and helps control the spread of the disease. We projected an artificial intelligence (AI) system for rapid COVID-19 detection using analysis of CTs of COVID-19 depending on the AI system. We developed and evaluated our system on a large dataset with more than 3000 CT volumes from COVID-19, viral community-acquired pneumonia (CAP) and non-pneumonia subjects—1601 positive cases, 1626 negative cases.KeywordsCovid-19CTDiagnosisMachine learningArtificial intelligenceCovid positive CT image

Although the categorization of ultrasound using the Breast Imaging Reporting and Data System (BI-RADS) has become widespread worldwide, the problem of inter-observer variability remains. To maintain uniformity in diagnostic accuracy, we have developed a novel artificial intelligence (AI) system in which AI can distinguish whether a static image obtained using a breast ultrasound represents BI-RADS3 or lower, or BI-RADS4a or higher, to determine the medical management that should be performed on a patient whose breast ultrasound shows abnormalities. To establish and validate the AI system, a training dataset consisting of 4,028 images containing 5,014 lesions and a test dataset consisting of 3,166 images containing 3,656 lesions were collected and annotated. We selected a setting that maximized the area under the curve (AUC) and minimized the difference in sensitivity and specificity by adjusting the internal parameters of the AI system, achieving an AUC, sensitivity, and specificity of 0.95, 90.0%, and 88.5%, respectively. Furthermore, based on 30 images extracted from the test data, the diagnostic accuracy of 20 clinicians and the AI system was compared, and the AI system was found to be significantly superior to the clinicians (McNemar test, p < 0.001). Then, we conducted a trial to introduce the system for use in clinical practice. Physicians reviewed the images and determined whether they were BI-RADS3 or lower, or BI-RADS4a or higher. Next, the classification was performed again for the same images concerning the AI diagnosis. At this time, the initial judgment was allowed to be overturned. We checked whether there was any difference in the diagnostic accuracy, sensitivity, and specificity before and after reviewing to the AI diagnosis. Reviews by 24 physicians were evaluated: 4 Japanese Breast Cancer Society breast specialists, 5 non-specialists and physicians with experience treating more than 40 cases of breast cancer, and 15 non-specialists and physicians with no experience treating more than 40 cases of breast cancer. The average rate of accuracy before confirming the AI diagnosis increased to 73.1% after confirming the AI diagnosis (p=0.00548), compared to 69.3% on average before the AI diagnosis. Compared to practice experience, the accuracy increased from an average of 77.1% to 79.6% for the 9 physicians who were breast specialists or who had treated 40 or more cases of breast cancer. For the 15 physicians with less than 40 breast cancer cases, the average rate of accuracy increased from 64.7% to 69.2%. Furthermore, sensitivity increased significantly to an average of 99.7% after reviewing of the AI diagnosis from an average of 88.8% prior to reviewing the AI-diagnosis.(p< 0.01). Specificity increased from an average of 62.4% to 63.8% (p=0.433) after reviewing AI diagnosis. We showed that our AI system, when applied to clinical practice and used by physicians, contributes to the improvement of diagnostic accuracy. Our results indicated that our AI diagnostic system was sufficiently accurate to be used in the clinical practice. Citation Format: Erina Odani, Tetsu Hayashida, masayuki kikuchi, Aiko Nagayama, tomoko seki, maiko takahashi, Akiko Matsumoto, Takeshi Murata, Rurina Watanuki, Takamichi Yokoe, Ayako Nakashoji, Hinako Maeda, Tatsuya Onishi, Sota Asaga, Takashi Hojo, Hiromitsu Jinno, Keiichi Sotome, Akira Matsui, Akihiko Suto, Shigeru Imoto, Yuko Kitagawa. Establishment of the breast ultrasound support system using deep-learning system [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-05-06.

Machine learning based artificial intelligence technology for big data processing has shown great potential in predicting patients' conditions and aiding clinical decisions, and has been widely used in the development of clinical decision support systems in recent years. Sepsis is a life-threatening organ dysfunction caused by host response disorder caused by infection, and its early recognition and treatment can significantly improve the prognosis of patients. At present, there are many deficiencies in the clinical application of sequential organ failure assessment (SOFA), bedside quick sequential organ failure assessment (qSOFA), national early warning score (NEWS), inflammatory indicators, and novel biomarkers for evaluating sepsis. Artificial intelligence has promoted the development of critical care medicine because of its ability to rapidly process and analyze massive data of severe patients. This paper reviews the recent application of artificial intelligence in the early diagnosis and prediction of sepsis, in order to emphasize the importance and limitations of artificial intelligence in the diagnosis and prediction of sepsis.

China is a country with high incidence of esophageal cancer. Advanced esophageal cancer not only brings serious threat to the health of patients, but also brings heavy economic burden to their families and society. Early diagnosis and treatment of esophageal cancer are always the hot spot in clinical research, and gastroscopy screening is the key point. The development of artificial intelligence is expected to provide new mean for early diagnosis and treatment of esophageal cancer in the aspects of endoscopy procedure and quality control.Through a brief overview of the concept and development of artificial intelligence in endoscopic diagnosis of superficial esophageal cancer, this study summarizes and reviews the research progress of artificial intelligence in the diagnosis of superficial esophageal carcinoma, and illustrates the importance of its application. This study also discusses the main problems and difficulties of artificial intelligence in the endoscopic diagnosis of esophageal carcinoma. It prospects the application of artificial intelligence in endoscopic esophageal diagnosis in the future.

Artificial intelligence (AI) is revolutionizing healthcare by enabling systems to perform tasks traditionally requiring human intelligence. In healthcare, AI encompasses various subfields, including machine learning, deep learning, natural language processing, and expert systems. In the specific domain of onychology, AI presents a promising avenue for diagnosing nail disorders, analyzing intricate patterns, and improving diagnostic accuracy. This review provides a comprehensive overview of the current applications of AI in onychology, focusing on its role in diagnosing onychomycosis, subungual melanoma, nail psoriasis, nail fold capillaroscopy, and nail involvement in systemic diseases. A literature review on AI in nail disorders was conducted via PubMed and Google Scholar, yielding relevant studies. AI algorithms, particularly deep convolutional neural networks (CNNs), have demonstrated high sensitivity and specificity in interpreting nail images, aiding differential diagnosis as well as enhancing the efficiency of diagnostic processes in a busy clinical setting. In studies evaluating onychomycosis, AI has shown the ability to distinguish between normal nails, fungal infections, and other differentials, including nail psoriasis, with a high accuracy. AI systems have proven effective in identifying subungual melanoma. For nail psoriasis, AI has been used to automate the scoring of disease severity, reducing the time and effort required. AI applications in nail fold capillaroscopy have aided the analysis of diagnosis and prognosis of connective tissue diseases. AI applications have also been extended to recognize nail manifestations of systemic diseases, by analyzing changes in nail morphology and coloration. AI also facilitates the management of nail disorders by offering tools for personalized treatment planning, remote care, treatment monitoring, and patient education. Despite these advancements, challenges such as data scarcity, image heterogeneity, interpretability issues, regulatory compliance, and poor workflow integration hinder the seamless adoption of AI in onychology practice. Ongoing research and collaboration between AI developers and nail experts is crucial to realize the full potential of AI in improving patient outcomes in onychology.

In recent years Artificial Intelligence (AI) has been used in the field of education settings but also in special education as well. The technologies that are implied can transform the process of learning and teaching and create inclusive environments for students who face a variety of difficulties in learning. The current review focuses on two dimensions: a) the use of AI in diagnosis and the use of AI in intervention for students with disabilities and special educational needs. We emphasize the advantages and disadvantages of using AI in diagnosis and intervention in special education. We also address ethical considerations for the use of AI in special education. Suggestions for the use of AI in diagnosis and intervention in Special Education are also developed to help teachers, professionals such as psychologists, speech therapists, occupational therapists, parents, students, and policymakers in developing an effective context in the use of AI in special education.

Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us?

Purpose: Acute appendicitis (AA) is a common surgical emergency affecting 7-8% of the population. Timely diagnosis and treatment are crucial for preventing serious morbidity and mortality. Diagnosis typically involves physical examination, laboratory tests, ultrasonography, and computed tomography (CT). This study aimed to evaluate the effectiveness of artificial intelligence (AI) in analyzing CT images for the early diagnosis of AA and prevention of complications. Methods: CT images of patients who underwent surgery for AA at the General Surgery Clinic of Kanuni Sultan Suleyman Health Application and Research Center between January 1, 2019, and June 31, 2023, were analyzed. A total of 1200 CT images were evaluated using four different AI models. The model performance was assessed using a confusion matrix. Results: The median age of the patients was 28 years, with a similar sex distribution. No significant differences were observed in terms of age or sex (P = .168 and P = .881, respectively). Among the AI models, MobileNet v2 showed the highest accuracy (0.7908) and precision (0.8203), whereas Inception v3 had the highest F-score (0.7928). In the receiver operating characteristic analysis, MobileNet v2 achieved an area under the curve (AUC) of 0.8767. Conclusion: AI's role in daily life is expanding. In the present study, the highest sensitivity and specificity were 77% and 86%, respectively. Supporting CT imaging with AI systems can enhance the accuracy of AA diagnoses.

Significance: Pressure injury is one of the most common health problems among hospitalized patients worldwide, and accurate and timely diagnosis is crucial for its treatment. Research on the application of artificial intelligence in the diagnosis of pressure injury is increasing, but there is currently no comprehensive meta-analysis to evaluate the accuracy of artificial intelligence in diagnosing different pressure injury stages. Recent Advances: This study synthesizes evidence on artificial intelligence diagnosis of pressure injury, focusing on evaluating diagnostic performance across different stages using core metrics including sensitivity, specificity, and the area under the summary receiver operating characteristic (SROC) curve. Critical Issues: Key findings from 21 included studies (12 contributing 47 eligible datasets) indicate high overall diagnostic accuracy of artificial intelligence for pressure injury, with sensitivity of 0.74 (95% confidence interval [CI]: 0.69-0.78), specificity of 0.93 (95% CI: 0.91-0.94), and area under the SROC curve of 0.92 (95% CI: 0.90-0.94). Moreover, the area under the SROC curve varies across different stages of pressure injury, with area under the curve values for stage 1, stage 2, stage 3, stage 4, unstageable, and deep tissue pressure injury of 0.95 (0.93-0.97), 0.85 (0.82-0.88), 0.88 (0.84-0.90), 0.94 (0.92-0.96), 0.96 (0.94-0.97), and 0.98 (0.96-0.99), respectively. Future Directions: Artificial intelligence models based on pressure injury image data show substantial potential for clinical application in pressure injury diagnosis. However, the need for high-quality studies with rigorous reporting and external validation remains critical to address current limitations and advance clinical translation.

cancer remains a leading cause of morbidity and mortality worldwide, with nearly 20 million new cases and 9.7 million deaths reported in 2022. The increasing burden of cancer, driven by population growth and aging, necessitates innovative solutions to improve diagnosis, prognosis, and treatment outcomes. Artificial Intelligence has emerged as a transformative tool in oncology, offering significant potential in cancer detection, diagnosis, and personalized treatment strategies. This review explores the real-world applications of Artificial Intelligence in oncology, focusing on lung cancer and breast cancer, two of the most prevalent and deadly cancers globally. Artificial Intelligence-driven technologies, particularly in imaging, pathology, and genomics, have demonstrated remarkable success in enhancing early detection, diagnostic accuracy, and treatment planning. In lung cancer, Artificial Intelligence-powered imaging tools, such as deep learning models, have shown high sensitivity and specificity in detecting small pulmonary nodules, often missed by traditional methods. Similarly, in breast cancer, Artificial Intelligence has proven effective in mammography interpretation, reducing false positives and false negatives, and alleviating the workload of radiologists. Despite its promising potential, the integration of Artificial Intelligence into clinical practice faces several challenges, including issues related to data quality, algorithmic biases, and ethical considerations. The "black box" nature of many Artificial Intelligence systems poses a significant barrier to clinical acceptance, highlighting the need for explainable Artificial Intelligence to provide transparent and interpretable decision-making processes. Furthermore, the successful implementation of Artificial Intelligence in oncology requires robust regulatory frameworks and standardized protocols to ensure patient safety and data security. This review underscores the transformative potential of Artificial Intelligence in revolutionizing cancer care, emphasizing the importance of addressing key challenges to harness its full potential. By enhancing early detection, reducing diagnostic errors, and enabling personalized treatment strategies, Artificial Intelligence has the potential to significantly improve patient outcomes and reduce the global burden of cancer. However, its successful integration into clinical practice will depend on interdisciplinary collaboration, ethical considerations, and a commitment to responsible implementation.

As the most common cancer after lung cancer presently, breast cancer needs to gain extensive awareness of both general public and healthcare professionals. With continuous developments of technology researchers on medical science began to attach great significance to the integration of artificial intelligence (AI) and clinical diagnostics. With the purpose of enhancing early diagnosis and prognosis and implementing more effective and timely treatment for breast cancer, increased research efforts are currently aimed at applying AI to breast cancer detection and classification, opening up new avenues for improving patient care. The paper initially discusses the progress of AI in the diagnosis and prognosis of breast cancer and presents an overview of the existing literature of research in this area. It describes the main methods and procedures employed in AI-assisted diagnosis and response prediction with particular emphasis on radiological as well as pathological approaches. Comparative analysis is also provided comparing differences in sensitivity and specificity between clinicians and AI systems when interpreting images. Furthermore, the paper discusses existing challenges in the introduction of AI in clinical practice to assist clinicians’ diagnosis and carry out treatment. The major challenges include requirements for data volumes, standardization and ethical problems. Then, it predicted the future progress of AI technologies in diagnostics and prognosis of breast cancer, providing practical suggestions on promoting applications of AI in diagnosis, therapy and general use.

The paper presents a brief overview of the tasks and methods of artificial intelligence, as well as a review of works devoted to its use in the field of diagnostics of oncological diseases, in particular, breast cancer. To reduce mortality and complications, it is necessary to conduct timely screening and improve methods of diagnosing the disease. It is especially important to diagnose and start treatment in the early stages. The goal is to study and summarize data on the use of various methods of artificial intelligence in the timely diagnosis of breast cancer. The analysis of scientific publications on this topic was carried out. The methods of Watson supercomputer, Microsoft Healthcare NExT, radiomics processes, automatic detection systems, Smart Detect for Breast are considered. The prospect of using artificial intelligence, as a screening method, it can allow for better detection of formations at an early stage, as well as lead to automation of this process, which entails a decrease in mortality from breast cancer. Comparing the performance of the artificial intelligence system in breast cancer screening with that of 101 individual radiologists, the researchers found that the former performed better than 61% of the radiologists. Currently, variations of artificial intelligence are presented. It is necessary to specify the methods and create a single program for use in the practice of a doctor.

Is the future of medical diagnosis in computer algorithms?

Artificial intelligence (AI) has significantly impacted dentistry by enhancing diagnostic accuracy, treatment planning, and patient care across various specialties, including endodontics, radiology, and periodontology. This review synthesizes findings from five key studies examining AI applications in dentistry, focusing on convolutional neural networks (CNNs) and deep learning models.AI-assisted diagnostics have shown superior accuracy compared to traditional methods, with CNNs achieving up to 94% accuracy in detecting periapical lesions and surpassing human radiologists in specific diagnostic tasks. Additionally, AI-assisted caries detection improves tooth retention and reduces treatment costs, demonstrating its potential economic benefits. However, challenges such as data biases, ethical considerations, and regulatory barriers remain future research should focus on developing transparent AI models, standardizing datasets, and addressing cost-effectiveness concerns to enhance clinical integration. Methods: A comprehensive review of five peer-reviewed articles was conducted, highlighting AI applications in dentistry. The articles were selected based on relevance to diagnostic advancements, clinical decision-making, and patient outcomes. Key methodologies included CNN-based image analysis, deep learning applications for caries detection, and neural networks for treatment optimization. Results: AI applications in dentistry demonstrated superior diagnostic performance. CNNs achieved 94% accuracy in detecting periapical lesions and surpassed human radiologists in specific diagnostic tasks. AI-assisted caries detection improved tooth retention by 62.8 years on average, with cost savings of €378 per patient compared to traditional methods. In endodontics, AI accurately identified root fractures and predicted treatment outcomes with up to 95.6% accuracy. Despite these advancements, limitations such as data biases and interpretability of AI models were noted. Conclusion: AI holds transformative potential for modern dentistry by improving diagnostic precision and clinical efficiency. However, integrating AI into routine practice requires addressing data standardization, ethical frameworks, and regulatory barriers. Future research should focus on developing transparent AI models and exploring their cost-effectiveness and long-term impact on patient care.