Artificial Intelligence Assistance Research Articles

A growing number of AIs cleared for clinical use is finally available: The AI-assisted Pathologist

As more pathology laboratories are transitioning to a digital workflow, the availability of commercial Artificial Intelligence assistance systems is also increasing. Today nearly 40 such products approved for diagnostic use are available. This article provides an overview of the most widely addressed use cases, including Immunohistochemistry scoring for breast cancer and non-small-cell lung cancer, Gleason grading for prostate cancer, or metastasis detection in lymph nodes. While automation alone already promises an increase in efficiency that may help to bridge the growing gap between supply (pathology work force) and demand (histological testing), this article introduces another category of Artificial Intelligence products that go beyond just mimicking today’s established score. Various Artificial Intelligence are being introduced that detect genetic alterations or stratify risk, directly from the standard hematoxylin & eosin staining. Finally, a brief outlook explains how basic AI models are currently finding their way into computational pathology and promise to further accelerate product developments by decreasing the time-to-model.

Artificial Intelligence in Medicine: A New Frontier

Artificial intelligence (AI) refers to the engineering and science of making intelligent machines through algorithms or rules, mimicking human cognitive functions, such as learning and problem-solving. AI has several branches, such as machine learning and deep learning, which can add intelligence to applications. Machine learning is the study of algorithms that allow computer programs to improve automatically through experience. Deep learning algorithms learn from an extensive, multi-layered collection of interconnected processes and expose these processors to many examples. In the coming years, the integration of AI in routine medical care is expected to revolutionize Medicine, potentially improving patient care and quality of life. The time required for a diagnosis can be greatly reduced, and diagnostic efficiency can be significantly enhanced when AI assists clinicians. Large language model chatbots are capable of clinical expert-level medical note-taking, consultation, and questionanswering. Chatbotscan generate human-like text, may help diagnose diseases based on medical records, and may suggest treatment options or plans. Artificial intelligence algorithms, particularly deep learning, have demonstrated remarkable progress in radiological image analysis and diagnosis and may improve radiologists’ efficiency. These algorithms may also improve diagnostic accuracy in dermatology, histopathology, fundoscopy, endoscopy, and other medical images. Natural language processing and ambient clinical intelligence automate administrative duties like recording patient visits in electronic health records, streamlining clinical workflow, and freeing up doctors to spend more time with patients. AI may also help with new drug discoveries, precision medicine, and clinical research. AI developments can revolutionize several healthcare-related fields and pave the way for a more individualized, accurate, predictive, and portable future. Bangladesh J Medicine 2024; 35: 54-60

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus.

Diabetes Mellitus is one of the oldest diseases known to humankind, dating back to ancient Egypt. The disease is a chronic metabolic disorder that heavily burdens healthcare providers worldwide due to the steady increment of patients yearly. Worryingly, diabetes affects not only the aging population but also children. It is prevalent to control this problem, as diabetes can lead to many health complications. As evolution happens, humankind starts integrating computer technology with the healthcare system. The utilization of artificial intelligence assists healthcare to be more efficient in diagnosing diabetes patients, better healthcare delivery, and more patient eccentric. Among the advanced data mining techniques in artificial intelligence, stacking is among the most prominent methods applied in the diabetes domain. Hence, this study opts to investigate the potential of stacking ensembles. The aim of this study is to reduce the high complexity inherent in stacking, as this problem contributes to longer training time and reduces the outliers in the diabetes data to improve the classification performance. In addressing this concern, a novel machine learning method called the Stacking Recursive Feature Elimination-Isolation Forest was introduced for diabetes prediction. The application of stacking with Recursive Feature Elimination is to design an efficient model for diabetes diagnosis while using fewer features as resources. This method also incorporates the utilization of Isolation Forest as an outlier removal method. The study uses accuracy, precision, recall, F1 measure, training time, and standard deviation metrics to identify the classification performances. The proposed method acquired an accuracy of 79.077% for PIMA Indians Diabetes and 97.446% for the Diabetes Prediction dataset, outperforming many existing methods and demonstrating effectiveness in the diabetes domain.

Artificial intelligence improves resident detection of pediatric and young adult upper extremity fractures.

We wished to evaluate if an open-source artificial intelligence (AI) algorithm ( https://www.childfx.com ) could improve performance of (1) subspecialized musculoskeletal radiologists, (2) radiology residents, and (3) pediatric residents in detecting pediatric and young adult upper extremity fractures. A set of evaluation radiographs drawn from throughout the upper extremity (elbow, hand/finger, humerus/shoulder/clavicle, wrist/forearm, and clavicle) from 240 unique patients at a single hospital was constructed (mean age 11.3years, range 0-22years, 37.9% female). Two fellowship-trained musculoskeletal radiologists, three radiology residents, and two pediatric residents were recruited as readers. Each reader interpreted each case initially without and then subsequently 3-4weeks later with AI assistance and recorded if/where fracture was present. Access to AI significantly improved area under the receiver operator curve (AUC) of radiology residents (0.768 [0.730-0.806] without AI to 0.876 [0.845-0.908] with AI, P < 0.001) and pediatric residents (0.706 [0.659-0.753] without AI to 0.844 [0.805-0.883] with AI, P < 0.001) in identifying fracture, respectively. There was no evidence of improvement for subspecialized musculoskeletal radiology attendings in identifying fracture (AUC 0.867 [0.832-0.902] to 0.890 [0.856-0.924], P = 0.093). There was no evidence of difference between overall resident AUC with AI and subspecialist AUC without AI (resident with AI 0.863, attending without AI AUC 0.867, P = 0.856). Overall physician radiograph interpretation time was significantly lower with AI (38.9s with AI vs. 52.1s without AI, P = 0.030). An openly accessible AI model significantly improved radiology and pediatric resident accuracy in detecting pediatric upper extremity fractures.

Abstract PO4-26-09: The Advantage of Artificial-Intelligence in HER2 IHC 0 and 1+ Scoring in Breast Cancer

Abstract Background: Trastuzumab deruxtecan has substantially changed the treatment of HER2-low breast cancer, emphasizing the need for accurate differentiation between HER2 immunohistochemistry (IHC) scores of 0 and 1+. However, the current accuracy of HER2 IHC 0 and 1+ scoring in real-world is inadequate. Artificial intelligence (AI) has emerged as a potential solution to improve interpretation accuracy. We developed an AI algorithm based on whole slide images (WSI) to quantitatively assess HER2 expression and its role in interpreting HER2 IHC scores of 0 and 1+. Methods: Our three-phase AI analysis framework involved segmenting tumor areas (excluding ductal carcinoma in situ), detecting and classifying tumor cells based on membrane staining patterns, and grading HER2 IHC scores according to the 2018 ASCO/CAP HER2 guideline. The AI tool was trained using 6012 patches annotated by experienced pathologists. Performance evaluation was conducted using a test dataset comprising 265 slides. A total of 141 HER2 IHC slides (27 IHC 0 and 114 IHC 1+) from patients diagnosed with invasive breast cancer at Fudan University Shanghai Cancer Center in 2021 were included. Two pretrained expert pathologists independently rescored the HER2 slides, followed by analysis using the AI tool. All inconsistent cases were also reviewed by a third senior pathologist. Interobserver agreement between the pathologists and concordance between the pathologists and the AI interpretation results were assessed. We also explored potential ranges where HER2 interpretation by pathologists exhibited inconsistency or inaccuracy. Results: The HER2 AI algorithm showed high performance in interpreting all levels of HER2 expression, with an overall kappa value of 0.85. For HER2 IHC 0 and 1+ cases, the AI model demonstrated high sensitivities (0.839 and 0.914) and specificities (0.983 and 0.890) (Table 1). After the rescoring process, 51 cases were reclassified as IHC 0 and 90 cases as IHC 1+. The overall agreement rate between the historical and re-scoring results was 73.05% (103/141). The agreement rate between the AI and re-scoring results was 91.49% (129/141) (Table 2), indicating comparable interpretation capabilities between the AI model and well-trained pathologists. The interobserver agreement between the two pathologists was 83.69% (118/141), with agreement rates of 88.23% (45/51) for IHC 0 and 81.11% (73/90) for IHC 1+. Analyzing the inconsistent cases (n=12) between IHC 0 and IHC 1+, we examined the percentage of weak and incomplete expression provided by the AI. In 9 out of 12 cases, the AI provided the same results as the third senior pathologist, suggesting that AI could assist in interpretation within this range. We identified a range of 2.83% to 24.98% as a "grey-zone," where even well-trained pathologists provided inconsistent results. Conclusion: Our study demonstrates the excellent performance of an AI-based tool for scoring HER2 IHC 0 and 1+. The AI model exhibits comparable capabilities to well-trained pathologists in interpreting HER2 expression. Additionally, we identified a "grey-zone" among pathologists, highlighting the limitations of manual subjective interpretation. AI assistance within this zone may help mitigate subjective variations. Table 1. Correlation between pathologist read and AI read of HER2 immunohistochemistry. Table 2. Correlation between pathologist rescoring and AI read of HER2 immunohistochemistry. Citation Format: Ming Li, Hong Lv, Yizhi Zhao, Chenglu Zhu, Hansheng Li, Mingzhen Lin, Wen-Tao Yang. The Advantage of Artificial-Intelligence in HER2 IHC 0 and 1+ Scoring in Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-26-09.

Prospective study of AI-assisted prediction of breast malignancies in physical health examinations: role of off-the-shelf AI software and comparison to radiologist performance.

In physical health examinations, breast sonography is a commonly used imaging method, but it can lead to repeated exams and unnecessary biopsy due to discrepancies among radiologists and health centers. This study explores the role of off-the-shelf artificial intelligence (AI) software in assisting radiologists to classify incidentally found breast masses in two health centers. Female patients undergoing breast ultrasound examinations with incidentally discovered breast masses were categorized according to the 5th edition of the Breast Imaging Reporting and Data System (BI-RADS), with categories 3 to 5 included in this study. The examinations were conducted at two municipal health centers from May 2021 to May 2023.The final pathological results from surgical resection or biopsy served as the gold standard for comparison. Ultrasonographic images were obtained in longitudinal and transverse sections, and two junior radiologists and one senior radiologist independently assessed the images without knowing the pathological findings. The BI-RADS classification was adjusted following AI assistance, and diagnostic performance was compared using receiver operating characteristic curves. A total of 196 patients with 202 breast masses were included in the study, with pathological results confirming 107 benign and 95 malignant masses. The receiver operating characteristic curve showed that experienced breast radiologists had higher diagnostic performance in BI-RADS classification than junior radiologists, similar to AI classification (AUC = 0.936, 0.806, 0.896, and 0.950, p < 0.05). The AI software improved the accuracy, sensitivity, and negative predictive value of the adjusted BI-RADS classification for the junior radiologists' group (p< 0.05), while no difference was observed in the senior radiologist group. Furthermore, AI increased the negative predictive value for BI-RADS 4a masses and the positive predictive value for 4b masses among radiologists (p < 0.05). AI enhances the sensitivity of invasive breast cancer detection more effectively than ductal carcinoma in situ and rare subtypes of breast cancer. The AI software enhances diagnostic efficiency for breast masses, reducing the performance gap between junior and senior radiologists, particularly for BI-RADS 4a and 4b masses. This improvement reduces unnecessary repeat examinations and biopsies, optimizing medical resource utilization and enhancing overall diagnostic effectiveness.

Abstract PS03-06: Clinical implementation of artificial-intelligence-assisted detection of breast cancer metastases in sentinel lymph nodes: saving costs and time (the CONFIDENT-B trial)

Abstract Background Metastases in sentinel lymph nodes (SN) of breast cancer (BC) patients are strongly associated with a worse survival and consequently guide treatment. If metastases are absent upon pathologist’ assessment of the regular hematoxylin and eosin (HE)-slide, additional immunohistochemistry (IHC)-stains are performed to ensure that no metastases are missed. However, these stains come at high additional costs, which may exceed specimen reimbursement. Fortunately, digital pathology is becoming more common, thereby creating an avenue of opportunities for artificial intelligence (AI) assistance. Although the number of studies on (promising) AI-algorithms increases exponentially, studies on actual clinical implementation are lacking. In this single-center prospective trial, we investigated to which extent an artificial intelligence (AI)-assisted clinical workflow for the detection of SN-metastases reduces IHC-use, while maintaining current diagnostic safety standards. Methods We enrolled 190 SN-specimens of 182 patients with invasive or in situ BC from September 2022 to May 2023. SN-specimens were allocated bi-weekly to either the control-arm (n=90) or the intervention-arm (n=100). In the control-arm, SN-specimens were digitally assessed according to the current clinical workflow, while pathologists in the intervention-arm assessed the SN-specimen with the output the ‘Metastasis-Detection-App’ (Visiopharm©) available. In both groups, IHC was performed in all morphologically negative cases. Main outcome was the relative risk (RR) of IHC-use per detected case of SN-metastases. Case-mix adjustment was performed by log-binomial regression. Results Overall, 59 SN-specimens contained metastases (31.1%). AI-assistance resulted in a significantly lower risk of IHC-use per detected case of SN-metastases (adjusted RR: 0.680, 95% CI: 0.347-0.878). Besides preventing IHC-use, thereby reducing costs, AI-assisted pathologists also spent significantly less time on their assessment of the SN-specimen (3m:41s vs. 6m:04s, p = 0.028). Furthermore, the sensitivity of AI-assisted pathologists was up to 30% higher. The AI-assisted pathologists missed two cases of micro-metastases in the intervention arm, one of which was in retrospect highlighted by the algorithm, while in the other case the tumor cells were located in a heavily cauterized area of the HE-slide and therefore only visible on the (serial) IHC-slides. In the control arm, the algorithm in retrospect picked up all micro- and macro-metastases and nearly half of the isolated tumor cells (ITC). In addition, all participating pathologists stated that AI was easy to use, that they felt confident using AI, and that besides saving them time, AI made their work more enjoyable. Cost reductions on IHC by AI-assistance depend on laboratory policy (i.e. when and on how many levels IHC is performed), but, at a cost of €25,- per IHC-stain, range from €1.500 to €3.500 per 100 SN’s in a scenario where IHC is performed in all morphologically negative cases. In a scenario where IHC is only performed in patients in whom finding ITC has clinical consequences (i.e. patients who received neoadjuvant treatment), cost savings on IHC range from €7.500-€12.500 per 100 SNs, depending on laboratory policy. Conclusion AI-implementation for the detection of SN-metastases in BC-patients leads to a significant reduction of IHC-use and subsequent costs, while saving pathologists time and making their work more enjoyable. Importantly, AI-implementation during this trial was safe and patients were not at risk of an inferior diagnosis. By doing this trial alone, an estimated €3,000 on IHC-use was saved. Such tangible cost savings are crucial to build a viable business case for AI implementation in diagnostic pathology. Citation Format: Carmen van Dooijeweert, Rachel Flach, Natalie ter Hoeve, Celien Vreuls, Roel Goldschmeding, Jan-Erik Freund, Paul Pham, Tri Nguyen, Elsken Van der Wall, Geert Frederix, Nikolas Stathonikos, Paul Van Diest. Clinical implementation of artificial-intelligence-assisted detection of breast cancer metastases in sentinel lymph nodes: saving costs and time (the CONFIDENT-B trial) [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS03-06.

Assistive AI in Lung Cancer Screening: A Retrospective Multinational Study in the United States and Japan.

Purpose To evaluate the impact of an artificial intelligence (AI) assistant for lung cancer screening on multinational clinical workflows. Materials and Methods An AI assistant for lung cancer screening was evaluated on two retrospective randomized multireader multicase studies where 627 (141 cancer-positive cases) low-dose chest CT cases were each read twice (with and without AI assistance) by experienced thoracic radiologists (six U.S.-based or six Japan-based radiologists), resulting in a total of 7524 interpretations. Positive cases were defined as those within 2 years before a pathology-confirmed lung cancer diagnosis. Negative cases were defined as those without any subsequent cancer diagnosis for at least 2 years and were enriched for a spectrum of diverse nodules. The studies measured the readers' level of suspicion (on a 0-100 scale), country-specific screening system scoring categories, and management recommendations. Evaluation metrics included the area under the receiver operating characteristic curve (AUC) for level of suspicion and sensitivity and specificity of recall recommendations. Results With AI assistance, the radiologists' AUC increased by 0.023 (0.70 to 0.72; P = .02) for the U.S. study and by 0.023 (0.93 to 0.96; P = .18) for the Japan study. Scoring system specificity for actionable findings increased 5.5% (57% to 63%; P < .001) for the U.S. study and 6.7% (23% to 30%; P < .001) for the Japan study. There was no evidence of a difference in corresponding sensitivity between unassisted and AI-assisted reads for the U.S. (67.3% to 67.5%; P = .88) and Japan (98% to 100%; P > .99) studies. Corresponding stand-alone AI AUC system performance was 0.75 (95% CI: 0.70, 0.81) and 0.88 (95% CI: 0.78, 0.97) for the U.S.- and Japan-based datasets, respectively. Conclusion The concurrent AI interface improved lung cancer screening specificity in both U.S.- and Japan-based reader studies, meriting further study in additional international screening environments. Keywords: Assistive Artificial Intelligence, Lung Cancer Screening, CT Supplemental material is available for this article. Published under a CC BY 4.0 license.

Assistance of Artificial Intelligence in the Early Detection of Oral Cancer is Transforming Diagnosis Methods

Assistance of Artificial Intelligence in the Early Detection of Oral Cancer is Transforming Diagnosis Methods

AI-assisted Analysis to Facilitate Detection of Humeral Lesions on Chest Radiographs.

Purpose To develop an artificial intelligence (AI) system for humeral tumor detection on chest radiographs (CRs) and evaluate the impact on reader performance. Materials and Methods In this retrospective study, 14 709 CRs (January 2000 to December 2021) were collected from 13 468 patients, including CT-proven normal (n = 13 116) and humeral tumor (n = 1593) cases. The data were divided into training and test groups. A novel training method called false-positive activation area reduction (FPAR) was introduced to enhance the diagnostic performance by focusing on the humeral region. The AI program and 10 radiologists were assessed using holdout test set 1, wherein the radiologists were tested twice (with and without AI test results). The performance of the AI system was evaluated using holdout test set 2, comprising 10 497 normal images. Receiver operating characteristic analyses were conducted for evaluating model performance. Results FPAR application in the AI program improved its performance compared with a conventional model based on the area under the receiver operating characteristic curve (0.87 vs 0.82, P = .04). The proposed AI system also demonstrated improved tumor localization accuracy (80% vs 57%, P < .001). In holdout test set 2, the proposed AI system exhibited a false-positive rate of 2%. AI assistance improved the radiologists' sensitivity, specificity, and accuracy by 8.9%, 1.2%, and 3.5%, respectively (P < .05 for all). Conclusion The proposed AI tool incorporating FPAR improved humeral tumor detection on CRs and reduced false-positive results in tumor visualization. It may serve as a supportive diagnostic tool to alert radiologists about humeral abnormalities. Keywords: Artificial Intelligence, Conventional Radiography, Humerus, Machine Learning, Shoulder, Tumor Supplemental material is available for this article. © RSNA, 2024.

Artificial intelligence assists identification and pathologic classification of glomerular lesions in patients with diabetic nephropathy.

Glomerular lesions are the main injuries of diabetic nephropathy (DN) and are used as a crucial index for pathologic classification. Manual quantification of these morphologic features currently used is semi-quantitative and time-consuming. Automatically quantifying glomerular morphologic features is urgently needed. A series of convolutional neural networks (CNN) were designed to identify and classify glomerular morphologic features in DN patients. Associations of these digital features with pathologic classification and prognosis were further analyzed. Our CNN-based model achieved a 0.928 F1-score for global glomerulosclerosis and 0.953 F1-score for Kimmelstiel-Wilson lesion, further obtained a dice of 0.870 for the mesangial area and F1-score beyond 0.839 for three glomerular intrinsic cells. As the pathologic classes increased, mesangial cell numbers and mesangial area increased, and podocyte numbers decreased (p for all < 0.001), while endothelial cell numbers remained stable (p = 0.431). Glomeruli with Kimmelstiel-Wilson lesion showed more severe podocyte deletion compared to those without (p < 0.001). Furthermore, CNN-based classifications showed moderate agreement with pathologists-based classification, the kappa value between the CNN model 3 and pathologists reached 0.624 (ranging from 0.529 to 0.688, p < 0.001). Notably, CNN-based classifications obtained equivalent performance to pathologists-based classifications on predicting baseline and long-term renal function. Our CNN-based model is promising in assisting the identification and pathologic classification of glomerular lesions in DN patients.

Development and external validation of a transfer learning-based system for the pathological diagnosis of colorectal cancer: a large emulated prospective study.

The progress in Colorectal cancer (CRC) screening and management has resulted in an unprecedented caseload for histopathological diagnosis. While artificial intelligence (AI) presents a potential solution, the predominant emphasis on slide-level aggregation performance without thorough verification of cancer in each location, impedes both explainability and transparency. Effectively addressing these challenges is crucial to ensuring the reliability and efficacy of AI in histology applications. In this study, we created an innovative AI algorithm using transfer learning from a polyp segmentation model in endoscopy. The algorithm precisely localized CRC targets within 0.25 mm² grids from whole slide imaging (WSI). We assessed the CRC detection capabilities at this fine granularity and examined the influence of AI on the diagnostic behavior of pathologists. The evaluation utilized an extensive dataset comprising 858 consecutive patient cases with 1418 WSIs obtained from an external center. Our results underscore a notable sensitivity of 90.25% and specificity of 96.60% at the grid level, accompanied by a commendable area under the curve (AUC) of 0.962. This translates to an impressive 99.39% sensitivity at the slide level, coupled with a negative likelihood ratio of <0.01, signifying the dependability of the AI system to preclude diagnostic considerations. The positive likelihood ratio of 26.54, surpassing 10 at the grid level, underscores the imperative for meticulous scrutiny of any AI-generated highlights. Consequently, all four participating pathologists demonstrated statistically significant diagnostic improvements with AI assistance. Our transfer learning approach has successfully yielded an algorithm that can be validated for CRC histological localizations in whole slide imaging. The outcome advocates for the integration of the AI system into histopathological diagnosis, serving either as a diagnostic exclusion application or a computer-aided detection (CADe) tool. This integration has the potential to alleviate the workload of pathologists and ultimately benefit patients.

Mengubah Gambar Sketsa Menjadi Visual yang Menakjubkan : Membangun Dunia Animasi dengan Kecerdasan Buatan

The rapid development of technology in the current era has brought changes to the industry of animation. The utilization of artificial intelligence technology in the field of animation helps accelerate the creative process of creating animated films, one of which is the search for visual concepts to be used. The search for visual concepts can now be made using only sketch images through the assistance of artificial intelligence. The research method employed is exploration, and this study will explore the utilization of Control Trait on the artificial intelligence platform used, namely Playground AI. The sketch images used consist of 10 images, comprising 5 character sketch images and 5 environmental sketch images. The results of the research reveal that the recommended Control Trait values for sketch images fall within the range of 0.4 – 0.6. The conclusion of this study is that black and white sketch images can be quickly transformed into stunning visuals with the assistance of artificial intelligence. The generated images are sufficiently good to be used as visual concepts for an animated film.

AI-assisted capsule endoscopy reading in suspected small bowel bleeding: a multicentre prospective study

Capsule endoscopy reading is time consuming, and readers are required to maintain attention so as not to miss significant findings. Deep convolutional neural networks can recognise relevant findings, possibly exceeding human performances and reducing the reading time of capsule endoscopy. Our primary aim was to assess the non-inferiority of artificial intelligence (AI)-assisted reading versus standard reading for potentially small bowel bleeding lesions (high P2, moderate P1; Saurin classification) at per-patient analysis. The mean reading time in both reading modalities was evaluated among the secondary endpoints. Patients aged 18 years or older with suspected small bowel bleeding (with anaemia with or without melena or haematochezia, and negative bidirectional endoscopy) were prospectively enrolled at 14 European centres. Patients underwent small bowel capsule endoscopy with the Navicam SB system (Ankon, China), which is provided with a deep neural network-based AI system (ProScan) for automatic detection of lesions. Initial reading was performed in standard reading mode. Second blinded reading was performed with AI assistance (the AI operated a first-automated reading, and only AI-selected images were assessed by human readers). The primary endpoint was to assess the non-inferiority of AI-assisted reading versus standard reading in the detection (diagnostic yield) of potentially small bowel bleeding P1 and P2 lesions in a per-patient analysis. This study is registered with ClinicalTrials.gov, NCT04821349. From Feb 17, 2021 to Dec 29, 2021, 137 patients were prospectively enrolled. 133 patients were included in the final analysis (73 [55%] female, mean age 66·5 years [SD 14·4]; 112 [84%] completed capsule endoscopy). At per-patient analysis, the diagnostic yield of P1 and P2 lesions in AI-assisted reading (98 [73·7%] of 133 lesions) was non-inferior (p<0·0001) and superior (p=0·0213) to standard reading (82 [62·4%] of 133; 95% CI 3·6-19·0). Mean small bowel reading time was 33·7 min (SD 22·9) in standard reading and 3·8 min (3·3) in AI-assisted reading (p<0·0001). AI-assisted reading might provide more accurate and faster detection of clinically relevant small bowel bleeding lesions than standard reading. ANKON Technologies, China and AnX Robotica, USA provided the NaviCam SB system.

Review of trends regarding artificial intelligence and its prospects for procedural decisions during criminal proceeding

Discussions on the advantages and risks of artificial intelligence (AI) in various fields are among the most relevant today. It is evident that proponents and opponents of its implementation have existed, exist, and will continue to exist. However, it is undeniable that with each scientific and technological advancement, humanity has the opportunity to improve or worsen the quality of its existence. Therefore, it is crucial to establish rules for developers and users who must be aware of the existing threats posed by the use of a particular tool, despite or in defiance of these limitations. This overview aims to explore how various aspects of our lives, including the legal sphere, are changing due to the widespread availability and increasing accessibility of artificial intelligence (AI). It seeks to clarify the role of the state as a regulator and implementer of relevant functions, the rules developers and users must adhere to, the aspects of AI usage already accessible to the legal sector, and how AI can assist in decision-making during criminal proceedings (while still leaving the leading role in decision-making and choice of alternatives to humans). It also discusses the prospects ahead and the myths that need to be overcome for progress. Through analysis and synthesis, it has been observed that threats from harmful AI are present, especially when it is deployed not for socially beneficial or human-centric purposes. Regulation of the AI sphere is crucial in all its aspects, starting from ethical considerations and ensuring an anthropocentric approach to AI development and usage, and ending with cutting-edge developments, including AI applications in various fields, such as the creation of new product designs like confectionery. Despite potential risks and threats, the use of artificial intelligence can have advantages, but careful regulation of this area is necessary. It is also noted that regulating AI to the detriment of progressive development should be avoided. It is argued that in the legal and law enforcement sectors, AI assistance can bring many benefits, but its use must be accompanied by a full awareness of the axiom that “AI systems for justice and law enforcement are considered ‘high risks’.” Therefore, before actively applying AI, especially in this realm, it is essential to establish clear boundaries and rules, and importantly, accountability for any harm caused. Such sensitive aspects as the integrity of human privacy, respect for human rights, and the like should prevail in this mechanism.

The virtual reference radiologist: comprehensive AI assistance for clinical image reading and interpretation.

Large language models (LLMs) have shown potential in radiology, but their ability to aid radiologists in interpreting imaging studies remains unexplored. We investigated the effects of a state-of-the-art LLM (GPT-4) on the radiologists' diagnostic workflow. In this retrospective study, six radiologists of different experience levels read 40 selected radiographic [n = 10], CT [n = 10], MRI [n = 10], and angiographic [n = 10] studies unassisted (session one) and assisted by GPT-4 (session two). Each imaging study was presented with demographic data, the chief complaint, and associated symptoms, and diagnoses were registered using an online survey tool. The impact of Artificial Intelligence (AI) on diagnostic accuracy, confidence, user experience, input prompts, and generated responses was assessed. False information was registered. Linear mixed-effect models were used to quantify the factors (fixed: experience, modality, AI assistance; random: radiologist) influencing diagnostic accuracy and confidence. When assessing if the correct diagnosis was among the top-3 differential diagnoses, diagnostic accuracy improved slightly from 181/240 (75.4%, unassisted) to 188/240 (78.3%, AI-assisted). Similar improvements were found when only the top differential diagnosis was considered. AI assistance was used in 77.5% of the readings. Three hundred nine prompts were generated, primarily involving differential diagnoses (59.1%) and imaging features of specific conditions (27.5%). Diagnostic confidence was significantly higher when readings were AI-assisted (p > 0.001). Twenty-three responses (7.4%) were classified as hallucinations, while two (0.6%) were misinterpretations. Integrating GPT-4 in the diagnostic process improved diagnostic accuracy slightly and diagnostic confidence significantly. Potentially harmful hallucinations and misinterpretations call for caution and highlight the need for further safeguarding measures. Using GPT-4 as a virtual assistant when reading images made six radiologists of different experience levels feel more confident and provide more accurate diagnoses; yet, GPT-4 gave factually incorrect and potentially harmful information in 7.4% of its responses.

A Study on AI in Marketing and Improving the Business Procedure

Everything will be accessible online in early 2020. To put it briefly, everything is bought and sold online. It represents a wise company with a wise customer. Artificial intelligence is being used in various industries, including business and engineering, because it is dependable, economical, capable of solving complex issues, and able to make expert system decisions. Perhaps the most prevalent and dynamic kind of marketing available today is social media. Artificial intelligence (AI) assists marketers in analyzing brand visibility and interactions to gauge client happiness. Consequently, a marketer can effectively promote his goods and services and run his firm thanks to the strength of artificial intelligence systems in digital marketing tactics. AI is becoming more and more sophisticated in marketing, and marketers have the ability to use it to their advantage by properly implementing and overseeing AI solutions, which will become increasingly important for enhancing the company.

Combining Artificial Intelligence and Simplified Image Processing for the Automatic Detection of Mycobacterium tuberculosis in Acid-fast Stain: A Cross-institute Training and Validation Study.

Tuberculosis (TB) poses a significant health threat in Taiwan, necessitating efficient detection methods. Traditional screening for acid-fast positive bacilli in acid-fast stain is time-consuming and prone to human error due to staining artifacts. To address this, we present an automated TB detection platform leveraging deep learning and image processing. Whole slide images from 2 hospitals were collected and processed on a high-performance system. The system utilizes an image processing technique to highlight red, rod-like regions and a modified EfficientNet model for binary classification of TB-positive regions. Our approach achieves a 97% accuracy in tile-based TB image classification, with minimal loss during the image processing step. By setting a 0.99 threshold, false positives are significantly reduced, resulting in a 94% detection rate when assisting pathologists, compared with 68% without artificial intelligence assistance. Notably, our system efficiently identifies artifacts and contaminants, addressing challenges in digital slide interpretation. Cross-hospital validation demonstrates the system's adaptability. The proposed artificial intelligence-assisted pipeline improves both detection rates and time efficiency, making it a promising tool for routine pathology work in TB detection.

AI Value Creation: Operational value creation potential of semantic AI assistants in corporate training

This paper explores the potential value of semantic artificial intelligence (AI) in corporate training based on a study of semantic AI learning assistants in secondary schools and interviews with human resource professionals. The findings suggest that semantic AI can significantly improve learning outcomes and streamline corporate training processes through personalized, on-demand learning. However, successful implementation requires addressing privacy, security, and user acceptance challenges. While benefits such as efficiency, cost savings, and performance gains are achievable, value creation depends on the organizational context. Further research should explore the long-term impact of semantic AI in corporate learning.

YOLOX-SwinT algorithm improves the accuracy of AO/OTA classification of intertrochanteric fractures by orthopedic trauma surgeons

Intertrochanteric fracture (ITF) classification is crucial for surgical decision-making. However, orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected. The objective of this study was to utilize an artificial intelligence (AI) method to improve the accuracy of ITF classification. We trained a network called YOLOX-SwinT, which is based on the You Only Look Once X (YOLOX) object detection network with Swin Transformer (SwinT) as the backbone architecture, using 762 radiographic ITF examinations as the training set. Subsequently, we recruited 5 senior orthopedic trauma surgeons (SOTS) and 5 junior orthopedic trauma surgeons (JOTS) to classify the 85 original images in the test set, as well as the images with the prediction results of the network model in sequence. Statistical analysis was performed using the Statistical Package for the Social Sciences (SPSS) 20.0 (IBM Corp., Armonk, NY, USA) to compare the differences among the SOTS, JOTS, SOTS+AI, JOTS+AI, SOTS+JOTS, and SOTS+JOTS+AI groups. All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field. Based on the actual clinical needs, after discussion, we integrated 8 subgroups into 5 new subgroups, and the dataset was divided into training, validation, and test sets by the ratio of 8:1:1. The mean average precision at the intersection over union (IoU) of 0.5 (mAP50) for subgroup detection reached 90.29%. The classification accuracy values of SOTS, JOTS, SOTS+AI, and JOTS+AI groups were 56.24%±4.02%, 35.29%±18.07%, 79.53%±7.14%, and 71.53%±5.22%, respectively. The paired t-test results showed that the difference between the SOTS and SOTS+AI groups was statistically significant, as well as the difference between the JOTS and JOTS+AI groups, and the SOTS+JOTS and SOTS+JOTS+AI groups. Moreover, the difference between the SOTS+JOTS and SOTS+JOTS+AI groups in each subgroup was statistically significant, with all p<0.05. The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant, while the difference between the SOTS+AI and JOTS+AI groups was not statistically significant. With the assistance of AI, the subgroup classification accuracy of both SOTS and JOTS was significantly improved, and JOTS achieved the same level as SOTS. In conclusion, the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.