This narrative review outlines the current applications and considerations of artificial intelligence (AI) for diagnosis, management, and prognosis in rheumatoid arthritis (RA), axial spondyloarthritis (axSpA), and psoriatic arthritis (PsA). Advances in AI, mainly in machine learning and deep learning, have significantly influenced medical research and clinical practice over the past decades by offering precisions in data understanding and treatment approaches. AI applications have enhanced risk prediction models, early diagnosis, and better management in RA. Predictive models have guided treatment decisions such as-response to methotrexate and biologics-while wearable devices and electronic health records (EHR) improve disease activity monitoring. In addition, AI applications are reported as promising for the early identification of extra-articular involvements, prediction, detection, and assessment of comorbidities. In axSpA, AI-driven models using imaging techniques such as sacroiliac radiography, magnetic resonance imaging, and computed tomography have increased diagnostic accuracy, especially for early inflammatory changes. Predictive algorithms help stratify and predict disease outcomes, while clinical decision support systems integrate clinical and imaging data for optimized management. For PsA, AI has also allowed for early detection among psoriasis patients using genetic markers, immune profiling, and EHR-based natural language processing systems. Overall, AI models may predict diagnosis, disease severity, treatment response, and comorbidities to improve care in patients with RA, axSpA, and PsA. As a rapidly developing and improving area, AI has the potential to change our current perspective of medical practice by offering better diagnostic evaluation and treatments and improved patient follow-up. Multimodal AI, focusing on collaboration, reliability, transparency, and patient-centered innovation, looks like the future of medical practice. However, data quality, model interpretability, and ethical considerations must be addressed to ensure reliable and equitable applications in clinical practice.

Interval breast cancers (IBCs) are detected between regular mammographic screenings after an initially negative result. Studies have shown that the prognosis of IBCs is similar to that of unscreened symptomatic cancers and is hence a surrogate used to assess the effectiveness of screening programs. This systematic review consolidates the current literature available on strategies to reduce the rates of IBC. Following PRISMA guidelines, three databases were searched from inception till October 29, 2023 to identify papers, which reported IBC rates. Key search terms included "interval breast cancer", "mammogram", "tomosynthesis" and "screening". A total of 32 articles were included. Fourteen studies discussed the use of digital breast tomosynthesis (DBT) as an alternative screening modality to mammograms. Six studies discussed the use of artificial intelligence (AI) on mammograms, five studies discussed the use of supplemental modalities including ultrasonography (US) in addition to mammograms, five studies discussed varying screening intervals and two studies discussed tamoxifen use. The trajectory of IBCs can be altered by early detection when they are more amenable to treatment, through advanced screening techniques, adjusting inter-screening intervals and modifiable risk factors. The goal is to create a screening protocol that is economically effective and accessible to various populations.

Thoracic diseases, including pneumonia, tuberculosis, lung cancer, and others, pose significant health risks and require timely and accurate diagnosis to ensure proper treatment. Thus, in this research, a model for thorax disease classification using Chest X-rays is proposed by considering deep learning model. The input is pre-processed by resizing, normalizing pixel values, and applying data augmentation to address the issue of imbalanced datasets and improve model generalization. Significant features are extracted from the images using an Enhanced Auto-Encoder (EnAE) model, which combines a stacked auto-encoder architecture with an attention module to enhance feature representation and classification accuracy. To further improve feature selection, we utilize the Chaotic Whale Optimization (ChWO) Algorithm, which optimally selects the most relevant attributes from the extracted features. Finally, the disease classification is performed using the novel Improved Swin Transformer (IMSTrans) model, which is designed to efficiently process high-dimensional medical image data and achieve superior classification performance. The proposed EnAE + ChWO+IMSTrans model for thorax disease classification was evaluated using extensive Chest X-ray datasets and the Lung Disease Dataset. The proposed method demonstrates enhanced Accuracy, Precision, Recall, F-Score, MCC and MAE of 0.964, 0.977, 0.9845, 0.964, 0.9647, and 0.184 respectively indicating the reliable and efficient solution for thorax disease classification.

<b>Background:</b> No robust biomarkers have been identified to predict the efficacy of programmed cell death protein 1 (PD-1) inhibitors in patients with locoregionally advanced nasopharyngeal carcinoma (LANPC). We aimed to develop radiomic models using pre-immunotherapy MRI to predict the response to PD-1 inhibitors and the patient prognosis. <b>Methods:</b> This study included 246 LANPC patients (training cohort, <i>n</i> = 117; external test cohort, <i>n</i> = 129) from 10 centers. The best-performing machine learning classifier was employed to create the radiomic models. A combined model was constructed by integrating clinical and radiomic data. A radiomic interpretability study was performed with whole slide images (WSIs) stained with hematoxylin and eosin (H&E) and immunohistochemistry (IHC). A total of 150 patient-level nuclear morphological features (NMFs) and 12 cell spatial distribution features (CSDFs) were extracted from WSIs. The correlation between the radiomic and pathological features was assessed using Spearman correlation analysis. <b>Results:</b> The radiomic model outperformed the clinical and combined models in predicting treatment response (area under the curve: 0.760 vs. 0.559 vs. 0.652). For overall survival estimation, the combined model performed comparably to the radiomic model but outperformed the clinical model (concordance index: 0.858 vs. 0.812 vs. 0.664). Six treatment response-related radiomic features correlated with 50 H&E-derived (146 pairs, |<i>r</i>|= 0.31 to 0.46) and 2 to 26 IHC-derived NMF, particularly for CD45RO (69 pairs, |<i>r</i>|= 0.31 to 0.48), CD8 (84, |<i>r</i>|= 0.30 to 0.59), PD-L1 (73, |<i>r</i>|= 0.32 to 0.48), and CD163 (53, |<i>r</i>| = 0.32 to 0.59). Eight prognostic radiomic features correlated with 11 H&E-derived (16 pairs, |<i>r</i>|= 0.48 to 0.61) and 2 to 31 IHC-derived NMF, particularly for PD-L1 (80 pairs, |<i>r</i>|= 0.44 to 0.64), CD45RO (65, |<i>r</i>|= 0.42 to 0.67), CD19 (35, |<i>r</i>|= 0.44 to 0.58), CD66b (61, |<i>r</i>| = 0.42 to 0.67), and FOXP3 (21, |<i>r</i>| = 0.41 to 0.71). In contrast, fewer CSDFs exhibited correlations with specific radiomic features. <b>Conclusion:</b> The radiomic model and combined model are feasible in predicting immunotherapy response and outcomes in LANPC patients. The radiology-pathology correlation suggests a potential biological basis for the predictive models.

Radiology has usually been the field of medicine that has been at the forefront of technological advances, often being the first to wholeheartedly embrace them. Whether it's from digitization to cloud side architecture, radiology has led the way for adopting the latest advances. With the advent of large language models (LLMs), especially with the unprecedented explosion of freely available ChatGPT, time is ripe for radiology and radiologists to find novel ways to use the technology to improve their workflow. Towards this, we believe these LLMs have a key role in the radiology reading room not only to expedite processes, simplify mundane and archaic tasks, but also to increase the radiologist's and radiologist trainee's knowledge base at a far faster pace. In this article, we discuss some of the ways we believe ChatGPT, and the likes can be harnessed in the reading room.

The use of artificial intelligence (AI) in medicine has shown promise to improve the quality of healthcare decisions. However, AI can be biased in a manner that produces unfair predictions for certain demographic subgroups. In MIMIC-CXR, a publicly available dataset of over 300,000 chest X-ray images, diagnostic AI has been shown to have a higher false negative rate for racial minorities. We evaluated the capacity of synthetic data augmentation, oversampling, and demographic-based corrections to enhance the fairness of AI predictions. We show that adjusting unfair predictions for demographic attributes, such as race, is ineffective at improving fairness or predictive performance. However, using oversampling and synthetic data augmentation to modify disease prevalence reduced such disparities by 74.7% and 10.6%, respectively. Moreover, such fairness gains were accomplished without reduction in performance (95% CI AUC: [0.816, 0.820] versus [0.810, 0.819] versus [0.817, 0.821] for baseline, oversampling, and augmentation, respectively).

Machine learning (ML) algorithms play a crucial role in the early and accurate diagnosis of Alzheimer's Disease (AD), which is essential for effective treatment planning. However, existing methods are not well-suited for identifying Mild Cognitive Impairment (MCI), a critical transitional stage between normal aging and AD. This inadequacy is primarily due to label imbalance and bias from different sensitve attributes in MCI classification. To overcome these challenges, we have designed an end-to-end fairness-aware approach for label-imbalanced classification, tailored specifically for neuroimaging data. This method, built on the recently developed FACIMS framework, integrates into STREAMLINE, an automated ML environment. We evaluated our approach against nine other ML algorithms and found that it achieves comparable balanced accuracy to other methods while prioritizing fairness in classifications with five different sensitive attributes. This analysis contributes to the development of equitable and reliable ML diagnostics for MCI detection.

Artificial intelligence (AI) shows promise in clinical tasks, yet its integration into workflows remains underexplored. This study proposes an AI-aided same-day diagnostic imaging workup to reduce recall rates following abnormal screening mammograms and alleviate patient anxiety while waiting for the diagnostic examinations. Using discrete simulation, we found minimal disruption to the workflow (a 4% reduction in daily patient volume or a 2% increase in operating time) under specific conditions: operation from 9 am to 12 pm with all radiologists managing all patient types (screenings, diagnostics, and biopsies). Costs specific to the AI-aided same-day diagnostic workup include AI software expenses and potential losses from unused pre-reserved slots for same-day diagnostic workups. These simulation findings can inform the implementation of an AI-aided same-day diagnostic workup, with future research focusing on its potential benefits, including improved patient satisfaction, reduced anxiety, lower recall rates, and shorter time to cancer diagnoses and treatment.