BACKGROUND:The effect of AI-assisted diagnosis on sonographer performance in real-world prenatal settings remains unclear. This study aimed to assess the Prenatal Ultrasound Diagnosis Artificial Intelligence Conduct System (PAICS) in detecting AI-recognisable intracranial malformations and its effect on identifying anomalies beyond its recognition scope in high-risk pregnancies. METHODS:This multicentre, self-crossover, randomised controlled trial was done at five Chinese centres. High risk of fetal malformation singleton pregnancies (11-32 weeks' gestation) were examined by sonographers with 3 to less than 8 years of experience. Participants were randomly assigned 1:1 to two diagnostic sequences: independent real-time diagnosis followed by PAICS-assisted offline review, or PAICS-assisted real-time diagnosis followed by independent offline review, with a 4-week washout period. Allocation was concealed. An expert panel's diagnosis on video review served as the reference standard. Sonographers were masked to fetal anomaly status but were aware of AI assistance during scanning. Outcome assessors and the independent expert panel responsible for the reference standard diagnosis were masked to group assignments. Primary outcomes were sensitivity and specificity in detecting ten specific fetal intracranial malformations, with sensitivity assessed for superiority and specificity against a 5% margin of non-inferiority. The trial is registered with the Chinese Clinical Trial Registry (ChiCTR2200063424). FINDINGS:Between Sept 6, 2022 and Nov 1, 2023, 1584 scans were completed. PAICS-assisted diagnosis improved sensitivity for detecting specific fetal intracranial malformations. In the fetal-based analysis, sensitivity increased by 0·087 (95% CI 0·029 to 0·147; psuperiority<0·0001), with specificity meeting the non-inferiority criterion (difference 0·009, 95% CI -0·006 to 0·023; pnon-inferiority<0·0001). In the malformation-targeted analysis, sensitivity improved by 0·118 (95% CI 0·054 to 0·181; psuperiority<0·0001), and specificity was also non-inferior (difference 0·001, 95% CI -0·001 to 0·002; pnon-inferiority<0·0001). No adverse events related to the diagnostic procedure or the AI assistance were reported. INTERPRETATION:PAICS improves sonographers' sensitivity for detecting targeted fetal intracranial malformations while preserving their specificity, which supports the integration of AI assistance into fetal anomaly screening in high-risk clinical settings. FUNDING:National Natural Science Foundation of China, Guangdong Provincial Basic and Applied Basic Research Fund Project, Guangzhou Science and Technology Program, and Sun Yat-sen University Fundamental Research Funds for the Junior Faculty Program.
Advances in digital pathology, image analysis, and artificial intelligence (AI) are rapidly transforming how pathologists and researchers interact with tissue samples and enable the development of diagnostic tools that harness high-resolution whole-slide images; these advances are in turn creating new opportunities for research, education, and routine clinical care globally. Liver disease is no exception, and digital pathology and AI have many applications in the diagnosis of liver cancer and liver diseases and in the assessment and management of transplantation. Although quantitative image analysis techniques have been applied to liver disease in research settings for over 50 years, recent improvements in image resolution, data storage, and the availability of advanced AI methods such as deep learning have driven multiple exciting developments. In this Review, we summarise the advancements in digital pathology, image analysis, and AI in liver disease. Key challenges such as access to and the logistics of using digital solutions, quality issues, and appropriate guidance in research and clinical use are reviewed, along with potential solutions to these challenges in the context of liver pathology and liver disease. Digital technologies are well established in liver pathology research, and access in clinical practice is increasing, with potential to address current laboratory challenges. Further evaluation is required to assess real-world effectiveness, clinical safety, and implementation of AI tools in liver pathology.
Electroencephalography is the most commonly used diagnostic tool for epilepsy. However, interpreting electroencephalograms (EEGs) requires expertise that is not widely available. Advances in digital technology and wearables have enabled large-scale EEG recording, generating vast amounts of data that cannot be managed through traditional visual interpretation by experts. Artificial intelligence (AI) has the potential to augment human expertise and reduce workloads. The application of artificial neural networks in analysing clinical EEG recordings has led to major breakthroughs, bringing AI-based EEG interpretation closer to clinical implementation. In this Review, we summarise the most important research and development results in this field from a clinical perspective. We provide an overview of AI applications in spike and seizure detection; analysis of data from wearable electroencephalographs, patients who are critically ill, and epilepsy surgery; and the automated interpretation of clinical EEGs.
Accurate prediction of treatment response and selection of optimal treatments remain challenging in epilepsy management. With no reliable surrogate biomarkers for treatment response, the current process of selecting an antiseizure medication remains largely a trial-and-error approach. Other non-pharmacological treatment options, such as epilepsy surgery, are viable alternatives for patients with drug-resistant epilepsy. Statistical and machine learning techniques have been used to predict seizure outcomes associated with antiseizure medications and epilepsy surgery. Recent breakthroughs in deep learning have unveiled new pathways and opportunities, potentially revolutionising personalised treatment selection in health care. In this Review, we explore a broad range of studies that have used various statistical and machine learning methodologies, with particular emphasis on state-of-the-art deep learning techniques to predict the outcomes of both pharmaceutical and surgical treatments for epilepsy. We also review potential future research trajectories and address the inherent challenges of incorporating machine learning into the clinical management of epilepsy.
