Heart disease is the leading cause of mortality globally. Electrocardiograms (ECGs) are standard instruments for the examination of heart conditions, but traditional analysis is time-consuming and prone to errors. Novel advances in artificial intelligence have improved ECG classification. However, some limitations remain, such as poor interpretability, computational cost, and class imbalance. This study proposes a novel deep learning algorithm based on Depthwise Separable Residual Attention called DRA-ECG and a customized Adaptive Focal Cross-Entropy (AFCE) loss function for cardiac condition classification. This proposed methodology leverages the Continuous Wavelet Transform (CWT) method to transform 1D raw ECG signals into 2D scalograms to enhance feature representation and training. The proposed customized AFCE loss function incorporated into the DRA-ECG model addresses the class imbalance problem and boost the performance of the model. More so, this study incorporates edge feature detection as a preprocessing technique to denoise and enhance the trainable features of the 2D scalograms for optimal feature representation. The proposed DRA-ECG model achieves a high accuracy of 98.17%, recall of 95.78%, F1-score of 95.82%, and precision of 95.89%. This study shows that the results achieved by the proposed DRA-ECG surpass the current state-of-the-art and existing research works, concerning classification performance and generalization ability in ECG classification, which underlines the effectiveness of the novel AFCE loss function for ensuring high-classification accuracy and robustness. The proposed novel methodology enhances heart disease classification and provides a robust and reliable solution for medical diagnosis, addressing the major drawbacks of existing models.

Depression is a prevalent mental health disorder that presents significant challenges for timely diagnosis and intervention. Automated Depression Detection (ADD) systems using text and audio offer scalable mental health assessment solutions. This review systematically evaluates 65 studies published between 2018 and 2024, focusing on ADD methods that utilize machine learning models with multimodal data. We examine key methodologies, including data augmentation, multimodal fusion, and feature extraction, along with state-of-the-art ADD systems. The review emphasizes the need for culturally adaptable, high-quality datasets and interpretable models for clinical use. We also identify gaps in longitudinal data and real-world applications. Future research should focus on developing clinically integrated, cross-cultural ADD systems that are interpretable, scalable, and robust. The findings of this review contribute to the research field by providing a comprehensive overview of existing methodologies, identifying gaps in the current literature, and offering insights for future advancements in depression detection using speech and text analysis.

Earlier this year, Tom Stanis was puzzling through what was next for his startup Story Health, which helps providers care for people with heart failure. The company had some big-name customers and plans to expand, but it last raised money in 2022. Stanis saw two options: shake more cash out of a stingy venture capital market, or sell.

Armed with $275 million in fresh funding and a built-in customer base, artificial intelligence company Innovaccer made the answer easy. It gobbled up Story Health for an undisclosed mix of equity and cash in September. 

Story Health is the fourth Innovaccer acquisition in about a year as it aims to become the default AI platform for health systems. CEO Abhinav Shashank plans to rapidly expand and to “accelerate that development through M&A,” he told STAT.

Innovaccer’s shopping spree is just one example of a trend playing out in digital health: big, well-funded companies with momentum are snapping up smaller players.

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About a year ago, I logged into MyChart the day before my annual wellness visit and saw a new consent form. My doctor wanted to record our visit so artificial intelligence could create notes and update my medical record. I agreed. Just hours after my visit ended, a comprehensive, accurate visit summary appeared in MyChart.

As a career clinical trialist, I immediately wondered: What if this tool were used for trial visits? Could it enable a breakthrough in research or patient engagement? How will clinical research adapt to what could be a “new normal” in medical documentation?

Driven by a crisis of clinician burnout, ambient digital scribes (ADS) like the one my doctor used are being rapidly adopted. Studies suggest these digital scribes can reduce documentation burden, improve clinician efficiency, and potentially enhance the patient experience. By digitizing conversations in real time, these scribes open possibilities beyond routine care, particularly in clinical research and trials.

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Our objective was to investigate how artificial intelligence (AI) predictions calculated on structured hospital data are presented to clinicians. We performed a systematic review of 5 databases and 9 other reviews, identifying 31 studies on 21 implemented clinical AI systems. We report current approaches to presenting AI predictions to clinicians, whether and how interaction on the user interface (UI) is used, how UIs have been evaluated and the extent to which clinicians have been involved in UI design and testing. The results indicate variation across systems in presentation content and styles, evaluation methods, and interaction approaches. Half of the systems implemented a co-design approach to UI development. Our findings provide valuable insights for future AI-based clinical decision support system designers, clinical AI researchers and healthcare organisations seeking to implement clinical AI solutions.

Clinical predictive analysis is a crucial task with numerous applications and has been extensively studied using machine learning approaches. Clinical notes, a vital data source, have been employed to develop natural language processing (NLP) models for risk prediction in healthcare with robust performance. However, clinical notes vary considerably in text composition–written by diverse healthcare providers for different purposes–and the impact of these variations on NLP modeling is also underexplored. It also remains uncertain whether the recent Large Language Models (LLMs) with instruction-following capabilities can effectively handle the risk prediction task out-of-the-box, especially when using routinely collected clinical notes instead of polished text. We address these two important research questions in the context of in-hospital mortality prediction within the critical care setting. Specifically, we propose a supervised hierarchical network with note-specific modules to account for variations across different note categories, and provide a detailed comparison with strong supervised baselines and LLMs. We benchmark 34 instruction-following LLMs based on zero-shot, few-shot, and chain-of-thought prompting with diverse prompt templates. Our results demonstrate that the note-specific network delivers improved risk prediction performance compared to established supervised baselines from both measurement-based and text-based modeling. In contrast, LLMs consistently underperform on this critical task, despite their remarkable performances in other domains. This highlights important limitations and raises caution regarding the use of LLMs for risk assessment in the critical setting. Additionally, we show that the proposed model can be leveraged to select informative clinical notes to enhance the training of other models.

npj Digital Medicine, Published online: 08 October 2025; doi:10.1038/s41746-025-01939-x

Systematic review: digital biomarkers of fatigue in chronic diseases

Identifying aggregation-prone proteins or peptides is essential for advancing our understanding of amyloid aggregation processes and their related pathogenic mechanisms. Recognizing potential amyloid hexapeptides can also support peptide-based drug design and reduce experimental costs. In this study, we proposed TSLAmy, a computational model designed to predict amyloid hexapeptides using a two-stage learning framework. In the first stage, we performed feature extraction on the hexapeptides, and in the second stage, we presented prediction model for amyloid hexapeptide aggregation. Firstly, to ensure balanced dataset partitioning, we applied a clustering-based method by training two autoencoders on all possible hexapeptides using their sequence and physicochemical features, respectively. The resulting clusters were used to stratify the data into training and testing datasets. Then, in the first stage, we extracted features from hexapeptides based on their sequence and physicochemical properties. The feature extraction module was used to obtain physicochemical features, while the ESM-2 module was responsible for extracting sequence features for each hexapeptide. Finally, in the second stage, the aggregation prediction module was employed to predict the aggregation potential of hexapeptides. The experimental results demonstrated that the accuracy of TSLAmy reached 0.8493 (0.8447-0.8539), outperforming other state-of-the-art methods. Furthermore, we predicted the aggregation potential of all 64,000,000 possible hexapeptides and analyzed the amino acids that form aggregation-prone hexapeptides. We anticipate that TSLAmy can offer new insights into the identification of aggregation-prone peptides, contributing to advancements in peptide drug development.

Drug-Gene Interaction (DGI) is crucial for drug discovery and personalized medicine. The continuous development of genomics and drug repositioning has brought increasing attention to the complex relations between drugs and genes. However, traditional biological experiments are time-consuming and costly, which makes it challenging to efficiently explore the multi-relational interactions between drugs and genes. Therefore, computational approaches aim to develop efficient schemes for predicting drug-gene relations to reduce the search space and experimental costs. Existing computational methods often suffer from data scarcity and poor generalization, which pose significant challenges for practical applications. To address these issues, we propose a novel multi-relation DGI prediction method based on molecular structure-driving and high-low-order attention denoising framework. Our approach captures molecular structural information through both atom and bond channels with a drug feature encoder. For network structure, we enhance both high- and low-order channels: the low-order channel leverages graph convolutional networks, while the high-order channel employs hypergraph-based message propagation. Additionally, we adopt consistency information loss and inter-channel attention mechanism to refine high- and low-order features. Experimental results on three drug-gene datasets demonstrate the superior performance of our model, particularly on sparse datasets DrugBank and DGIdb, with F1 improvements of 4.06% and 5.67%, respectively.

npj Digital Medicine, Published online: 06 October 2025; doi:10.1038/s41746-025-01978-4

Digital twin models for predicting venetoclax and azacitidine-induced neutropenia in patients with acute myeloid leukemia

Nature, Published online: 08 October 2025; doi:10.1038/s41586-025-09603-w

MetaGraph enables scalable indexing of large sets of DNA, RNA or protein sequences using annotated de Bruijn graphs.

Nature, Published online: 08 October 2025; doi:10.1038/d41586-025-03222-1

Tests of large language models reveal that they can behave in deceptive and potentially harmful ways. What does this mean for the future?

Nature, Published online: 08 October 2025; doi:10.1038/d41586-025-03195-1

Susumu Kitagawa, Richard Robson and Omar Yaghi pioneered the creation of metal–organic frameworks, which can capture and store molecules such as carbon dioxide.

Nature, Published online: 08 October 2025; doi:10.1038/d41586-025-03219-w

MetaGraph compresses vast data archives into a search engine for scientists, opening up new frontiers of biological discovery.

Nature, Published online: 07 October 2025; doi:10.1038/d41586-025-03287-y

Regulatory T cells, which help to dampen inflammation, are being used in clinical trials against ailments such as rheumatoid arthritis.

Nature, Published online: 07 October 2025; doi:10.1038/d41586-025-03196-0

Scientists, research institutions, funders, libraries and publishers must all improve software practices.

Nature, Published online: 06 October 2025; doi:10.1038/d41586-025-03286-z

Meet the winners of this year’s Nobel Prize in Physiology or Medicine. Plus, creative hobbies could slow brain ageing at the molecular level and a visit to a sewage-filtering wetland.

Nature, Published online: 06 October 2025; doi:10.1038/d41586-025-03223-0

Other researchers question whether autonomous AI scientists are possible or even desirable.

Nature, Published online: 06 October 2025; doi:10.1038/d41586-025-03193-3

Mary Brunkow, Fred Ramsdell and Shimon Sakaguchi discovered cells that protect the body from autoimmune diseases.