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Received — 12 January 2026 ⏭ Favourites

Streamlining evidence based clinical recommendations with large language models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dubai Li · Nan Jiang · Kangping Huang · Ruiqi Tu · Shuyu Ouyang · Huayu Yu · Lin Qiao · Chen Yu · Tianshu Zhou · Danyang Tong · Qian Wang · Mengtao Li · Xiaofeng Zeng · Yu Tian · Xinping Tian · Jingsong Li
12 January 2026 at 13:00
arXiv:2505.10282v2 Announce Type: replace-cross Abstract: Clinical evidence underpins informed healthcare decisions, yet integrating it into real-time practice remains challenging due to intensive workloads, complex procedures, and time constraints. This study presents Quicker, an LLM-powered system that automates evidence synthesis and generates clinical recommendations following standard guideline development workflows. Quicker delivers an end-to-end pipeline from clinical questions to recommendations and supports customized decision-making through integrated tools and interactive interfaces. To evaluate how closely Quicker can reproduce guideline development processes, we constructed Q2CRBench-3, a benchmark derived from guideline development records for three diseases. Experiments show that Quicker produces precise question decomposition, expert-aligned retrieval, and near-comprehensive screening. Quicker assistance improved the accuracy of extracted study data, and its recommendations were more comprehensive and coherent than clinician-written ones. In system-level testing, Quicker working with one participant reduced recommendation development to 20-40 min. Overall, the findings demonstrate Quicker's potential to enhance the speed and reliability of evidence-based clinical decision-making.
Received — 9 January 2026 ⏭ Favourites

PsychEval: A Multi-Session and Multi-Therapy Benchmark for High-Realism AI Psychological Counselor

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qianjun Pan · Junyi Wang · Jie Zhou · Yutao Yang · Junsong Li · Kaiyin Xu · Yougen Zhou · Yihan Li · Jingyuan Zhao · Qin Chen · Ningning Zhou · Kai Chen · Liang He
9 January 2026 at 13:00
arXiv:2601.01802v3 Announce Type: replace Abstract: To develop a reliable AI for psychological assessment, we introduce \texttt{PsychEval}, a multi-session, multi-therapy, and highly realistic benchmark designed to address three key challenges: \textbf{1) Can we train a highly realistic AI counselor?} Realistic counseling is a longitudinal task requiring sustained memory and dynamic goal tracking. We propose a multi-session benchmark (spanning 6-10 sessions across three distinct stages) that demands critical capabilities such as memory continuity, adaptive reasoning, and longitudinal planning. The dataset is annotated with extensive professional skills, comprising over 677 meta-skills and 4577 atomic skills. \textbf{2) How to train a multi-therapy AI counselor?} While existing models often focus on a single therapy, complex cases frequently require flexible strategies among various therapies. We construct a diverse dataset covering five therapeutic modalities (Psychodynamic, Behaviorism, CBT, Humanistic Existentialist, and Postmodernist) alongside an integrative therapy with a unified three-stage clinical framework across six core psychological topics. \textbf{3) How to systematically evaluate an AI counselor?} We establish a holistic evaluation framework with 18 therapy-specific and therapy-shared metrics across Client-Level and Counselor-Level dimensions. To support this, we also construct over 2,000 diverse client profiles. Extensive experimental analysis fully validates the superior quality and clinical fidelity of our dataset. Crucially, \texttt{PsychEval} transcends static benchmarking to serve as a high-fidelity reinforcement learning environment that enables the self-evolutionary training of clinically responsible and adaptive AI counselors.

Multi-Modal AI for Remote Patient Monitoring in Cancer Care

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yansong Liu · Ronnie Stafford · Pramit Khetrapal · Huriye Kocadag · Gra\c{c}a Carvalho · Patricia de Winter · Maryam Imran · Amelia Snook · Adamos Hadjivasiliou · D. Vijay Anand · Weining Lin · John Kelly · Yukun Zhou · Ivana Drobnjak
9 January 2026 at 13:00
arXiv:2512.00949v2 Announce Type: replace-cross Abstract: For patients undergoing systemic cancer therapy, the time between clinic visits is full of uncertainties and risks of unmonitored side effects. To bridge this gap in care, we developed and prospectively trialed a multi-modal AI framework for remote patient monitoring (RPM). This system integrates multi-modal data from the HALO-X platform, such as demographics, wearable sensors, daily surveys, and clinical events. Our observational trial is one of the largest of its kind and has collected over 2.1 million data points (6,080 patient-days) of monitoring from 84 patients. We developed and adapted a multi-modal AI model to handle the asynchronous and incomplete nature of real-world RPM data, forecasting a continuous risk of future adverse events. The model achieved an accuracy of 83.9% (AUROC=0.70). Notably, the model identified previous treatments, wellness check-ins, and daily maximum heart rate as key predictive features. A case study demonstrated the model's ability to provide early warnings by outputting escalating risk profiles prior to the event. This work establishes the feasibility of multi-modal AI RPM for cancer care and offers a path toward more proactive patient support.(Accepted at Europe NeurIPS 2025 Multimodal Representation Learning for Healthcare Workshop. Best Paper Poster Award.)
Received — 3 December 2025 ⏭ Favourites

Multi-Modal AI for Remote Patient Monitoring in Cancer Care

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yansong Liu · Ronnie Stafford · Pramit Khetrapal · Huriye Kocadag · Gra\c{c}a Carvalho · Patricia de Winter · Maryam Imran · Amelia Snook · Adamos Hadjivasiliou · D. Vijay Anand · Weining Lin · John Kelly · Yukun Zhou · Ivana Drobnjak
2 December 2025 at 13:00
arXiv:2512.00949v1 Announce Type: cross Abstract: For patients undergoing systemic cancer therapy, the time between clinic visits is full of uncertainties and risks of unmonitored side effects. To bridge this gap in care, we developed and prospectively trialed a multi-modal AI framework for remote patient monitoring (RPM). This system integrates multi-modal data from the HALO-X platform, such as demographics, wearable sensors, daily surveys, and clinical events. Our observational trial is one of the largest of its kind and has collected over 2.1 million data points (6,080 patient-days) of monitoring from 84 patients. We developed and adapted a multi-modal AI model to handle the asynchronous and incomplete nature of real-world RPM data, forecasting a continuous risk of future adverse events. The model achieved an accuracy of 83.9% (AUROC=0.70). Notably, the model identified previous treatments, wellness check-ins, and daily maximum heart rate as key predictive features. A case study demonstrated the model's ability to provide early warnings by outputting escalating risk profiles prior to the event. This work establishes the feasibility of multi-modal AI RPM for cancer care and offers a path toward more proactive patient support.(Accepted at Europe NeurIPS 2025 Multimodal Representation Learning for Healthcare Workshop)
Received — 25 May 2025 ⏭ Favourites

Minimal Residual Disease Detection: Implications for Clinical Diagnosis and Cancer Patient Treatment

✇MRD
By: Meiling Song · Wenjing Pan · Xinjie Yu · Jie Ren · Congli Tang · Zhu Chen · Zhe Wang · Yan Deng · Nongyue He · Hongna Liu · Song Li
19 May 2025 at 18:00

MedComm (2020). 2025 May 15;6(6):e70193. doi: 10.1002/mco2.70193. eCollection 2025 Jun.

ABSTRACT

Minimal residual disease (MRD) serves as a pivotal biomarker for the clinical diagnosis and subsequent treatment of cancer patients. In hematological malignancies, MRD pose an increasingly serious threat to the health of Chinese people. Accurate MRD detection is essential for assessing relapse risk and optimizing therapeutic strategies, yet current methods such as flow cytometry, polymerase chain reaction (PCR), and next-generation sequencing (NGS) each have distinct limitations, and significant gaps remain in achieving optimal sensitivity and specificity of these technologies. This review provides a comprehensive analysis of MRD detection methods, high-lighting their clinical implications, including their roles in treatment decision-making, risk stratification, and patient outcomes. It discusses the strengths and weaknesses of existing techniques and explores emerging technologies that promise enhanced diagnostic precision. Key advancements such as integrating NGS with other methodologies and novel approaches like liquid biopsy and PCR are examined. The review underscores the academic and practical value of early and accurate MRD detection, emphasizing its impact on improving patient management and treatment outcomes. By addressing the limitations of current technologies and exploring future directions, this review aims to advance the field and support personalized medicine approaches to cancer treatment.

PMID:40384986 | PMC:PMC12079024 | DOI:10.1002/mco2.70193

Received — 24 December 2024 ⏭ Favourites

Author Correction: π-HuB: the proteomic navigator of the human body

✇Nature - Issue - nature.com science feeds
By: Fuchu He · Ruedi Aebersold · Mark S. Baker · Xiuwu Bian · Xiaochen Bo · Daniel W. Chan · Cheng Chang · Luonan Chen · Xiangmei Chen · Yu-Ju Chen · Heping Cheng · Ben C. Collins · Fernando Corrales · Jürgen Cox · Weinan E · Jennifer E. Van Eyk · Jia Fan · Pouya Faridi · Daniel Figeys · George Fu Gao · Wen Gao · Zu-Hua Gao · Keisuke Goda · Wilson Wen Bin Goh · Dongfeng Gu · Changjiang Guo · Tiannan Guo · Yuezhong He · Albert J. R. Heck · Henning Hermjakob · Tony Hunter · Narayanan Gopalakrishna Iyer · Ying Jiang · Connie R. Jimenez · Lokesh Joshi · Neil L. Kelleher · Ming Li · Yang Li · Qingsong Lin · Cui Hua Liu · Fan Liu · Guang-Hui Liu · Yansheng Liu · Zhihua Liu · Teck Yew Low · Ben Lu · Matthias Mann · Anming Meng · Robert L. Moritz · Edouard Nice · Guang Ning · Gilbert S. Omenn · Christopher M. Overall · Giuseppe Palmisano · Yaojin Peng · Charles Pineau · Terence Chuen Wai Poon · Anthony W. Purcell · Jie Qiao · Roger R. Reddel · Phillip J. Robinson · Paola Roncada · Chris Sander · Jiahao Sha · Erwei Song · Sanjeeva Srivastava · Aihua Sun · Siu Kwan Sze · Chao Tang · Liuju
23 December 2024 at 08:00

Nature, Published online: 23 December 2024; doi:10.1038/s41586-024-08555-x

Author Correction: π-HuB: the proteomic navigator of the human body

A Novel Urine DNA Predictor for Noninvasive Early Diagnosis and Monitoring Minimal Residual Disease of Upper Tract Urothelial Carcinoma

✇MRD
By: Wei Zuo · Xuanjun Guo · Jilong Zhang · Wei Yu · Yanrui Zhang · Huanqing Cheng · Qi Tang · Xuesong Li · Zhisong He · Liqun Zhou
23 October 2024 at 18:00

Cancer Med. 2024 Oct;13(20):e70346. doi: 10.1002/cam4.70346.

ABSTRACT

BACKGROUND: For early detection and postoperative monitoring of upper tract urothelial carcinoma (UTUC), the traditional detection method was limited to its invasiveness and insufficient sensitivity. We aim to use urine tumour DNA (utDNA) for detecting minimal residual disease (MRD), early diagnosis and perioperative monitoring in UTUC.

METHOD: We previously established a utDNA multidimensional bioinformatic valuation model, named utLIFE, using low-coverage whole-genome sequencing and targeted deep sequencing. This prospective cohort enrolled 93 patients diagnosed with UTUC without metastasis. We collected morning urine samples on the day of surgery and the discharge day after the operation for utLIFE testing. In addition, we also enrolled 80 healthy controls to further validate the specificity of the utLIFE model in the study.

RESULTS: The utLIFE of preoperative samples could discriminate UTUC with high specificity (96.25%, 77/80), and high sensitivity (96.77%, 90/93) regardless of stage and grade. The sensitivity of utLIFE was significantly higher than urine cytology (p < 0.001) and fluorescence in situ hybridisation (FISH) (p < 0.001) (N = 19), especially in early-stage and low-grade UTUC. Postoperative utLIFE scores were significantly decreased compared with those of preoperative samples (79 vs. 36, p < 0.001), indicating its association with tumour burden. For special pathology types, utLIFE performed less well in sensitivity and perioperative alteration.

CONCLUSION: In conclusion, we established a bioinformatic utDNA valuation model, utLIFE, which was validated to be a rapid and noninvasive approach with high sensitivity for early detection and MRD monitoring for UTUC.

PMID:39440792 | PMC:PMC11497171 | DOI:10.1002/cam4.70346

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