Artificial Intelligence in the Life Sciences

Long COVID affects millions of people worldwide, yet no disease-modifying treatment has been approved, and existing interventions have shown only modest and inconsistent benefits. A key reason for this limited progress is that current computational drug repurposing pipelines do not match well with the clinical reality of Long COVID. These patients often have persistent, multisystemic symptoms and may already be taking multiple medications, making treatment safety a primary concern. However, most repurposing workflows still treat safety as a downstream filter and rely on disease-associated targets rather than causal drivers. They also assume that the findings of one analysis would generalize across the diverse presentations of Long COVID. We introduce SPLIT, a safety-first repurposing framework that addresses these limitations. SPLIT prioritizes safety at the start of the candidate evaluation, integrates complementary causal inference strategies to identify likely driver genes, and uses a counterfactual substitution design to compare drugs within specific cohort contexts. When applied to cognitive and respiratory Long COVID cohorts, SPLIT revealed three main findings. First, drugs with similar predicted efficacy could have very different predicted safety profiles. Second, the drugs flagged as unfavorable were often different between the two cohorts, showing that drug prioritization is phenotype-specific. Third, SPLIT flagged 18 drugs currently under active investigation in Long COVID trials as having unfavorable predicted profiles. SPLIT provides a practical framework to identify safer, more context-appropriate candidates earlier in the process, supporting more targeted and better-tolerated treatment strategies for Long COVID.

The apolipoprotein E {varepsilon}4 (APOE4) allele is the strongest genetic risk factor for late-onset Alzheimer's disease (AD), the most common form of dementia. APOE4 carriers exhibit cerebrovascular and metabolic dysfunction, structural brain alterations, and gut microbiome changes decades before the onset of clinical symptoms. A better understanding of the early manifestation of these physiological changes is critical for the development of timely AD interventions and risk reduction protocols. Multimodal datasets encompassing a wide range of APOE4- and AD-associated biomarkers provide a valuable opportunity to gain insight into the APOE4 phenotype; however, these datasets often present analytical challenges due to small sample sizes and high heterogeneity. Here, we propose a two-stage multimodal AI model (APOEFormer) that integrates blood metabolites, brain vascular and structural MRI, microbiome profiles, and other clinical and demographic data to predict APOE4 allele status. In the first stage, modality-specific encoders are used to generate initial representations of input data modalities, which are aligned in a shared latent space via self-supervised contrastive learning during pretraining. This objective encourages the learning of informative and consistent representations across modalities by leveraging cross-modality relationships. In the second stage, the pretrained representations are used as inputs to a multimodal transformer that integrates information across modalities to predict a key AD risk genetic variant (APOE4). Across 10 independent experimental runs with different train-validation-test splits, APOEFormer predicts whether an individual carries an APOE4 allele with an average accuracy of 75%, demonstrating robust performance under limited sample sizes. Post hoc perturbation analysis of the predictive model revealed valuable insights into the driving components of the APOE4 phenotype, including key blood biomarkers and brain regions strongly associated with APOE4.

Manuscript preparation is a critical bottleneck in scientific publishing, yet existing AI writing tools require cloud transmission of sensitive content, creating data-confidentiality barriers for clinical researchers. We introduce the Paper Analysis Tool (PAT), a free, multi-agent framework that deploys 31 specialized agents powered by small language models (SLMs) to audit manuscripts across multiple quality dimensions without external data transmission. Applied to three published clinical neurological papers, PAT generated 540 evaluable suggestions. Validation by two expert reviewers (R.B., A.G.) confirmed 391 actionable, high-value revisions (90% agreement), achieving a 72.4% overall usefulness accuracy spanning methodological, statistical, and visual domains. Furthermore, deterministic re-evaluation of 126 agent-suggested rewrite pairs using Phase 0 metrics confirmed text improvement: total word count decreased by 25%, passive voice prevalence dropped sharply from 35% to 5%, average sentence length decreased by 24%, long-sentence fraction fell by 67%, and the Flesch-Kincaid grade improved by 17% . Our validation confirms that systematic, agent-driven pre-submission review drives measurable improvements, successfully converting manuscript optimization from an opaque, manual endeavor into a transparent and rigorous scientific process. Manuscript preparation is a critical bottleneck in scientific publishing, yet existing AI writing tools require cloud transmission of sensitive content, creating data-confidentiality barriers for clinical researchers. We introduce the Paper Analysis Tool (PAT), a free, multi-agent framework that deploys 31 specialized agents powered by small language models (SLMs) to audit manuscripts across multiple quality dimensions without external data transmission. Applied to three published clinical neurological papers, PAT generated 540 evaluable suggestions. Independent validation by two expert reviewers (R.B., A.G.) confirmed 391 actionable, high-value revisions (90% agreement), achieving a 72.4% overall usefulness accuracy spanning methodological, statistical, and visual domains. Furthermore, deterministic re-evaluation of 126 suggested Phase 0 rewrite pairs confirmed text improvement: total word count decreased by 25%, passive voice prevalence dropped sharply from 35% to 5%, average sentence length decreased by 24%, and long-sentence fraction fell by 67%, and the Flesch-Kincaid grade improved modestly. Our validation confirms that systematic, agent-driven pre-submission review drives measurable improvements, successfully converting manuscript optimization from an opaque, manual endeavor into a transparent and rigorous scientific process.

Background: Pharmacogenomic testing (PGx) can optimise drug efficacy and minimise toxicity, but the extent of prescriber adherence to PGx recommendations remains unclear. We aimed to quantify clinician adherence to international genotype-guided prescribing recommendations in a cohort of paediatric oncology patients. Methods: We reviewed files of children enrolled in the MARVEL-PIC (NCT05667766) randomised control trial, who had PGx recommendations available. Patients were included if 12 weeks had passed since their PGx report was released to clinicians. Prescribing events were identified for actionable PGx recommendations, and classified as "explicitly followed", "inadvertently followed", or "not followed". Adherence was assessed by patient, drug, and recommendation. Results: 2,063 PGx recommendations were available for 216 patients. 64 (3.1%) recommendations were actionable for 44 patients and 10 drugs within the 12-week study period. Recommendations were explicitly followed in 57/288 (19.8%) of prescribing events, inadvertently followed in 145 (50.3%), and not followed in 86 (29.9%). Mercaptopurine demonstrated the highest rate of explicit adherence (87.5%). No significant associations were observed between adherence and age group, cancer type, drug type, or strength of recommendation. Conclusion: Adherence to pharmacogenomic recommendations was very low, highlighting the need to understand barriers to PGx implementation, and consideration of clinical decision supports to facilitate adherence.

Inadequate discharge communication is a well-documented contributor to medication non-adherence, missed follow-ups, and preventable readmissions across healthcare systems worldwide. In resource-limited oncology settings, where patients are often low-literate, speak non-dominant languages, and manage complex multi-drug regimens, this problem is acute and largely unaddressed. We present Aakhyan, a vernacular patient communication platform that addresses the full post-discharge arc: from converting English-language discharge summaries into structured, voice-based vernacular explanations, through medication adherence support, to proactive follow-up management - all delivered via WhatsApp. The architecture is novel in its strict separation of concerns: a vision-language model performs structured JSON extraction from discharge images; all patient-facing content is generated deterministically from clinician-approved templates with community-sensitive vocabulary registers. This design eliminates the hallucination risk inherent in generative AI patient communication (documented at 18-82% in prior studies) while preserving the extraction capability of large language models. The platform supports four language registers, Bengali, Hindi, simplified English for tribal populations, and Assamese, with text-to-speech synthesis across all registers, including a custom grapheme-to-phoneme engine developed for Assamese phonology. Beyond discharge communication, the platform includes scheduled medication adherence nudges, interactive follow-up reminders, and a Daily Availability and Patient Notification System (DAPNS) that notifies patients the evening before their follow-up whether their doctor and required investigations are available, preventing wasted trips by rural patients who travel 2-6 hours to reach the centre. A 100-patient stratified randomised controlled study is planned at Silchar Cancer Centre, with structured teach-back assessment at 48-72 hours post-discharge as the primary comprehension outcome and preliminary clinical efficacy as a secondary objective. This paper describes the clinical rationale, technical architecture, safety framework, and positioning of Aakhyan within the existing literature on mHealth patient communication interventions.

Parkinsons disease (PD) exhibits well-established sex differences in prevalence and clinical phenotypes, yet the underlying molecular mechanisms remain largely elusive. Here, we conducted a comprehensive sex-stratified multi-omic integration to identify sex-specific causal proteins and biological pathways in PD. We performed gene-based association analysis, transcriptome-wide association studies (TWAS), and proteome-wide Mendelian randomization (PWMR) with colocalization analysis using GWAS summary statistics from the International PD Genetics Consortium (IPDGC; 12,054 male cases/11,999 controls; 7,384 female cases/12,389 controls) for sex-stratified analyses and Global Parkinsons Genetics Program (GP2; 34,933 cases/31,009 controls) for sex-combined analyses. Prioritized candidates were further evaluated through MR with brain expression quantitative trait loci (eQTLs) from MetaBrain and differential protein abundance analysis using the Global Neurodegeneration Proteomics Consortium (GNPC; 704 PD cases/5,629 controls in plasma; 78 cases/1,411 controls in cerebrospinal fluid). Additionally, pathway enrichment analysis was performed for prioritized molecules. Integration across three analytical layers prioritized 102 molecular candidates across 31 unique loci, significant from multiple analyses. Of these, eleven genes reached significance across all three layers, including SNCA, MAPT, and CTSB significant in both sexes; CD160, GPNMB, and LRRC37A2 as male-predominant; STX4 and PRSS53 as female-predominant; and BST1, SCARB2, and LGALS3 significant only in sex-combined analysis. In males, CD160 emerged as a novel candidate with convergent evidence across all three analyses and colocalization, while L3MBTL2 was identified as a novel risk gene from gene-based association and TWAS analyses. In females, STX4 and PRSS53 at the 16p11.2 locus showed female-predominant associations. Pathway enrichment analysis revealed innate immune and SUMOylation pathways in males, with CD160 and L3MBTL2 as key contributors respectively, contrasting with WDR5-mediated chromatin remodeling in females. Brain eQTL-based MR confirmed significant associations for 69 of 86 testable candidates (80.2%) in at least one tissue. Protein abundance analysis confirmed sex-specific patterns, and several candidates showed discordant directions between genetically predicted causal effects and observed protein abundance -- including male-specific plasma elevation of CD160 and female-specific patterns for STX4 -- underscoring the distinction between causal risk mechanisms and disease-state molecular changes. These findings demonstrate that PD is a molecularly heterogeneous disorder with sexually dimorphic pathogenic drivers. While shared axes such as lysosomal dysfunction and vesicle trafficking disruption exist, the divergence into male-specific immune dysregulation and female-specific chromatin remodeling suggests that the primary triggers of neurodegeneration differ by sex. Our results underscore the necessity of sex-stratified approaches in biomarker discovery and the development of precision therapeutic strategies for PD.

Background: Generative artificial intelligence (GenAI) chatbots have shown utility in assisting with various research tasks. Traditional, complementary, and integrative medicine (TCIM) is a patient-centric approach that emphasizes holistic well-being. The integration of TCIM and GenAI presents numerous key opportunities. However, TCIM researchers' attitudes toward GenAI tools remain less understood. This large-scale, international cross-sectional survey aimed to elucidate the attitudes and perceptions of TCIM researchers regarding the use of GenAI chatbots in the scientific process. Methods: A search strategy in Ovid MEDLINE identified corresponding authors who were TCIM researchers. Eligible authors were invited to complete an anonymous online survey administered via SurveyMonkey. The survey included questions on socio-demographic characteristics, familiarity with GenAI chatbots, and perceived benefits and challenges of using GenAI chatbots. Results were analysed using descriptive statistics and thematic content analysis. Results: The survey received 716 responses. Most respondents reported familiarity with GenAI chatbots (58.08%) and viewed them as very important to the future of scientific research (54.37%). The most acknowledged benefits included workload reduction (74.07%) and increased efficiency in data analysis/experimentation (71.14%). The most frequently reported challenges involved bias, errors, and limitations. More than half of the respondents (57.02%) expressed a need for training to use GenAI chatbots in the scientific process, alongside an interest in receiving training (72.07%). However, 43.67% indicated that their institutions did not offer these programs. Discussion: By developing a deeper understanding of TCIM researchers' perspectives, future AI applications in this field can be more informed, and guide future policies and collaboration among researchers.

Medical concept extraction from electronic health records underpins many downstream applications, yet remains challenging because medically meaningful concepts, such as diagnoses, are frequently implied rather than explicitly stated in medical narratives. Existing benchmarks with human-annotated evidence spans underscore the importance of grounding extracted concepts in medical text. However, they predominantly focus on explicitly stated concepts and provide limited coverage of cases in which medically relevant concepts must be inferred. We present MedicalBench, a new benchmark for medical concept extraction with evidence grounding that evaluates implicit medical reasoning. MedicalBench formulates medical concept extraction as a verification task over medical note concept pairs, coupled with sentence level evidence identification. Built from MIMIC-IV discharge summaries and human verified ICD-10 codes, the dataset is curated through a multi stage large language model (LLM) triage pipeline followed by medical annotation and expert review. It deliberately includes implicit positives, semantically confusable negatives, and cases where LLM judgments disagree with medical expert assessments. Annotators provide sentence level evidence spans and concise medical rationales. The final dataset contains 823 high quality examples. We define two complementary evaluation tasks: (1) medical concept extraction and (2) sentence level evidence retrieval, enabling assessment of both correctness and interpretability. Benchmarking state-of-the-art LLMs and a supervised baseline reveals that performance remains modest, highlighting the difficulty of extracting implicitly expressed concepts. We further show that explicitly incorporating reasoning cues and prompting to extract implicit evidence substantially improves medical concept extractions, while performance is largely invariant to note length, indicating that MedicalBench isolates reasoning difficulty rather than superficial confounders. MedicalBench provides the first systematic benchmark for implicit, evidence-grounded medical concept extraction, offering a foundation for developing medical language models that can both identify medically relevant concepts and justify their predictions in a transparent and medically faithful manner.

Patients who participate in intracranial neuroscience research make invaluable contributions to our understanding of the brain, accelerating the development of neurotechnological interventions. Engagement of patients as part of this research presents unique challenges, where study goals can be distant from immediate clinical applications and require specialized domain knowledge. Yet methods for meaningfully integrating patient communities as part of these research efforts is essential, as intracranial neuroscience guides the application of artificial intelligence for understanding and enhancing human cognition. In order to identify what patients consider meaningful research engagement we interviewed individuals who participated in a study during their Deep Brain Stimulation (DBS) surgery and attended a group event where they interacted with our research team. Analysis of semi-structured interviews identified four main themes: interest in science and the future of clinical care, contributing to science to improve lives, connecting with others, and accessibility considerations. Based on these insights, we propose strategies for transformational participation of patient communities in intracranial neuroscience research with respect to engagement objectives, communication and scope. This approach offers a foundation for sustaining relationships between scientists and communities rooted in trust and transparency, to ensure that impacts of neurotechnology on human health and cognition are aligned with patient needs as well as desired public values.

BackgroundSynthetic data have emerged as a complementary strategy for secondary use of clinical registries, enabling data sharing without patient-level exposure. In spine surgery, multicenter data sharing is constrained by institutional governance and patient privacy regulations. Validated synthetic data generation may enable broader access to surgical outcomes data for artificial intelligence development without compromising patient confidentiality. ObjectiveTo describe and benchmark a three-domain validated synthetic data pipeline applied to a multicenter, tokenized spine surgery registry (SpineBase), and to establish a reproducible certification framework for synthetic spine surgery datasets. MethodsWe extracted 125 sacroiliac joint fusion cases from the SpineBase registry (SIBONE study, IRB-SOFCOT approval Ref. 14-2025; CNIL MR-004 Ref. 2234503 v 0). A GaussianCopula generative model was trained on 52 structured variables spanning demographics, preoperative assessments, operative details, and longitudinal outcomes at 3, 6, 12, and 24 months. Synthetic datasets of 100, 1,000, and 10,000 patients were generated. Validation followed a three-domain framework: (1) fidelity, assessed by Kolmogorov-Smirnov tests and Jensen-Shannon divergence; (2) utility, assessed by train-on-synthetic, test-on-real (TSTR) methodology; and (3) privacy, assessed by nearest-neighbor distance ratio (NNDR), membership inference attack, and k-anonymity proxy. ResultsAll three validation gates passed. Fidelity: mean KS p-value 0.52 (threshold >0.05). Privacy: NNDR >1.0 in 98.9% of synthetic records; membership inference AUROC 0.57. Utility: 12-month Oswestry Disability Index prediction yielded Pearson r = 0.29, consistent with expected attenuation at N = 125. A SHA-256 cryptographic hash of each certified dataset was anchored on the Solana blockchain for immutable provenance. ConclusionsA validated, blockchain-anchored synthetic data pipeline for spine surgery registries is technically feasible and meets current publication-standard criteria for fidelity and privacy. Utility metrics scale with registry size, creating a direct incentive for multicenter data contribution. This framework provides a reproducible methodology for synthetic data certification in spine surgery research, and establishes certified synthetic datasets as a privacy-native substrate for expert-annotation pipelines -- as demonstrated in the companion Spine Reviews study.

Accurate recognition and deidentification of sensitive health information (SHI) in spoken dialogues requires multimodal algorithms that can understand medical language and contextual nuance. However, the recognition and deidentification risks expose sensitive health information (SHI). Additionally, the variability and complexity of medical terminology, along with the inherent biases in medical datasets, further complicate this task. This study introduces the SREDH/AI-Cup 2025 Medical Speech Sensitive Information Recognition Challenge, which focuses on two tasks: Task-1: Speech transcription systems must accurately transcribe speech into text; and Task-2: Medical speech de-identification to detect and appropriately classify mentions of SHI. The competition attracted 246 teams; top-performing systems achieved a mixed error rate (MER) of 0.1147 and a macro F1-score of 0.7103, with average MER and macro F1-score of 0.3539 and 0.2696, respectively. Results were presented at the IW-DMRN workshop in 2025. Notably, the results reveal that LLMs were prevalent across both tasks: 97.5% of teams adopted LLMs for Task 1 and 100% for Task 2. Highlighting their growing role in healthcare. Furthermore, we finetuned six models, demonstrating strong precision ([~]0.885-0.889) with slightly lower recall ([~]0.830-0.847), resulting in F1-scores of 0.857-0.867.

Background. Federated learning (FL) enables collaborative model training across institutions without sharing patient-level data. However, standard FL algorithms such as FedAvg degrade under non-independently and non-identically distributed (non-IID) data, a prevalent condition when patient demographics, scanner hardware, and disease prevalence differ across hospital sites. Objective. We propose iPS-MFFL (Individualized Per-Site Meta-Federated Feature Learning), a federated framework with a hierarchical local-model architecture that addresses non-IID heterogeneity through (1) a shared feature extractor, (2) multiple weak-learner classification heads that can be trained with heterogeneous training objectives to promote complementary decision boundaries, (3) independent per-learner server aggregation so that each weak learner's parameters are averaged only with its counterparts at other clients, and (4) a lightweight meta-model, itself federated, that adaptively stacks the weak-learner outputs. Methods. We evaluate on the Brain Tumor MRI Classification dataset (7,200 images; 4 classes: glioma, meningioma, pituitary tumor, no tumor) partitioned across K = 5 simulated hospital sites using Dirichlet non-IID sampling (alpha = 0.3). Four baselines are compared: Local-only training, FedAvg, FedProx, and Freeze-FT. All experiments are repeated over three random seeds (13, 42, 2025) and evaluated using paired t-tests, Cohen's d effect sizes, and post-hoc power analysis.

In this study, we investigate gender bias in a Retrieval-Augmented Generation (RAG) based AI assistant developed for Finnish wellbeing services counties. We tested the system using 36 clinically relevant queries, each rendered in three gendered variants (male, female, gender-neutral), and evaluated responses using both an LLM-as-a-judge approach and a human expert panel consisting of a physician and a sociologist specializing in ethics. We observed substantial and clinically significant differences across gendered variants, including differential treatment urgency, inappropriate symptom associations, and misidentification of clinical context. Female variants disproportionately framed responses around childcare and reproductive health regardless of clinical relevance, reflecting societal stereotypes rather than medical reasoning. Bias manifested both at the LLM generation stage and the RAG retrieval stage, in several cases causing the model to hallucinate responses entirely. Some bias patterns were persistent across repeated runs, while others appeared inconsistently, highlighting the challenge of distinguishing systematic bias from stochastic variation.

Background. Adult diffuse glioma is a representative class of primary brain tumors for which accurate MRI-based tumor segmentation is indispensable for treatment planning. Conventional automated segmentation methods have relied primarily on image information and spatial prompts, and auxiliary clinical information that is routinely acquired in clinical practice has not been sufficiently exploited as an input. Objective. Building on a dual-prompt-driven Segment Anything Model (SAM) extension framework that fuses visual and language reference prompts, we propose a method that integrates patient demographics, unsupervised molecular cluster variables derived from TCGA high-throughput profiling, and histopathological parameters as learnable prompt embeddings, and we evaluate its effect on the accuracy of lower-grade glioma (LGG) MRI segmentation. Methods. An auxiliary prompt encoder converts clinical metadata into high-dimensional embeddings that are fused with the prompt representations of Segment Anything Model (SAM) ViT-B through a cross-attention fusion mechanism. The TCGA-LGG MRI Segmentation dataset (Kaggle release by Buda et al.; n = 110 patients; WHO grade II-III) was split at the patient level (train/val/test = 71/17/22) using three different random seeds, and the three slices with the largest tumor area were extracted from each patient. To avoid pseudo-replication arising from multiple slices per patient and repeated measurements across seeds, our primary analysis aggregated Dice and 95th-percentile Hausdorff distance (HD95) to the patient x seed unit (n = 66); secondary analyses at the unique-patient level (n = 22) and at the per-slice level (n = 198) are also reported. Pairwise comparisons used paired t-tests with Bonferroni correction (k = 3) and Wilcoxon signed-rank tests, and a permutation test (K = 30) served as an auxiliary check of effective use of the auxiliary information. Results. At the patient x seed level (n = 66), Proposed (full clinical) achieved a Dice gain of +0.287 over the zero-shot SAM ViT-B baseline (paired-t p = 4.2 x 10^-15, Cohen's d_z = +1.25, Bonferroni-corrected p << 0.001; Wilcoxon p = 2.0 x 10^-10), and HD95 improved from 218.2 to 64.6. Because zero-shot SAM is not designed for domain-specific medical segmentation, the large absolute HD95 gap largely reflects the expected domain gap rather than a competitive baseline. The additional contribution of the full clinical configuration over the demographics-only configuration was Dice = +0.023 (paired-t p = 0.057, Bonferroni-corrected p = 0.172), which did not reach statistical significance at the patient level and is reported as a directional trend. The permutation test (K = 30, seed 2025) yielded real-metadata Dice = 0.819 versus a shuffled-metadata mean of 0.773, giving an empirical p = 0.032 = 1/(K + 1), which is at the resolution limit of this test and should therefore be interpreted as preliminary evidence. Conclusions. Integrating auxiliary clinical information as multimodal prompts produced a large improvement over the zero-shot SAM baseline on this LGG cohort. More importantly, a robustness analysis showed that Proposed (full clinical) outperformed the trained Base (no auxiliary information) under all tested spatial-prompt conditions, including perfect centroid (+0.014), and that the advantage was most pronounced in the prompt-free regime (+0.231, p = 0.039), where the base model collapsed but the proposed model maintained meaningful segmentation by leveraging clinical metadata alone. The additional contribution of molecular and histopathological information beyond demographics was not statistically resolved at the patient level (+0.023, n.s.). Establishing clinical utility will require external validation on larger multi-center cohorts and direct comparisons with established segmentation methods. Keywords: brain tumor segmentation; Segment Anything Model (SAM); vision-language prompt-driven segmentation; auxiliary clinical prompts; multimodal learning; TCGA-LGG; deep learning

Background: Artificial intelligence (AI) has the potential to improve the efficiency of evidence synthesis and reduce human error. However, robust methods for evaluating rapidly evolving AI tools within the practical workflows of evidence synthesis remain underdeveloped. This protocol describes a study design for assessing the effectiveness, efficiency, and usability of AI tools in comparison to traditional human-only workflows in the context of Cochrane systematic reviews. Methods: Members of the Cochrane Evaluation of (Semi-) Automated Review (CESAR) Methods Project developed an adaptive platform study-within-a-review (SWAR) design, modeled after clinical platform trials. This design employs a master protocol to concurrently evaluate multiple AI tools (interventions) against a standard human-only process (control) across three key review tasks: title and abstract screening, full-text screening, and data extraction. The adaptive framework allows for the addition or removal of AI tools based on interim performance analyses without necessitating a restart of the study. Performance will be assessed using metrics such as accuracy (sensitivity, specificity, precision), efficiency (time on task), response stability, impact of errors, and usability, in alignment with Responsible use of AI in evidence SynthEsis (RAISE) principles. Results: The study will generate comparative data about the performance and usability of specific AI tools employed in a semi- or fully automated manner relative to standard human effort. The protocol provides a flexible framework for the assessment of AI tools in evidence synthesis, addressing the limitations of static, one-time evaluations. Discussion: This study protocol presents a novel methodological approach to addressing the challenges of evaluating AI tools for evidence syntheses. By validating entire workflows rather than individual technologies, the findings will establish an evidence base for determining the viability of integrating AI into evidence-synthesis workflows. The adaptive design of this study is flexible and can be adopted by other investigators, ensuring that the evaluation framework remains relevant as new tools emerge.

Severe mental disorders (SMDs), including bipolar disorder, schizophrenia, and major depressive disorder, are highly complex conditions associated with a substantial clinical burden and an increased suicide risk. Here, we present the Madrid Manic Cohort (MadManic), a large-scale initiative from Spain designed to integrate genomic, multi-omics, clinical, and digital phenotyping data to investigate the biological basis and clinical heterogeneity of SMDs. The cohort is still expanding and currently includes over 4,400 participants (~2,300 psychiatric patients and ~2,100 controls) and >11,000 biospecimens. Genotyping, transcriptomic and epigenetic data are available for different subsets of the cohort. By establishing the MadManic cohort we aim to integrate molecular data with detailed clinical and longitudinal digital information, allowing a more precise characterization of patient subgroups based on biological and phenotypic profiles. The MadManic cohort is well positioned to contribute to major international efforts in psychiatric genetics by enhancing the representation of Southern European populations, and advancing the identification of genetic risk, clinical predictors, and pharmacogenomic markers of treatment response. This cohort represents a valuable resource for advancing precision psychiatry, with the potential to improve risk prediction and guide personalized interventions in SMDs.

Target trial emulation (TTE) enables causal inference from observational data but remains bottlenecked by manual, expert-dependent protocol operationalization. While large language models (LLMs) have advanced clinical knowledge extraction and code generation, their ability to automate end-to-end TTE workflows remains largely unexplored. We present an LLM-driven framework using retrieval-augmented generation to extract the five core TTE design parameters from the Carotid Revascularization and Medical Management for Asymptomatic Carotid Stenosis Trial (CREST-2) protocol and generate executable phenotyping pipelines for real-world EHR data. The performance of the framework was evaluated along two dimensions. First, protocol extraction accuracy was assessed against a gold-standard checklist of trial design components using precision, recall, and F1-score metrics. Second, outcome validity was evaluated through population-level concordance analyses comparing EHR-derived outcomes with published trial endpoints using standardized mean difference, observed-to-expected ratios, confidence interval overlap, and two-proportion z-tests. Further, Human-in-the-loop validation assessed the correctness of extracted clinical logic and phenotype definitions. Together, these evaluations demonstrate a structured approach for assessing LLM-driven protocol-to-pipeline translation for scalable real-world evidence generation.

AI documentation frameworks remain poorly designed for point-of-care use, leaving clinicians without actionable information on how to use clinical AI models when they need it most. We developed the Clinician Model Card, an interactive, clinician-centered documentation tool, and evaluated it in a sequential exploratory mixed-methods study: interviews with 12 physicians informed iterative co-design, evaluated in a national survey of 129 physicians across Germany. The tool was well-received: 84% agreed it should be routinely available, and 66% considered its content relevant to clinical decision-making. Yet comprehensibility of statistical performance metrics remained poor despite targeted interventions: only 32% understood the Validation & Performance section well, and fewer than 54% correctly interpreted AUROC or PPV, with AI literacy as strong predictor of comprehension. We propose empirically derived design principles for clinician-centered AI documentation. Effective AI transparency requires not only clinician-friendly design and workflow integration, but sustained investment in AI literacy.

BackgroundSystematic reviews are essential for evidence-based decision making in health sciences but require substantial time and resource for manual processes, particularly title and abstract screening. Recent advances in machine learning and large language models (LLMs) have demonstrated promise in accelerating screening with high recall but are often limited by modest gains in efficiency, mostly due to the absence of a generalisable stopping criterion. Here, we introduce and report preliminary findings on the performance of a novel semi-automated active learning system, JARVIS, that integrates LLM-based reasoning using the PICOS framework, neural networks-based classification, and human decision-making to facilitate abstract screening. MethodsDatasets containing author-made inclusion and exclusion decisions from six published systematic reviews were used to pilot the semi-automated screening system. Model performance was evaluated across recall, specificity and area under the curve precision-recall (AUC-PR), using full-text inclusion as the ground truth. Estimated workload and financial savings were calculated by comparing total screening time and reviewer costs across manual and semi-automated scenarios. ResultsAcross the six review datasets, recall ranged between 98.2% and 100%, and specificity ranged between 97.9% and 99.2% at the defined stopping point. Across iterations, AUC-PR values ranged between 83.8% and 100%. Compared with human-only screening, JARVIS delivered workload savings between 71.0% and 93.6%. When a single reviewer read the excluded records, workload savings ranged between 35.6 % and 46.8%. ConclusionThe proposed semi-automated system substantially reduced reviewer workload while maintaining high recall, improving on previously reported approaches. Further validation in larger and more varied reviews, as well as prospective testing, is warranted.

Study Design: Prospective observational study using a novel digital ledger technology (DLT)-based crowdsourcing platform. Objective: To develop and evaluate Spine Reviews, a blockchain-based platform for aggregating spine treatment recommendations from an international specialist panel, and to validate the clinical coherence of the resulting dataset. Summary of Background Data: Predictive models for low back pain treatment are limited by small, homogeneous datasets that fail to capture inter-clinician variability. Traditional multi-center data collection is expensive, slow, and geographically constrained. DLT-based crowdsourcing with cryptographic credentialing may overcome these barriers. Methods: Five hundred synthetic patient vignettes (digital twins) were generated; 463 retained after quality control. A review platform was built on the Solana blockchain using non-transferable Soulbound Tokens (SBTs) for credentialing and smart-contract compensation. Fifty-two specialists from 7 countries provided 4+ reviews per vignette across four treatment tiers, without access to imaging or physical examination. Mixed-effects regression with reviewer random intercepts partitioned decision variability. Results: The platform collected 2,066 completed reviews (97.7%) over 37 days at USD 0.97/review. Variance decomposition revealed that 36.7% of treatment tier variability was attributable to patient presentation, 19.2% to reviewer practice style, and 44.1% to their interaction. Neurological deficits (beta=0.39), symptom duration (beta=0.12), and pain (beta=0.09) independently predicted treatment escalation (all p<0.001). Gwet's AC1 was almost perfect for emergency (0.92) and substantial for conservative decisions (0.67). Reviewer confidence in treatment recommendations decreased with escalating tier severity (conservative 4.59/5 vs surgical 4.05/5), suggesting appropriate uncertainty calibration. Conclusions: DLT with SBT credentialing enables rapid, global, cost-effective aggregation of clinically coherent expert judgment. The three-component variance structure quantifies clinical equipoise in spine care and establishes that predictive models require diverse, multi-reviewer training data. Keywords: digital ledger technology; blockchain; crowdsourcing; clinical decision-making; low back pain; Soulbound Tokens