Hasil untuk "Arctic medicine. Tropical medicine"

Jihoon Jeong

Model Medicine is the science of understanding, diagnosing, treating, and preventing disorders in AI models, grounded in the principle that AI models -- like biological organisms -- have internal structures, dynamic processes, heritable traits, observable symptoms, classifiable conditions, and treatable states. This paper introduces Model Medicine as a research program, bridging the gap between current AI interpretability research (anatomical observation) and the systematic clinical practice that complex AI systems increasingly require. We present five contributions: (1) a discipline taxonomy organizing 15 subdisciplines across four divisions -- Basic Model Sciences, Clinical Model Sciences, Model Public Health, and Model Architectural Medicine; (2) the Four Shell Model (v3.3), a behavioral genetics framework empirically grounded in 720 agents and 24,923 decisions from the Agora-12 program, explaining how model behavior emerges from Core--Shell interaction; (3) Neural MRI (Model Resonance Imaging), a working open-source diagnostic tool mapping five medical neuroimaging modalities to AI interpretability techniques, validated through four clinical cases demonstrating imaging, comparison, localization, and predictive capability; (4) a five-layer diagnostic framework for comprehensive model assessment; and (5) clinical model sciences including the Model Temperament Index for behavioral profiling, Model Semiology for symptom description, and M-CARE for standardized case reporting. We additionally propose the Layered Core Hypothesis -- a biologically-inspired three-layer parameter architecture -- and a therapeutic framework connecting diagnosis to treatment.

Peter S Andrus, Claire J Standley, J Russell Stothard et al.

Lake Victoria is a well-known hot spot for intestinal schistosomiasis, caused by infection with the trematode Schistosoma mansoni. The snail intermediate hosts of this parasite are Biomphalaria snails, with Biomphalaria choanomphala being the predominant intermediate host within Lake Victoria. The prevalence of S. mansoni infection within snail populations is influenced by both biotic and abiotic factors, including the physical and chemical characteristics of their environment, the incidence of infection in human populations (and reservoir hosts) and the level of genetic compatibility between the parasite and the host. Using molecular xenomonitoring, we measured the prevalence of S. mansoni infection within B. choanomphala populations along the Kenyan, Tanzanian and Ugandan shorelines of Lake Victoria and related this to the abiotic (habitat type, water depth, turbulence, temperature, conductivity, total dissolved solids, salinity, pH level) and biotic (B. choanomphala abundance, genetic diversity of host snail populations) factors of the lake. The overall mean prevalence of S. mansoni infection at Lake Victoria was 9.3%, with the highest prevalence of infection occurring on the Tanzanian shoreline (13.1%), followed by the Ugandan (8.2%) and Kenyan (4.7%) shorelines. There was a significant difference in B. choanomphala abundance, water temperature, conductivity, salinity, total dissolved solids and major anion/cation concentrations between the Kenyan, Tanzanian and Ugandan shorelines of Lake Victoria. A Spearman's rank analysis found that the prevalence of S. mansoni infection had a significant, positive relationship with higher levels of B. choanomphala abundance, water acidity, and cation (Ca2+, Mg2+) concentrations. Additionally, we observed that sites with S. mansoni infection correlated with B. choanomphala populations with a higher mean haplotype diversity score compared to sites found without infection, though there was no significant relationship between the prevalence of infection and B. choanomphala haplotype diversity scores. Although our analysis is based upon an archival and unique collection of Biomphalaria snails, the abiotic and biotic relationships uncovered are useful for eco-epidemiological comparisons of intestinal schistosomiasis across Lake Victoria in future.

Periklis Petridis, Georgios Margaritis, Vasiliki Stoumpou et al.

With the increasing interest in deploying Artificial Intelligence in medicine, we previously introduced HAIM (Holistic AI in Medicine), a framework that fuses multimodal data to solve downstream clinical tasks. However, HAIM uses data in a task-agnostic manner and lacks explainability. To address these limitations, we introduce xHAIM (Explainable HAIM), a novel framework leveraging Generative AI to enhance both prediction and explainability through four structured steps: (1) automatically identifying task-relevant patient data across modalities, (2) generating comprehensive patient summaries, (3) using these summaries for improved predictive modeling, and (4) providing clinical explanations by linking predictions to patient-specific medical knowledge. Evaluated on the HAIM-MIMIC-MM dataset, xHAIM improves average AUC from 79.9% to 90.3% across chest pathology and operative tasks. Importantly, xHAIM transforms AI from a black-box predictor into an explainable decision support system, enabling clinicians to interactively trace predictions back to relevant patient data, bridging AI advancements with clinical utility.

Robert A. Rigby, Mikis D. Stasinopoulos, Achim Zeileis et al.

We read with interest the above article by Zavorsky (2025, Respiratory Medicine, doi:10.1016/j.rmed.2024.107836) concerning reference equations for pulmonary function testing. The author compares a Generalized Additive Model for Location, Scale, and Shape (GAMLSS), which is the standard adopted by the Global Lung Function Initiative (GLI), with a segmented linear regression (SLR) model, for pulmonary function variables. The author presents an interesting comparison; however there are some fundamental issues with the approach. We welcome this opportunity for discussion of the issues that it raises. The author's contention is that (1) SLR provides "prediction accuracies on par with GAMLSS"; and (2) the GAMLSS model equations are "complicated and require supplementary spline tables", whereas the SLR is "more straightforward, parsimonious, and accessible to a broader audience". We respectfully disagree with both of these points.

Robert Sparrow, Joshua Hatherley

What does Artificial Intelligence (AI) have to contribute to health care? And what should we be looking out for if we are worried about its risks? In this paper we offer a survey, and initial evaluation, of hopes and fears about the applications of artificial intelligence in medicine. AI clearly has enormous potential as a research tool, in genomics and public health especially, as well as a diagnostic aid. It's also highly likely to impact on the organisational and business practices of healthcare systems in ways that are perhaps under-appreciated. Enthusiasts for AI have held out the prospect that it will free physicians up to spend more time attending to what really matters to them and their patients. We will argue that this claim depends upon implausible assumptions about the institutional and economic imperatives operating in contemporary healthcare settings. We will also highlight important concerns about privacy, surveillance, and bias in big data, as well as the risks of over trust in machines, the challenges of transparency, the deskilling of healthcare practitioners, the way AI reframes healthcare, and the implications of AI for the distribution of power in healthcare institutions. We will suggest that two questions, in particular, are deserving of further attention from philosophers and bioethicists. What does care look like when one is dealing with data as much as people? And, what weight should we give to the advice of machines in our own deliberations about medical decisions?

Paulo Ricardo Martins-Filho, Francy Waltília Cruz Araújo, Luiz Carlos Santos-Júnior et al.

Quan Wang, Yanmin Cao, Xinyu Liu et al.

Matthias Christenson, Cove Geary, Brian Locke et al.

The success of precision medicine requires computational models that can effectively process and interpret diverse physiological signals across heterogeneous patient populations. While foundation models have demonstrated remarkable transfer capabilities across various domains, their effectiveness in handling individual-specific physiological signals - crucial for precision medicine - remains largely unexplored. This work introduces a systematic pipeline for rapidly and efficiently evaluating foundation models' transfer capabilities in medical contexts. Our pipeline employs a three-stage approach. First, it leverages physiological simulation software to generate diverse, clinically relevant scenarios, particularly focusing on data-scarce medical conditions. This simulation-based approach enables both targeted capability assessment and subsequent model fine-tuning. Second, the pipeline projects these simulated signals through the foundation model to obtain embeddings, which are then evaluated using linear methods. This evaluation quantifies the model's ability to capture three critical aspects: physiological feature independence, temporal dynamics preservation, and medical scenario differentiation. Finally, the pipeline validates these representations through specific downstream medical tasks. Initial testing of our pipeline on the Moirai time series foundation model revealed significant limitations in physiological signal processing, including feature entanglement, temporal dynamics distortion, and reduced scenario discrimination. These findings suggest that current foundation models may require substantial architectural modifications or targeted fine-tuning before deployment in clinical settings.

Hyojin Bae, Bongsu Kang, Chang-Eop Kim

This study examines the clinical decision-making processes in Traditional East Asian Medicine (TEAM) by reinterpreting pattern identification (PI) through the lens of dimensionality reduction. Focusing on the Eight Principle Pattern Identification (EPPI) system and utilizing empirical data from the Shang-Han-Lun, we explore the necessity and significance of prioritizing the Exterior-Interior pattern in diagnosis and treatment selection. We test three hypotheses: whether the Ext-Int pattern contains the most information about patient symptoms, represents the most abstract and generalizable symptom information, and facilitates the selection of appropriate herbal prescriptions. Employing quantitative measures such as the abstraction index, cross-conditional generalization performance, and decision tree regression, our results demonstrate that the Exterior-Interior pattern represents the most abstract and generalizable symptom information, contributing to the efficient mapping between symptom and herbal prescription spaces. This research provides an objective framework for understanding the cognitive processes underlying TEAM, bridging traditional medical practices with modern computational approaches. The findings offer insights into the development of AI-driven diagnostic tools in TEAM and conventional medicine, with the potential to advance clinical practice, education, and research.

Frederik F. Flöther

The last few years have seen rapid progress in transitioning quantum computing from lab to industry. In healthcare and life sciences, more than 40 proof-of-concept experiments and studies have been conducted; an increasing number of these are even run on real quantum hardware. Major investments have been made with hundreds of millions of dollars already allocated towards quantum applications and hardware in medicine. In addition to pharmaceutical and life sciences uses, clinical and medical applications are now increasingly coming into the picture. This chapter focuses on three key use case areas associated with (precision) medicine, including genomics and clinical research, diagnostics, and treatments and interventions. Examples of organizations and the use cases they have been researching are given; ideas how the development of practical quantum computing applications can be further accelerated are described.

Qiao Jin, Fangyuan Chen, Yiliang Zhou et al.

Recent studies indicate that Generative Pre-trained Transformer 4 with Vision (GPT-4V) outperforms human physicians in medical challenge tasks. However, these evaluations primarily focused on the accuracy of multi-choice questions alone. Our study extends the current scope by conducting a comprehensive analysis of GPT-4V's rationales of image comprehension, recall of medical knowledge, and step-by-step multimodal reasoning when solving New England Journal of Medicine (NEJM) Image Challenges - an imaging quiz designed to test the knowledge and diagnostic capabilities of medical professionals. Evaluation results confirmed that GPT-4V performs comparatively to human physicians regarding multi-choice accuracy (81.6% vs. 77.8%). GPT-4V also performs well in cases where physicians incorrectly answer, with over 78% accuracy. However, we discovered that GPT-4V frequently presents flawed rationales in cases where it makes the correct final choices (35.5%), most prominent in image comprehension (27.2%). Regardless of GPT-4V's high accuracy in multi-choice questions, our findings emphasize the necessity for further in-depth evaluations of its rationales before integrating such multimodal AI models into clinical workflows.

Sue Ann Costa Clemens, Gustavo Mendes Lima Santos, Isabela Gonzalez et al.

Poliovirus infection causes paralysis in up to 1 in 200 infected persons. The use of safe and effective inactivated poliovirus vaccines and live attenuated oral poliovirus vaccines (OPVs) means that only two pockets of wildtype poliovirus type 1 remain, in Afghanistan and Pakistan. However, OPVs can revert to virulence, causing outbreaks of circulating vaccine-derived poliovirus (cVDPV). During 2020–2022, cVDPV type 2 (cVDPV2) was responsible for 97–99% of poliomyelitis cases, mainly in Africa. Between January and August 2022, cVDPV2 was detected in sewage samples in Israel, the United Kingdom and the United States of America, where a case of acute flaccid paralysis caused by cVDPV2 also occurred. The Pan American Health Organization has warned that Brazil, the Dominican Republic, Haiti and Peru are at very high risk for the reintroduction of poliovirus and an additional eight countries in Latin America are at high risk, following dropping vaccination rates (average 80% coverage in 2022). Sabin type 2 monovalent OPV has been used to control VDPV2 outbreaks, but its use could also lead to outbreaks. To address this issue, a more genetically stable, novel OPV2 (nOPV2) was developed against cVDPV2 and in 2020 was granted World Health Organization Emergency Use Listing. Rolling out a novel vaccine under the Emergency Use Listing in mass settings to contain outbreaks requires unique local regulatory and operational preparedness.

Vaneza Tique-Salleg, Jairo Chevel-Mejia, Jorge Miranda et al.

Frederik F. Flöther

Medicine, including fields in healthcare and life sciences, has seen a flurry of quantum-related activities and experiments in the last few years (although biology and quantum theory have arguably been entangled ever since Schrödinger's cat). The initial focus was on biochemical and computational biology problems; recently, however, clinical and medical quantum solutions have drawn increasing interest. The rapid emergence of quantum computing in health and medicine necessitates a mapping of the landscape. In this review, clinical and medical proof-of-concept quantum computing applications are outlined and put into perspective. These consist of over 40 experimental and theoretical studies. The use case areas span genomics, clinical research and discovery, diagnostics, and treatments and interventions. Quantum machine learning (QML) in particular has rapidly evolved and shown to be competitive with classical benchmarks in recent medical research. Near-term QML algorithms have been trained with diverse clinical and real-world data sets. This includes studies in generating new molecular entities as drug candidates, diagnosing based on medical image classification, predicting patient persistence, forecasting treatment effectiveness, and tailoring radiotherapy. The use cases and algorithms are summarized and an outlook on medicine in the quantum era, including technical and ethical challenges, is provided.

Hubert Baniecki, Bartlomiej Sobieski, Patryk Szatkowski et al.

Time-to-event prediction, e.g. cancer survival analysis or hospital length of stay, is a highly prominent machine learning task in medical and healthcare applications. However, only a few interpretable machine learning methods comply with its challenges. To facilitate a comprehensive explanatory analysis of survival models, we formally introduce time-dependent feature effects and global feature importance explanations. We show how post-hoc interpretation methods allow for finding biases in AI systems predicting length of stay using a novel multi-modal dataset created from 1235 X-ray images with textual radiology reports annotated by human experts. Moreover, we evaluate cancer survival models beyond predictive performance to include the importance of multi-omics feature groups based on a large-scale benchmark comprising 11 datasets from The Cancer Genome Atlas (TCGA). Model developers can use the proposed methods to debug and improve machine learning algorithms, while physicians can discover disease biomarkers and assess their significance. We hope the contributed open data and code resources facilitate future work in the emerging research direction of explainable survival analysis.

Kathrin Krieger, Jan Egger, Jens Kleesiek et al.

3D data from high-resolution volumetric imaging is a central resource for diagnosis and treatment in modern medicine. While the fast development of AI enhances imaging and analysis, commonly used visualization methods lag far behind. Recent research used extended reality (XR) for perceiving 3D images with visual depth perception and touch but used restrictive haptic devices. While unrestricted touch benefits volumetric data examination, implementing natural haptic interaction with XR is challenging. The research question is whether a multisensory XR application with intuitive haptic interaction adds value and should be pursued. In a study, 24 experts for biomedical images in research and medicine explored 3D medical shapes with 3 applications: a multisensory virtual reality (VR) prototype using haptic gloves, a simple VR prototype using controllers, and a standard PC application. Results of standardized questionnaires showed no significant differences between all application types regarding usability and no significant difference between both VR applications regarding presence. Participants agreed to statements that VR visualizations provide better depth information, using the hands instead of controllers simplifies data exploration, the multisensory VR prototype allows intuitive data exploration, and it is beneficial over traditional data examination methods. While most participants mentioned manual interaction as best aspect, they also found it the most improvable. We conclude that a multisensory XR application with improved manual interaction adds value for volumetric biomedical data examination. We will proceed with our open-source research project ISH3DE (Intuitive Stereoptic Haptic 3D Data Exploration) to serve medical education, therapeutic decisions, surgery preparations, or research data analysis.

Stéphanie Watier-Grillot, Damien Costa, Cédric Petit et al.

<h4>Introduction</h4>Contaminated drinking and recreational waters account for most of the reported Cryptosporidium spp. exposures in high-income countries. In June 2017, two successive cryptosporidiosis outbreaks occurred among service members in a military training camp located in Southwest France. Several other gastroenteritis outbreaks were previously reported in this camp, all among trainees in the days following their arrival, without any causative pathogen identification. Epidemiological, microbiological and environmental investigations were carried out to explain theses outbreaks.<h4>Material and methods</h4>Syndromic diagnosis using multiplex PCR was used for stool testing. Water samples (100 L) were collected at 10 points of the drinking water installations and enumeration of Cryptosporidium oocysts performed. The identification of Cryptosporidium species was performed using real-time 18S SSU rRNA PCR and confirmed by GP60 sequencing.<h4>Results</h4>A total of 100 human cases were reported with a global attack rate of 27.8%. Cryptosporidium spp. was identified in 93% of stool samples with syndromic multiplex PCR. The entire drinking water network was contaminated with Cryptosporidium spp. The highest level of contamination was found in groundwater and in the water leaving the treatment plant, with >1,000 oocysts per 100 L. The same Cryptosporidium hominis isolate subtype IbA10G2 was identified in patients' stool and water samples. Several polluting activities were identified within the protection perimeters of the water resource. An additional ultrafiltration module was installed at the outlet of the water treatment plant. After several weeks, no Cryptosporidium oocysts were found in the public water supply.<h4>Conclusions</h4>After successive and unexplained gastroenteritis outbreaks, this investigation confirmed a waterborne outbreak due to Cryptosporidium hominis subtype IbA10G2. Our study demonstrates the value of syndromic diagnosis for gastroenteritis outbreak investigation. Our results also highlight the importance of better assessing the microbiological risk associated with raw water and the need for sensitive and easy-to-implement tools for parasite detection.

Sergey Primakov, Elizaveta Lavrova, Zohaib Salahuddin et al.

Medical image analysis plays a key role in precision medicine as it allows the clinicians to identify anatomical abnormalities and it is routinely used in clinical assessment. Data curation and pre-processing of medical images are critical steps in the quantitative medical image analysis that can have a significant impact on the resulting model performance. In this paper, we introduce a precision-medicine-toolbox that allows researchers to perform data curation, image pre-processing and handcrafted radiomics extraction (via Pyradiomics) and feature exploration tasks with Python. With this open-source solution, we aim to address the data preparation and exploration problem, bridge the gap between the currently existing packages, and improve the reproducibility of quantitative medical imaging research.

Eduard Mas‐Carrió, Judith Schneider, Battogtokh Nasanbat et al.

1Laboratory for Conservation Biology, Department of Ecology and Evolution, Biophore, University of Lausanne, Lausanne, Switzerland 2Department of Animal Science and Food Processing, Faculty of Tropical AgriSciences, Czech University of Life Sciences Prague, Prague, Czech Republic 3Laboratory of Mammalian Ecology, Institute of Biology, Mongolian Academy of Sciences, Ulaanbaatar, Mongolia 4Department of Biology, School of Arts and Sciences, National University of Mongolia, Ulaanbaatar, Mongolia 5Department of Biological Sciences and Biotechnology, Botswana International University of Sciences and Technology (BIUST), Palapye, Botswana 6Department of Ecology and Evolution, Biophore, University of Lausanne, Lausanne, Switzerland 7Laboratoire d’Ecologie Alpine, CNRS, Université Grenoble Alpes, Grenoble, France 8UiT – The Arctic University of Norway, Tromsø Museum, Tromsø, Norway 9Musée Cantonal de Zoologie, Lausanne, Switzerland 10Swiss Human Institute of Forensic Taphonomy, University Centre of Legal Medicine LausanneGeneva, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland

Sarah J Burthe, Stefanie M Schäfer, Festus A Asaaga et al.

Zoonoses disproportionately affect tropical communities and are associated with human modification and use of ecosystems. Effective management is hampered by poor ecological understanding of disease transmission and often focuses on human vaccination or treatment. Better ecological understanding of multi-vector and multi-host transmission, social and environmental factors altering human exposure, might enable a broader suite of management options. Options may include "ecological interventions" that target vectors or hosts and require good knowledge of underlying transmission processes, which may be more effective, economical, and long lasting than conventional approaches. New frameworks identify the hierarchical series of barriers that a pathogen needs to overcome before human spillover occurs and demonstrate how ecological interventions may strengthen these barriers and complement human-focused disease control. We extend these frameworks for vector-borne zoonoses, focusing on Kyasanur Forest Disease Virus (KFDV), a tick-borne, neglected zoonosis affecting poor forest communities in India, involving complex communities of tick and host species. We identify the hierarchical barriers to pathogen transmission targeted by existing management. We show that existing interventions mainly focus on human barriers (via personal protection and vaccination) or at barriers relating to Kyasanur Forest Disease (KFD) vectors (tick control on cattle and at the sites of host (monkey) deaths). We review the validity of existing management guidance for KFD through literature review and interviews with disease managers. Efficacy of interventions was difficult to quantify due to poor empirical understanding of KFDV-vector-host ecology, particularly the role of cattle and monkeys in the disease transmission cycle. Cattle are hypothesised to amplify tick populations. Monkeys may act as sentinels of human infection or are hypothesised to act as amplifying hosts for KFDV, but the spatial scale of risk arising from ticks infected via monkeys versus small mammal reservoirs is unclear. We identified 19 urgent research priorities for refinement of current management strategies or development of ecological interventions targeting vectors and host barriers to prevent disease spillover in the future.