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.
Abstract Background Kidney function is associated with kidney volume. This study aims to explore automated segmentation for measuring total kidney volume (TKV) and to analyse the association between (changes in) TKV and acute kidney injury (AKI) incidence and/or severity in Intensive Care Unit (ICU) patients. Methods Patients were included in this retrospective pilot cohort study when at least two abdominal Computed Tomography (CT) scans were performed during ICU admission. If available, CT scans made before the ICU admission were included as a baseline scan. TKV was measured by automated segmentation of both kidneys using Data Analysis Facilitation Suite (DAFS, Voronoi Analytics Incorporated). All segmentations were visually checked and manually adjusted when necessary. ΔTKV was calculated between baseline CT and CT1 (ΔTKVCT1–baseline) and CT1 and CT2 (ΔTKVCT2–CT1). Primary outcomes were differences in kidney volume before and after manual correction and AKI incidence and severity, per the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines, on the day of scanning. Results Twenty-six patients were included, of whom eighteen developed AKI during ICU admission. Analysis showed no significant differences in volumes before and after manual correction of the automated segmentations. TKV was not associated with AKI incidence or severity. Longitudinal intraindividual changes in TKV were observed. Median ΔTKVCT1–baseline was statistically significantly different for AKI versus non-AKI patients (−22 cm3 (−49–9) versus 42 cm3 (23–43), p = 0.03) and for different KDIGO stages. Conclusion This study demonstrates the possibility of measuring TKV on CT in ICU patients using automated segmentation. Longitudinal intraindividual changes in TKV were observed, however, no clear association between TKV and AKI was found. Clinical trial number Not applicable.
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.
Vidya Venkatesan, Aomawa L. Shields, Russell Deitrick
et al.
Eccentric planets may spend a significant portion of their orbits at large distances from their host stars, where low temperatures can cause atmospheric CO2 to condense out onto the surface, similar to the polar ice caps on Mars. The radiative effects on the climates of these planets throughout their orbits would depend on the wavelength-dependent albedo of surface CO2 ice that may accumulate at or near apoastron and vary according to the spectral energy distribution of the host star. To explore these possible effects, we incorporated a CO2 ice-albedo parameterization into a one-dimensional energy balance climate model. With the inclusion of this parameterization, our simulations demonstrated that F-dwarf planets require 29% more orbit-averaged flux to thaw out of global water ice cover compared with simulations that solely use a traditional pure water ice-albedo parameterization. When no eccentricity is assumed, and host stars are varied, F-dwarf planets with higher bond albedos relative to their M-dwarf planet counterparts require 30% more orbit-averaged flux to exit a water snowball state. Additionally, the intense heat experienced at periastron aids eccentric planets in exiting a snowball state with a smaller increase in instellation compared with planets on circular orbits; this enables eccentric planets to exhibit warmer conditions along a broad range of instellation. This study emphasizes the significance of incorporating an albedo parameterization for the formation of CO2 ice into climate models to accurately assess the habitability of eccentric planets, as we show that, even at moderate eccentricities, planets with Earth-like atmospheres can reach surface temperatures cold enough for the condensation of CO2 onto their surfaces, as can planets receiving low amounts of instellation on circular orbits.
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.
Abstract Background In-silico and in-vitro studies have revealed an appropriate posterior tibial slope (PTS) is critical for normal anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL) tension and knee biomechanical behavior of unicompartmental knee arthroplasty (UKA). However, the effects of PTS on in-vivo elongation of ACL and PCL in UKA remains unknown. The study aimed to quantify in-vivo ACL and PCL elongations during lunge and analyze their relations with PTS. Methods Thirteen fixed-bearing (FB) and 11 mobile-bearing (MB) UKA patients were recruited. The postoperative medial PTS was defined as the angle between the tibial transverse plane (perpendicular to mechanical axis) and cut plane. Accurate knee spatial postures of UKA and contralateral native knees during single-leg lunge were measured by the dual fluoroscopic imaging system. The ACL (AM, PL bundles) and PCL (AL, PM bundles) footprints were determined based on anatomical features on femoral and tibial 3D surface model reconstructed from CT. A validated 3D wrapping method was used to measure ligament bundle length. The paired Wilcoxon signed-rank test was used to analyze the ligament elongation difference between bilateral knees. The Spearman correlation between PTS and average ligament elongation difference (ACL during 0–30° early-flexion, PCL during 60–100° deep-flexion) was calculated. Results The elongation of FB UKA PCL double-bundle was larger than contralateral sides in most flexion range of lunge (Max-Difference: AL 7.6 ± 8.7%, PM 8.2 ± 5.1%, p < 0.05). In contrast, ACL double-bundle elongations of MB UKA in mid-flexion were larger than contralateral sides (Max-Difference: AM 8.0 ± 8.1%, PL 7.6 ± 9.8%, p < 0.05). The increased PTS was significantly relevant to the increased ACL double-bundle elongation difference of bilateral knees for both FB and MB UKA patients (R > 0.6, p < 0.05). Conclusion There was abnormal in-vivo elongation of PCL in FB UKA and ACL in MB UKA during lunge and cause over-constraints to the contralateral knee. There was a positive correlation between PTS and ACL elongation difference for both FB and MB UKA, indicating excessive PTS should be avoided to preserve native ACL function in further UKA implantation. Levels of Evidence III.
Laura Izquierdo Sanchez, Julen Matin Robles, Jone Narbaiza
et al.
Introduction and Objectives: Cholangiocarcinoma (CCA) incidence and mortality are rising globally. Chronic liver diseases (CLD) are recognized risk factors. This study aimed to compare the clinical presentation and outcomes of CCA in patients with and without CLD, using data from the International CCA Registry. Patients and Methods: The international CCA Registry is a multicenter observational study enrolling cases from 54 centers across Latin America, Europe, and Asia (2010–2024). Results: Among 3,693 patients enrolled, 916 had CLD and 2,777 did not. Common CLD conditions were fatty liver disease, cirrhosis, viral hepatitis, and primary sclerosing cholangitis. Compared to non-CLD patients, those with CLD were more often male (69% vs. 53%), younger at diagnosis (63 vs. 66 years), and had higher rates of metabolic risk factors, alcohol use, and smoking. Intrahepatic CCA was more frequent in CLD patients (64% vs. 43%), whereas distal CCA was more common in non-CLD cases (20% vs. 9%). CLD patients had better performance status (ECOG 0: 53% vs. 35%), lower CA19-9 levels (59.0 vs. 134.5 U/mL), and more localized disease (56% vs. 48%). Curative-intent surgery was more frequent in the CLD group (59% vs. 48%), translating into longer median overall survival (12.3 vs. 11.0 months) and higher 5-year survival (OR = 1.67; p < 0.001). The benefit was especially evident in intrahepatic CCA. Treatment responses were comparable between groups. Conclusions: CCA is diagnosed at earlier stages in individuals with CLD, likely due to certain clinical surveillance, leading to better prognosis. Prospective validation and standardized surveillance protocols are warrant.
Autoimmune hepatitis (AIH) is a progressive liver inflammatory disease mediated by an autoimmune response, with an increasing incidence rate. In severe cases, AIH will rapidly progress to liver cirrhosis and liver failure and even lead to death. The gut microbiota is a complex ecosystem that significantly regulates physiological and pathological processes among various digestive system diseases. It is widely acknowledged that there is a critical correlation between AIH and the gut microbiota. Numerous studies have demonstrated that the composition of gut microbiota in individuals with AIH differs markedly from that of healthy subjects. Immune cells, especially T cells, are pivotal in the development of AIH, closely interacting with the gut microbiota. In this review, we discuss the regulatory role of the gut microbiota in T cell-mediated development of AIH, as well as the effect of T cells on the composition of the gut microbiota in AIH. By modulating gut microbiota or immunity pathways, novel opportunities are provided to regulate the balance of the immune-microbial microenvironment, targeting the dual factor for autoimmune hepatitis therapies.
Objective To evaluate trends in racial, ethnic, and sex representation at US medical schools across 16 specialties: internal medicine, pediatrics, surgery, psychiatry, radiology, anesthesiology, obstetrics and gynecology, neurology, family practice, pathology, emergency medicine, orthopedic surgery, ophthalmology, otolaryngology, physical medicine and rehabilitation, and dermatology. Using a novel, Census-derived statistical measure of diversity, the S-score, we quantified the degree of underrepresentation for racial minority groups and female faculty by rank for assistant, associate, and full professors from 1990–2016. Methods This longitudinal study of faculty diversity uses data obtained from the American Association of Medical Colleges (AAMC) Faculty Roster from US allopathic medical schools. The proportion of professors of racial minority groups and female faculty by rank was compared to the US population based on data from the US Census Bureau. The Roster includes data on 52,939 clinical medical faculty in 1990, and 129,545 in 2016, at the assistant professor level or higher. The primary measure used in this study was the S-score, a measure of representation based on the probability of the observed frequency of faculty from a racial/ethnic group and sex, given the racial and ethnic distribution of the US. Pearson correlations and 95% confidence intervals for S-score with time were used to measure trends. Results Blacks and Hispanics showed statistically significant trends (p<0.05) towards increasing underrepresentation in most specialties and are more underrepresented in 2016 than in 1990 across all ranks and specialties analyzed, except for Black females in obstetrics & gynecology. White females were also underrepresented in many specialties and in a subset of specialties trended toward greater underrepresentation. Conclusions Current efforts to improve faculty diversity are inadequate in generating an academic physician workforce that represents the diversity of the US. More aggressive measures for faculty recruitment, retention, and promotion are necessary to reach equity in academia and healthcare.
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.
In this paper, we introduce InMD-X, a collection of multiple large language models specifically designed to cater to the unique characteristics and demands of Internal Medicine Doctors (IMD). InMD-X represents a groundbreaking development in natural language processing, offering a suite of language models fine-tuned for various aspects of the internal medicine field. These models encompass a wide range of medical sub-specialties, enabling IMDs to perform more efficient and accurate research, diagnosis, and documentation. InMD-X's versatility and adaptability make it a valuable tool for improving the healthcare industry, enhancing communication between healthcare professionals, and advancing medical research. Each model within InMD-X is meticulously tailored to address specific challenges faced by IMDs, ensuring the highest level of precision and comprehensiveness in clinical text analysis and decision support. This paper provides an overview of the design, development, and evaluation of InMD-X, showcasing its potential to revolutionize the way internal medicine practitioners interact with medical data and information. We present results from extensive testing, demonstrating the effectiveness and practical utility of InMD-X in real-world medical scenarios.
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.
Arterial aneurysms remain a significant public health problem because they often result in death when ruptured; therefore, they require immediate medical treatment. Endovascular aneurysm repair (EVAR) has recently become the primary treatment option, owing to the fewer side effects compared to those with open surgery. However, stents used for conventional EVAR often cause side-branch occlusion, which alters the perfusion of vital organs. Recently, multilayer flow modulator (MFM) stents have been used as a new treatment for arterial aneurysms. These stents appear to be feasible owing to their unique design consisting of an uncoated three-dimensionally braided multilayered structure. MFM stents generally remodulate laminar flow and reduce the flow velocity in the aneurysmal sac, leading to thrombosis, which causes the aneurysm to shrink over time. Thus, they reduce the risk of mortality. Moreover, they reduce morbidity by preserving the side-branch blood flow. They can be easily applied to complex aneurysms and are ready to use without customization, which shortens the waiting time for interventions. This study aimed to evaluate the role of MFM stents in the treatment of arterial aneurysms based on available data.
Diseases of the circulatory (Cardiovascular) system
Objective: Gender parity lags in academic medicine. We applied the Rank Equity Index (REI) to compare the longitudinal progress of women's academic medicine careers. We hypothesized that women have different rank parity in promotion by specialty based on the proportion of women in the specialty. Materials and Methods: Aggregate data by sex for medical students, residents, assistant professors, associate professors, and professors in nine specialties were obtained from the Association of American Medical Colleges for 2019–2020. Specialties were clustered into terciles based on the proportion of women in the field: upper (obstetrics and gynecology, pediatrics, psychiatry), middle (internal medicine, emergency medicine, anesthesia), and lower (surgery, urology, and orthopedic surgery). We calculated the percentage representation by sex by specialty and rank to calculate REI. Specialty-specific REI comparisons between each rank were performed to assess parity in advancement. Results: Only specialties in the upper tercile recruited proportionally more women medical students to residency training. All specialties advanced women for the resident-to-assistant professor with psychiatry, internal medicine, emergency medicine, anesthesia, urology, and orthopedic surgery that promoted women faculty at rates above parity. No specialty demonstrated parity in advancement based on sex for the assistant professor-to-associate professor or associate professor-to-professor transitions. Conclusion: Gender inequity in advancement is evident in academic medicine starting at the assistant professor-to-associate professor stage, regardless of overall proportion of women in the specialty. This suggests a common set of barriers to career advancement of women faculty in academic medicine that must be addressed starting at the early career stage.
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.
Andrii Voshchepynets, Oleksiy Agapitov, Lynn Wilson
et al.
We present the results of processing the effects of the powerful Gamma Ray Burst GRB221009A captured by the charged particle detectors (electrostatic analyzers and solid-state detectors) onboard spacecraft at different points in the heliosphere on October 9, 2022. To follow the GRB221009A propagation through the heliosphere we used the electron and proton flux measurements from solar missions Solar Orbiter and STEREO-A; Earth magnetosphere and the solar wind missions THEMIS and Wind; meteorological satellites POES15, POES19, MetOp3; and MAVEN - a NASA mission orbiting Mars. GRB221009A had a structure of four bursts: less intense Pulse 1 - the triggering impulse - was detected by gamma-ray observatories at 131659 UT (near the Earth); the most intense Pulses 2 and 3 were detected on board all the spacecraft from the list, and Pulse 4 detected in more than 500 s after Pulse 1. Due to their different scientific objectives, the spacecraft, which data was used in this study, were separated by more than 1 AU (Solar Orbiter and MAVEN). This enabled tracking GRB221009A as it was propagating across the heliosphere. STEREO-A was the first to register Pulse 2 and 3 of the GRB, almost 100 seconds before their detection by spacecraft in the vicinity of Earth. MAVEN detected GRB221009A Pulses 2, 3, and 4 at the orbit of Mars about 237 seconds after their detection near Earth. By processing the time delays observed we show that the source location of the GRB221009A was at RA 288.5 degrees, Dec 18.5 degrees (J2000) with an error cone of 2 degrees
Introduction
Tobacco users are categorized as single, dual, and triple users based
on the number of tobacco products (cigarettes, e-cigarettes, and heated tobacco
products) used. This study addressed a literature gap by examining how adult
Korean tobacco users’ quit attempts/plans differed based on the user type, and
the associated psychosocial and subjective health-related factors.
Methods
We used a questionnaire to examine participants' self-reported health,
stress, health concerns, health behavior, tobacco addiction, intentions/plans to
quit, and demographic characteristics. Data were analyzed using chi-squared tests,
one-way analysis of variance, and multiple linear regression.
Results
Of the 1288 tobacco users, 55.4%, 28.3%, and 16.4% were single, dual, and
triple users, respectively. Self-rated health and stress were lowest among single
users and highest among triple users. Most user types had intentions/plans to
quit, especially triple users. Quit attempts and plans increased with increasing
health behaviors and time elapsed before first tobacco use in the morning, but
decreased with higher stress and self-rated addiction.
Conclusions
Intentions/plans to quit tobacco use varied based on the type of tobacco
user. Multiple users had higher self-rated health, plans to quit, and self-reported
addiction; they considered themselves healthy or engaged in healthy behaviors
to offset problems from tobacco use and used multiple tobacco products to quit
smoking. Highly stressed users had fewer plans to quit and used tobacco for stress
relief. Thus, the provision of accurate information about tobacco products and
stress management is important to promote successful quitting.
Diseases of the respiratory system, Neoplasms. Tumors. Oncology. Including cancer and carcinogens