The promise of AI in medicine depends on learning from data that reflect what matters to patients and clinicians. Most existing models are trained on electronic health records (EHRs), which capture biological measures but rarely patient-clinician interactions. These relationships, central to care, unfold across voice, text, and video, yet remain absent from datasets. As a result, AI systems trained solely on EHRs risk perpetuating a narrow biomedical view of medicine and overlooking the lived exchanges that define clinical encounters. Our objective is to design, implement, and evaluate the feasibility of a longitudinal, multimodal system for capturing patient-clinician encounters, linking 360 degree video/audio recordings with surveys and EHR data to create a dataset for AI research. This single site study is in an academic outpatient endocrinology clinic at Mayo Clinic. Adult patients with in-person visits to participating clinicians are invited to enroll. Encounters are recorded with a 360 degree video camera. After each visit, patients complete a survey on empathy, satisfaction, pace, and treatment burden. Demographic and clinical data are extracted from the EHR. Feasibility is assessed using five endpoints: clinician consent, patient consent, recording success, survey completion, and data linkage across modalities. Recruitment began in January 2025. By August 2025, 35 of 36 eligible clinicians (97%) and 212 of 281 approached patients (75%) had consented. Of consented encounters, 162 (76%) had complete recordings and 204 (96%) completed the survey. This study aims to demonstrate the feasibility of a replicable framework for capturing the multimodal dynamics of patient-clinician encounters. By detailing workflows, endpoints, and ethical safeguards, it provides a template for longitudinal datasets and lays the foundation for AI models that incorporate the complexity of care.

Large Language Models (LLMs) constitute a breakthrough state-of-the-art Artificial Intelligence (AI) technology which is rapidly evolving and promises to aid in medical diagnosis either by assisting doctors or by simulating a doctor's workflow in more advanced and complex implementations. In this technical paper, we outline Cognitive Network Evaluation Toolkit for Medical Domains (COGNET-MD), which constitutes a novel benchmark for LLM evaluation in the medical domain. Specifically, we propose a scoring-framework with increased difficulty to assess the ability of LLMs in interpreting medical text. The proposed framework is accompanied with a database of Multiple Choice Quizzes (MCQs). To ensure alignment with current medical trends and enhance safety, usefulness, and applicability, these MCQs have been constructed in collaboration with several associated medical experts in various medical domains and are characterized by varying degrees of difficulty. The current (first) version of the database includes the medical domains of Psychiatry, Dentistry, Pulmonology, Dermatology and Endocrinology, but it will be continuously extended and expanded to include additional medical domains.

Today, almost half of the world's population does not have access to proper healthcare, with many people driven into poverty because of high health expenses. It is estimated that over $140 billion is required annually to meet the health-related sustainable development goal objectives. Further, significant health technology, digital technology, and artificial intelligence (AI) investments are needed to bridge the health service gap in emerging markets. Many health-related startups and tech innovators have started integrating AI with their products and solutions, showing promise of improved diagnoses, reduced costs, and proper access to remote health services. COVID-19 has also accelerated the pace of transition to digital health applications, including those that integrate AI. Health startups and tech companies aiming to integrate AI technologies account for a large proportion of AI-specific investments, accounting for up to $2 billion in 2018 (Figure 1).

NIR Calibration-Model Services Spectroscopy and Chemometrics News Weekly 33, 2022 NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 33, 2022 NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK Spettroscopia e Chemiometria Weekly News 33, 2022 NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK Near-Infrared Spectroscopy (NIRS) "Comparative Performance of NIR-Hyperspectral Imaging Systems" LINK "Near infrared spectroscopy calibration strategies to predict multiple nutritional parameters of pasture species from different functional groups" LINK "Near-infrared spectroscopy as a tool to assist Sargassum fusiforme quality grading: Harvest time discrimination and polyphenol prediction" LINK "Sensors : Using Vis-NIR Spectroscopy for Predicting Quality Compounds in Foods" LINK "Development of an amino acid sequence-dependent analytical method for peptides using near-infrared spectroscopy" LINK "NDT model study of crown pear based on near infrared spectroscopy" LINK "Analyzing the Water Confined in Hydrogel Using Near-Infrared Spectroscopy" LINK "Foods : Finite Element Analysis and Near-Infrared Hyperspectral Reflectance Imaging for the Determination of Blueberry Bruise Grading" LINK "Application of near infrared spectroscopy in sub-surface monitoring of petroleum contaminants in laboratory-prepared soils" LINK "Identification of multiple raisins by feature fusion combined with NIR spectroscopy" LINK " … of quality markers for quality control of Zanthoxylum nitidum using ultra-performance liquid chromatography coupled with near infrared spectroscopy" LINK "Karakterisasi Fitokimia Enkapsulasi Nira Tebu Powder dengan Menggunakan Varietas BL, PSDK-923, dan PSBM-901" LINK "Inside the Egg--Demonstrating Provenance Without the Cracking Using Near Infrared Spectroscopy" LINK "Organic resources from Madagascar: Dataset of chemical and near-infrared spectroscopy measurements" LINK "An alternative method for identification of industrial tomato hybrids using NIRS" LINK "Uniformity evaluation of stem distribution in cut tobacco and single cigarette by near infrared spectroscopy" LINK "A General and Scalable Vision Framework for Functional Near-Infrared Spectroscopy Classification" LINK "Near infrared spectroscopy for the pre-cure freezing discrimination of Montanera Iberian dry-cured lomito" LINK "Determination of Moisture and Protein Content in Living Mealworm Larvae (Tenebrio molitor L.) Using Near-Infrared Reflectance Spectroscopy (NIRS)" LINK "Towards Inline Prediction of Color Development for Wood Stained with Chemical Stains Using Near-Infrared Spectroscopy" LINK "Comparison Between Pure Component Modeling Approaches for Monitoring Pharmaceutical Powder Blends with Near-Infrared Spectroscopy in Continuous Manufacturing Schemes" LINK "Potential of NIRS technology for the determination of cannabinoid content in industrial hemp (Cannabis sativa L.)" LINK " A Variable Selection Method Based on Fast Nondominated Sorting Genetic Algorithm for Qualitative Discrimination of Near Infrared Spectroscopy" LINK "Scale invariance in fNIRS as a measurement of cognitive load" LINK "Quantification of Salicylates and Flavonoids in Poplar Bark and Leaves Based on IR, NIR, and Raman Spectra" LINK Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR) "Near-infrared spectroscopy monitoring during endovascular treatment for acute ischaemic stroke" LINK "Keakuratan Teknologi Near Infrared Dalam Mengukur Dan Memetakan Bahan Organik Di Pulau Lombok" LINK "NearInfrared Spectroscopic Characterization of Cardiac and Renal Fibrosis in Fixed and Fresh Rat Tissue" LINK "Application of Fourier transform infrared spectroscopy (FTIR) techniques in the mid-IR (MIR) and near-IR (NIR) spectroscopy to determine n-alkane and long-chain alcohol contents in plant species and faecal samples" LINK Hyperspectral Imaging (HSI) "Detection Storage Time of Mild Bruise's Loquats Using Hyperspectral Imaging" LINK "Determination of plumpness for kernel of semen ziziphi spinosae use of hyperspectral transmittance imaging technology coupled with improved Otsu algorithm" LINK "Prediction of oil content in single maize kernel based on hyperspectral imaging and attention convolution neural network" LINK "Convolutional neural networks for mapping of lake sediment core particle size using hyperspectral imaging" LINK Spectral Imaging "Applied Sciences : Non-Invasive Monitoring of the Thermal and Morphometric Characteristics of Lettuce Grown in an Aeroponic System through Multispectral Image System" LINK Chemometrics and Machine Learning "Rapid quantification of goat milk adulteration with cow milk using Raman spectroscopy and chemometrics" LINK "Plants : Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow" LINK "Near Infrared Spectra Data Analysis by Using Machine Learning Algorithms" LINK "Applied Sciences : Deep-Learning Model Selection and Parameter Estimation from a Wind Power Farm in Taiwan" LINK "Predicting maize LAI in partial least square modeling by continuous wavelet transform and uninformative variable elimination from canopy spectral reflectance" LINK "Machine Learning Algorithms for Protein Physicochemical Component Prediction Using Near Infrared Spectroscopy in Chickpea Germplasm" LINK "NIR Validation and Calibration of Proximate components of available Corn Silage in Bangladesh." So interested people will connect.

Identifying academic plagiarism is a pressing problem, among others, for research institutions, publishers, and funding organizations. Detection approaches proposed so far analyze lexical, syntactical, and semantic text similarity. These approaches find copied, moderately reworded, and literally translated text. However, reliably detecting disguised plagiarism, such as strong paraphrases, sense-for-sense translations, and the reuse of non-textual content and ideas, is an open research problem. The thesis addresses this problem by proposing plagiarism detection approaches that implement a different concept: analyzing non-textual content in academic documents, specifically citations, images, and mathematical content. To validate the effectiveness of the proposed detection approaches, the thesis presents five evaluations that use real cases of academic plagiarism and exploratory searches for unknown cases. The evaluation results show that non-textual content elements contain a high degree of semantic information, are language-independent, and largely immutable to the alterations that authors typically perform to conceal plagiarism. Analyzing non-textual content complements text-based detection approaches and increases the detection effectiveness, particularly for disguised forms of academic plagiarism. To demonstrate the benefit of combining non-textual and text-based detection methods, the thesis describes the first plagiarism detection system that integrates the analysis of citation-based, image-based, math-based, and text-based document similarity. The system's user interface employs visualizations that significantly reduce the effort and time users must invest in examining content similarity.

Even though model predictive control (MPC) is currently the main algorithm for insulin control in the artificial pancreas (AP), it usually requires complex online optimizations, which are infeasible for resource-constrained medical devices. MPC also typically relies on state estimation, an error-prone process. In this paper, we introduce a novel approach to AP control that uses Imitation Learning to synthesize neural-network insulin policies from MPC-computed demonstrations. Such policies are computationally efficient and, by instrumenting MPC at training time with full state information, they can directly map measurements into optimal therapy decisions, thus bypassing state estimation. We apply Bayesian inference via Monte Carlo Dropout to learn policies, which allows us to quantify prediction uncertainty and thereby derive safer therapy decisions. We show that our control policies trained under a specific patient model readily generalize (in terms of model parameters and disturbance distributions) to patient cohorts, consistently outperforming traditional MPC with state estimation.

The top stories in endocrinology last week focused on how artificial intelligence, or AI, could be a game-changer in predicting nonalcoholic fatty liver disease and detecting thyroid nodules. AI tool for nonalcoholic fatty liver disease could'make biopsies history' The detection and diagnosis of thyroid nodules may be on the cusp of a technological overhaul as a growing body of research on AI and machine learning tools aims to bring about more efficiency and accuracy while decreasing cost and improving ease-of-use. The Dermatologic and Ophthalmic Drugs Advisory Committee of the FDA voted 12-0 last week in favor of recommending approval of a biologics license application for teprotumumab, an experimental human monoclonal antibody shown to dramatically reduce the most debilitating symptoms of Graves' orbitopathy. The Society for Endocrinology and the British Thyroid Association issued a joint statement urging caution when interpreting a recent study linking radioactive iodine therapy to cancer mortality among people with hyperthyroidism. Japanese adults with type 2 diabetes assigned a long-term low-dose aspirin regimen did not lower their risk for dementia vs. similar adults who did not routinely take aspirin, according to a post hoc analysis of the Japanese Primary Prevention of Atherosclerosis with Aspirin for Diabetes trial.

As a subfield of machine learning, \emph{reinforcement learning} (RL) aims at empowering one's capabilities in behavioural decision making by using interaction experience with the world and an evaluative feedback. Unlike traditional supervised learning methods that usually rely on one-shot, exhaustive and supervised reward signals, RL tackles with sequential decision making problems with sampled, evaluative and delayed feedback simultaneously. Such distinctive features make RL technique a suitable candidate for developing powerful solutions in a variety of healthcare domains, where diagnosing decisions or treatment regimes are usually characterized by a prolonged and sequential procedure. This survey will discuss the broad applications of RL techniques in healthcare domains, in order to provide the research community with systematic understanding of theoretical foundations, enabling methods and techniques, existing challenges, and new insights of this emerging paradigm. By first briefly examining theoretical foundations and key techniques in RL research from efficient and representational directions, we then provide an overview of RL applications in a variety of healthcare domains, ranging from dynamic treatment regimes in chronic diseases and critical care, automated medical diagnosis from both unstructured and structured clinical data, as well as many other control or scheduling domains that have infiltrated many aspects of a healthcare system. Finally, we summarize the challenges and open issues in current research, and point out some potential solutions and directions for future research.

A survey of early work exploring how AI can be used in medicine, with somewhat more technical expositions than in the complementary volume Artificial Intelligence in Medicine."Each chapter is preceded by a brief introduction that outlines our view of its contribution to the field, the reason it was selected for inclusion in this volume, an overview of its content, and a discussion of how the work evolved after the article appeared and how it relates to other chapters in the book.

JANICE S. AIKINS Dr. Aikins received her Ph.D. in computer science from Stanford University in 1980. She is currently a research computer scientist at IBM's Palo Alto Scientific Center. She specializes in designing systems with an emphasis on the explicit representation of control knowledge in expert systems. ROBERT L. BLUM Dr. Blum received his M.D. from the University of California Medical School at San Francisco in 1973. From 1973 to 1976 he did an internship and residency in the Department of Internal Medicine at the Kaiser Foundation Hospital in Oakland, California, where he was chief resident in 1976.