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Medical Writings7 August 2001"Let Me See If I Have This Right …": Words That Help Build EmpathyJohn L. Coulehan, MD, Frederic W. Platt, MD, Barry Egener, MD, Richard Frankel, PhD, Chen-Tan Lin, MD, Beth Lown, MD, and William H. Salazar, MDJohn L. Coulehan, MDDr. Coulehan: State University of New York at Stony Brook; Stony Brook, NY 11794-8036Dr. Platt: University of Colorado Health Sciences Center; Denver, CO 80222Dr. Egener: American Academy on Physician and Patient; Portland, OR 97210Dr. Frankel: Highland Hospital; Rochester, NY 14620Dr. Lin: University of Colorado Health Sciences Center; Denver, CO 80222Dr. Lown: Mount Auburn Hospital; Cambridge, MA 02238Dr. Salazar: Medical College of Georgia; Augusta, GA 30902, Frederic W. Platt, MDDr. Coulehan: State University of New York at Stony Brook; Stony Brook, NY 11794-8036Dr. Platt: University of Colorado Health Sciences Center; Denver, CO 80222Dr. Egener: American Academy on Physician and Patient; Portland, OR 97210Dr. Frankel: Highland Hospital; Rochester, NY 14620Dr. Lin: University of Colorado Health Sciences Center; Denver, CO 80222Dr. Lown: Mount Auburn Hospital; Cambridge, MA 02238Dr. Salazar: Medical College of Georgia; Augusta, GA 30902, Barry Egener, MDDr. Coulehan: State University of New York at Stony Brook; Stony Brook, NY 11794-8036Dr. Platt: University of Colorado Health Sciences Center; Denver, CO 80222Dr. Egener: American Academy on Physician and Patient; Portland, OR 97210Dr. Frankel: Highland Hospital; Rochester, NY 14620Dr. Lin: University of Colorado Health Sciences Center; Denver, CO 80222Dr. Lown: Mount Auburn Hospital; Cambridge, MA 02238Dr. Salazar: Medical College of Georgia; Augusta, GA 30902, Richard Frankel, PhDDr. Coulehan: State University of New York at Stony Brook; Stony Brook, NY 11794-8036Dr. Platt: University of Colorado Health Sciences Center; Denver, CO 80222Dr. Egener: American Academy on Physician and Patient; Portland, OR 97210Dr. Frankel: Highland Hospital; Rochester, NY 14620Dr. Lin: University of Colorado Health Sciences Center; Denver, CO 80222Dr. Lown: Mount Auburn Hospital; Cambridge, MA 02238Dr. Salazar: Medical College of Georgia; Augusta, GA 30902, Chen-Tan Lin, MDDr. Coulehan: State University of New York at Stony Brook; Stony Brook, NY 11794-8036Dr. Platt: University of Colorado Health Sciences Center; Denver, CO 80222Dr. Egener: American Academy on Physician and Patient; Portland, OR 97210Dr. Frankel: Highland Hospital; Rochester, NY 14620Dr. Lin: University of Colorado Health Sciences Center; Denver, CO 80222Dr. Lown: Mount Auburn Hospital; Cambridge, MA 02238Dr. Salazar: Medical College of Georgia; Augusta, GA 30902, Beth Lown, MDDr. Coulehan: State University of New York at Stony Brook; Stony Brook, NY 11794-8036Dr. Platt: University of Colorado Health Sciences Center; Denver, CO 80222Dr. Egener: American Academy on Physician and Patient; Portland, OR 97210Dr. Frankel: Highland Hospital; Rochester, NY 14620Dr. Lin: University of Colorado Health Sciences Center; Denver, CO 80222Dr. Lown: Mount Auburn Hospital; Cambridge, MA 02238Dr. Salazar: Medical College of Georgia; Augusta, GA 30902, and William H. Salazar, MDDr. Coulehan: State University of New York at Stony Brook; Stony Brook, NY 11794-8036Dr. Platt: University of Colorado Health Sciences Center; Denver, CO 80222Dr. Egener: American Academy on Physician and Patient; Portland, OR 97210Dr. Frankel: Highland Hospital; Rochester, NY 14620Dr. Lin: University of Colorado Health Sciences Center; Denver, CO 80222Dr. Lown: Mount Auburn Hospital; Cambridge, MA 02238Dr. Salazar: Medical College of Georgia; Augusta, GA 30902Author, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-135-3-200108070-00022 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Consider these two physician–patient dialogues:1. Patient: You know, when you discover a lump in your breast, you kind of feel—well, kind of—(her speech tapers off; she looks down; tears form in her eyes).Dr. A: When did you actually discover the lump?Patient: (absently) I don't know. It's been a while.2. Patient: (same as above)Dr. B: That sounds frightening.Patient: Well, yeah, sort of.Dr. B: Sort of frightening?Patient: Yeah … and I guess I'm feeling like my life is over.Dr. B: I see. Worried and sad too.Patient: That's it, Doctor.Dr. A's patient ...References1. Konrad TR, Williams ES, Linzer M, McMurray J, Pathman DE, Gerrity M, et al . Measuring physician job satisfaction in a changing workplace and a challenging environment. SGIM Career Satisfaction Study Group. Society of General Internal Medicine. Med Care. 1999;37:1174-82. [PMID: 10549620] CrossrefMedlineGoogle Scholar2. Donelan K, Blendon RJ, Lundberg GD, Calkins DR, Newhouse JP, Leape LL, et al . The new medical marketplace: physicians' views. Health Aff Millwood. 1997;16:139-48. [PMID: 9314685] CrossrefMedlineGoogle Scholar3. Bates AS, Harris LE, Tierney WM, Wolinsky FD. Dimensions and correlates of physician work satisfaction in a midwestern city. Med Care. 1998;36:610-7. 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Being in the present moment: developing the capacity for mindfulness in medicine. Acad Med. 1999;74:420-4. [PMID: 10219225] CrossrefMedlineGoogle Scholar43. Epstein RM. Mindful practice. JAMA. 1999;282:833-9. [PMID: 10478689] CrossrefMedlineGoogle Scholar44. Miller SZ, Schmidt HJ. The habit of humanism: a framework for making humanistic care a reflexive clinical skill. Acad Med. 1999;74:800-3. [PMID: 10429589] CrossrefMedlineGoogle Scholar45. Barrett-Lennard GT. The phases and focus of empathy. Br J Med Psychol. 1993;66 Pt 1 3-14. [PMID: 8485075] CrossrefMedlineGoogle Scholar46. Gallop R, Lancee WJ, Garfinkel PE. The empathic process and its mediators. A heuristic model. J Nerv Ment Dis. 1990;178:649-54. [PMID: 2230750] CrossrefMedlineGoogle Scholar47. Hall JA, Roter DL, Rand CS. Communication of affect between patient and physician. J Health Soc Behav. 1981;22:18-30. [PMID: 7240703] CrossrefMedlineGoogle Scholar48. Larsen KM, Smith CK. Assessment of nonverbal communication in the patient–physician interview. J Fam Pract. 1981;12:481-8. [PMID: 7462949] MedlineGoogle Scholar49. Suchman AL, Matthews DA. What makes the patient-doctor relationship therapeutic? Exploring the connexional dimension of medical care. Ann Intern Med. 1988;108:125-30. [PMID: 3276262] LinkGoogle Scholar50. Suchman AL. Control and Relation: Two Foundational Values and Their Consequences.. In: Suchman AL, Botelho RJ, Hinton-Walker P, eds. Partnerships in Healthcare: Transforming Relational Process. Rochester, NY: Univ of Rochester Pr; 1998. Google Scholar51. Branch WT, Malik TK. Using "windows of opportunities" in brief interviews to understand patients' concerns. JAMA. 1993;269:1667-8. [PMID: 8455300] CrossrefMedlineGoogle Scholar52. Pinderhughes EB. Teaching empathy: ethnicity, race and power at the cross-cultural treatment interface. American Journal of Social Psychology. 1984;4:5-12. Google Scholar53. Kleinman A, Eisenberg L, Good B. Culture, illness, and care: clinical lessons from anthropologic and cross-cultural research. Ann Intern Med. 1978;88:251-8. [PMID: 626456] LinkGoogle Scholar54. Platt FW, Gaspar DL, Coulehan JL, Fox L, Adler AJ, Weston WW, et al . "Tell me about yourself": the patient-centered interview. Ann Intern Med. 2001;134:1079-85. LinkGoogle Scholar Author, Article, and Disclosure InformationAffiliations: Dr. Coulehan: State University of New York at Stony Brook; Stony Brook, NY 11794-8036Dr. Platt: University of Colorado Health Sciences Center; Denver, CO 80222Dr. Egener: American Academy on Physician and Patient; Portland, OR 97210Dr. Frankel: Highland Hospital; Rochester, NY 14620Dr. Lin: University of Colorado Health Sciences Center; Denver, CO 80222Dr. Lown: Mount Auburn Hospital; Cambridge, MA 02238Dr. Salazar: Medical College of Georgia; Augusta, GA 30902Corresponding Author: John L. Coulehan, MD, Department of Preventive Medicine, HSC L3-086, State University of New York at Stony Brook, Stony Brook, NY 11794-8036; e-mail, [email protected]sunysb.edu.Current Author Addresses: Dr. Coulehan: Department of Preventive Medicine, HSC L3-086, State University of New York at Stony Brook, Stony Brook, NY 11794-8036.Drs. Platt and Lin: University of Colorado Health Sciences Center, 4200 East Ninth Avenue, Denver, CO 80222.Dr. Egener: American Academy on Physician and Patient, Legacy Clinic Northwest, 1130 NW 22nd Avenue, Suite 220, Portland, OR 97210.Dr. Frankel: Highland Hospital, 1000 South Avenue, Rochester, NY 14620.Dr. Lown: Mount Auburn Hospital, 300 Mt. Auburn Street, Cambridge, MA 02238.Dr. Salazar: Medical College of Georgia, 1120 15th Street, HS2010, Augusta, GA 30902. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics Cited ByHow does narrative medicine impact medical trainees' learning of professionalism? 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How?The Effect of Patient Participation through Physician's Resources on Experience and WellbeingDepression/AnxietySuffering, Hope, and HealingA Review of Empathy, Its Importance, and Its Teaching in Surgical TrainingAthletes' Perception of Athletic Trainer Empathy: How Important Is It?Empathy from the perspective of oncology nursesPatients' Trust in Physician, Patient Enablement, and Health-Related Quality of Life During Colon Cancer TreatmentMeasuring Medical Students' EmpathyEmpathy in the Clinician–Patient RelationshipComparing two types of perspective taking as strategies for detecting distress amongst parents of children with cancer: A randomised trialIf You Could Read My Mind: The Role of Healthcare Providers' Empathic and Communicative Competencies in Clients' Satisfaction with ConsultationsPatients who are anxious or fearfulFatigue: Has It Affected Your Compassion?Study on Empathy among Undergraduate Students of the Medical Profession in NepalPower Relations and Health Care Communication in Older Adulthood: Educating Recipients and ProvidersBreast Biopsies are Minimally Painful, Exceed Patient Expectations, and Do Not Represent a Genuine Lasting Harm for Most WomenSpirituality and Ubuntu as the foundation for building African institutions, organizations and leadersShowing you care: An empathetic approach to doctor–patient communicationEmpathy and affect: what can empathied bodies do?Computational Analysis and Simulation of Empathic Behaviors: a Survey of Empathy with in medical students and empathy is with differences in communication and in empathy: the to clinical and at the of Social of What it to of changing empathy and patient in healthcare to for and Chronic medical communication: the of by Care A and of the Therapy of undergraduate and students medical A longitudinal studyEmpathy: what does it for A qualitative empathic and compassionate patient care: education and to the Patient with and Student Empathy in Medical Students During a Clinical and patients' perspectives on empathy: of and treatment of Approaches to the of and of a Communication in for of Healthcare as a do primary care when patients during and of the of the in a general medicine Patient Satisfaction for the . . . of of Care in the development of doctor-patient a call and a care: Enhancing physician–patient communication and education in of to Hope, and of empathy in general practice: a systematic with of Cancer Patient: of an of empathy and correlates among types of in empathic in primary care for research and with an to patients in A new perspective on empathic A for about and in longitudinal study of in interaction in treatment of in empathy medical role of and in medical education: the as as the approach to an empathic understanding of among patients and in the of The of Role of Empathy in Therapy and the New Communication Skills Assessment by Patients and and EmpathyEmpathy and Its A Review of With Medical Students and the nature of in the for health and the does care of the of of a with by among and of A to for and to clinical empathic a of Empathy with Behavior Medical Communication in in for Communication to and Patient to and A of and Cancer the and of clinical empathy: A theoretical and a research of communication in the medical A review and Clinical Empathy: An about to Physician during Care MD, H. PhD, M. MD, and and role in education about care in Competencies in Care for and Communication in patients with in the Care of Cancer in of and of Empathic during When Being Not empathy learning for be and for effective the role of care in with and in the of the medicine and outcomes of physician empathy in A structural focus study of and perceptions of the of of Medical Student role of empathy in in the a new and in the a of the An or Do You to to Patients Do Not or with Challenges and a of of the of a for children with Care. A to in care: The role of perceptions in Older in Clinical during Health and H. or Not to Is That the Right empathy and sympathy: responses to troubles on a health care with to make the patient the Communication and With Patients in Your as a of The of Medicine, and How to and A Patient-Centered Communication Illness Using to Communication Care and Social in the office approach to the Words That in to and MD, M. 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Medical image analysis is essential to clinical diagnosis and treatment, which is increasingly supported by multi-modal large language models (MLLMs). However, previous research has primarily focused on 2D medical images, leaving 3D images under-explored, despite their richer spatial information. This paper aims to advance 3D medical image analysis with MLLMs. To this end, we present a large-scale 3D multi-modal medical dataset, M3D-Data, comprising 120K image-text pairs and 662K instruction-response pairs specifically tailored for various 3D medical tasks, such as image-text retrieval, report generation, visual question answering, positioning, and segmentation. Additionally, we propose M3D-LaMed, a versatile multi-modal large language model for 3D medical image analysis. Furthermore, we introduce a new 3D multi-modal medical benchmark, M3D-Bench, which facilitates automatic evaluation across eight tasks. Through comprehensive evaluation, our method proves to be a robust model for 3D medical image analysis, outperforming existing solutions. All code, data, and models are publicly available at: https://github.com/BAAI-DCAI/M3D.

There is a lack of benchmarks for evaluating large language models (LLMs) in long-form medical question answering (QA). Most existing medical QA evaluation benchmarks focus on automatic metrics and multiple-choice questions. While valuable, these benchmarks fail to fully capture or assess the complexities of real-world clinical applications where LLMs are being deployed. Furthermore, existing studies on evaluating long-form answer generation in medical QA are primarily closed-source, lacking access to human medical expert annotations, which makes it difficult to reproduce results and enhance existing baselines. In this work, we introduce a new publicly available benchmark featuring real-world consumer medical questions with long-form answer evaluations annotated by medical doctors. We performed pairwise comparisons of responses from various open and closed-source medical and general-purpose LLMs based on criteria such as correctness, helpfulness, harmfulness, and bias. Additionally, we performed a comprehensive LLM-as-a-judge analysis to study the alignment between human judgments and LLMs. Our preliminary results highlight the strong potential of open LLMs in medical QA compared t

Contrastive Language-Image Pre-training (CLIP) has demonstrated outstanding performance in global image understanding and zero-shot transfer through large-scale text-image alignment. However, the core of medical image analysis often lies in the fine-grained understanding of specific anatomical structures or lesion regions. Therefore, precisely comprehending region-of-interest (RoI) information provided by medical professionals or perception models becomes crucial. To address this need, we propose MedP-CLIP, a region-aware medical vision-language model (VLM). MedP-CLIP innovatively integrates medical prior knowledge and designs a feature-level region prompt integration mechanism, enabling it to flexibly respond to various prompt forms (e.g., points, bounding boxes, masks) while maintaining global contextual awareness when focusing on local regions. We pre-train the model on a meticulously constructed large-scale dataset (containing over 6.4 million medical images and 97.3 million region-level annotations), equipping it with cross-disease and cross-modality fine-grained spatial semantic understanding capabilities. Experiments demonstrate that MedP-CLIP significantly outperforms basel

Access to diverse, well-annotated medical images with interactive learning tools is fundamental for training practitioners in medicine and related fields to improve their diagnostic skills and understanding of anatomical structures. While medical atlases are valuable, they are often impractical due to their size and lack of interactivity, whereas online image search may provide mislabeled or incomplete material. To address this, we propose MIRAGE, a multimodal medical text and image retrieval and generation system that allows users to find and generate clinically relevant images from trustworthy sources by mapping both text and images to a shared latent space, enabling semantically meaningful queries. The system is based on a fine-tuned medical version of CLIP (MedICaT-ROCO), trained with the ROCO dataset, obtained from PubMed Central. MIRAGE allows users to give prompts to retrieve images, generate synthetic ones through a medical diffusion model (Prompt2MedImage) and receive enriched descriptions from a large language model (Dolly-v2-3b). It also supports a dual search option, enabling the visual comparison of different medical conditions. A key advantage of the system is that it

Medical imaging systems such as CT, MRI, PET, and SPECT do not directly acquire images. Instead, they measure physical signals that encode anatomical or physiological information, and image reconstruction recovers the underlying image by solving an inverse problem. Although these imaging modalities are governed by different imaging physics, they share a common computational framework that naturally connects medical physics, linear algebra, probability, numerical optimization, and efficient computing. As medical imaging systems acquire increasingly large and higher-dimensional datasets, image reconstruction has become one of the primary computational bottlenecks in modern medical imaging. Advanced reconstruction methods, including analytical reconstruction, iterative optimization, and statistical model-based reconstruction, substantially improve image quality while reducing radiation dose or scan time, but at significantly increased computational cost. Efficient computing has therefore become essential for achieving clinically practical reconstruction times. This chapter presents a unified computational perspective on medical image acquisition and reconstruction across CT, MRI, PET,

The consideration of diversity, equity and inclusivity (DEI) is an important part of promoting a robust and respectful workforce, and is critical to the continued success of organisations, including healthcare providers, academic institutions and professional societies. Many professional bodies representing medical physicists have made commitments to DEI principles in the form of mission statements, policies, steering groups, frameworks and workforce surveys. In the Australian and New Zealand medical physics community, DEI work has included reflecting on the impact of stereotypes, surveys on workforce experiences, and capturing workforce diversity metrics (including gender, nationality, age and professional background). These projects have been conducted with the support of the Australasian College of Physical Sciences and Engineering in Medicine (ACPSEM) and have contributed to the enhancement of DEI in the ACPSEM workforce. Most of this work has been focused on gender diversity, reflecting increasing involvement in the Australian and New Zealand workforce: women accounted for 41\% of medical physics trainees and 32\% of registered medical physicists in a 2020 survey. In 2021, the

Vision-language foundation models (VLMs) have shown great potential in feature transfer and generalization across a wide spectrum of medical-related downstream tasks. However, fine-tuning these models is resource-intensive due to their large number of parameters. Prompt tuning has emerged as a viable solution to mitigate memory usage and reduce training time while maintaining competitive performance. Nevertheless, the challenge is that existing prompt tuning methods cannot precisely distinguish different kinds of medical concepts, which miss essentially specific disease-related features across various medical imaging modalities in medical image classification tasks. We find that Large Language Models (LLMs), trained on extensive text corpora, are particularly adept at providing this specialized medical knowledge. Motivated by this, we propose incorporating LLMs into the prompt tuning process. Specifically, we introduce the CILMP, Conditional Intervention of Large Language Models for Prompt Tuning, a method that bridges LLMs and VLMs to facilitate the transfer of medical knowledge into VLM prompts. CILMP extracts disease-specific representations from LLMs, intervenes within a low-ra

The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation due to its impressive capabilities in various segmentation tasks and its prompt-based interface. However, recent studies and individual experiments have shown that SAM underperforms in medical image segmentation, since the lack of the medical specific knowledge. This raises the question of how to enhance SAM's segmentation capability for medical images. In this paper, instead of fine-tuning the SAM model, we propose the Medical SAM Adapter (Med-SA), which incorporates domain-specific medical knowledge into the segmentation model using a light yet effective adaptation technique. In Med-SA, we propose Space-Depth Transpose (SD-Trans) to adapt 2D SAM to 3D medical images and Hyper-Prompting Adapter (HyP-Adpt) to achieve prompt-conditioned adaptation. We conduct comprehensive evaluation experiments on 17 medical image segmentation tasks across various image modalities. Med-SA outperforms several state-of-the-art (SOTA) medical image segmentation methods, while updating only 2\% of the parameters. Our code is released at https://github.com/KidsWithTokens/Medical-SAM-Adapter.

Large volumes of medical data remain underutilized because centralizing distributed data is often infeasible due to strict privacy regulations and institutional constraints. In addition, models trained in centralized settings frequently fail to generalize across clinical sites because of heterogeneity in imaging protocols and continuously evolving data distributions arising from differences in scanners, acquisition parameters, and patient populations. Federated learning offers a promising solution by enabling collaborative model training without sharing raw data. However, incorporating differential privacy into federated learning, while essential for privacy guarantees, often leads to degraded accuracy, unstable convergence, and reduced generalization. In this work, we propose an adaptive differentially private federated learning (ADP-FL) framework for medical image segmentation that dynamically adjusts privacy mechanisms to better balance the privacy-utility trade-off. The proposed approach stabilizes training, significantly improves Dice scores and segmentation boundary quality, and maintains rigorous privacy guarantees. We evaluated ADP-FL across diverse imaging modalities and s

Image-to-image translation is a common task in computer vision and has been rapidly increasing the impact on the field of medical imaging. Deep learning-based methods that employ conditional generative adversarial networks (cGANs), such as Pix2PixGAN, have been extensively explored to perform image-to-image translation tasks. However, when noisy medical image data are considered, such methods cannot be directly applied to produce clean images. Recently, an augmented GAN architecture named AmbientGAN has been proposed that can be trained on noisy measurement data to synthesize high-quality clean medical images. Inspired by AmbientGAN, in this work, we propose a new cGAN architecture, Ambient-Pix2PixGAN, for performing medical image-to-image translation tasks by use of noisy measurement data. Numerical studies that consider MRI-to-PET translation are conducted. Both traditional image quality metrics and task-based image quality metrics are employed to assess the proposed Ambient-Pix2PixGAN. It is demonstrated that our proposed Ambient-Pix2PixGAN can be successfully trained on noisy measurement data to produce high-quality translated images in target imaging modality.

Deep learning has achieved widespread success in medical image analysis, leading to an increasing demand for large-scale expert-annotated medical image datasets. Yet, the high cost of annotating medical images severely hampers the development of deep learning in this field. To reduce annotation costs, active learning aims to select the most informative samples for annotation and train high-performance models with as few labeled samples as possible. In this survey, we review the core methods of active learning, including the evaluation of informativeness and sampling strategy. For the first time, we provide a detailed summary of the integration of active learning with other label-efficient techniques, such as semi-supervised, self-supervised learning, and so on. We also summarize active learning works that are specifically tailored to medical image analysis. Additionally, we conduct a thorough comparative analysis of the performance of different AL methods in medical image analysis with experiments. In the end, we offer our perspectives on the future trends and challenges of active learning and its applications in medical image analysis.

Medical imaging is an essential tool for diagnosing and treating diseases. However, lacking medical images can lead to inaccurate diagnoses and ineffective treatments. Generative models offer a promising solution for addressing medical image shortage problems due to their ability to generate new data from existing datasets and detect anomalies in this data. Data augmentation with position augmentation methods like scaling, cropping, flipping, padding, rotation, and translation could lead to more overfitting in domains with little data, such as medical image data. This paper proposes the GAN-GA, a generative model optimized by embedding a genetic algorithm. The proposed model enhances image fidelity and diversity while preserving distinctive features. The proposed medical image synthesis approach improves the quality and fidelity of medical images, an essential aspect of image interpretation. To evaluate synthesized images: Frechet Inception Distance (FID) is used. The proposed GAN-GA model is tested by generating Acute lymphoblastic leukemia (ALL) medical images, an image dataset, and is the first time to be used in generative models. Our results were compared to those of InfoGAN a

Anomaly detection (AD) aims at detecting abnormal samples that deviate from the expected normal patterns. Generally, it can be trained merely on normal data, without a requirement for abnormal samples, and thereby plays an important role in rare disease recognition and health screening in the medical domain. Despite the emergence of numerous methods for medical AD, the lack of a fair and comprehensive evaluation causes ambiguous conclusions and hinders the development of this field. To address this problem, this paper builds a benchmark with unified comparison. Seven medical datasets with five image modalities, including chest X-rays, brain MRIs, retinal fundus images, dermatoscopic images, and histopathology images, are curated for extensive evaluation. Thirty typical AD methods, including reconstruction and self-supervised learning-based methods, are involved in comparison of image-level anomaly classification and pixel-level anomaly segmentation. Furthermore, for the first time, we systematically investigate the effect of key components in existing methods, revealing unresolved challenges and potential future directions. The datasets and code are available at https://github.com/

The increasing popularity of social media promotes the proliferation of fake news, which has caused significant negative societal effects. Therefore, fake news detection on social media has recently become an emerging research area of great concern. With the development of multimedia technology, fake news attempts to utilize multimedia content with images or videos to attract and mislead consumers for rapid dissemination, which makes visual content an important part of fake news. Despite the importance of visual content, our understanding of the role of visual content in fake news detection is still limited. This chapter presents a comprehensive review of the visual content in fake news, including the basic concepts, effective visual features, representative detection methods and challenging issues of multimedia fake news detection. This chapter can help readers to understand the role of visual content in fake news detection, and effectively utilize visual content to assist in detecting multimedia fake news.

Medical image segmentation is critical for clinical diagnosis, treatment planning, and monitoring, yet segmentation models often struggle with uncertainties stemming from occlusions, ambiguous boundaries, and variations in imaging devices. Traditional test-time augmentation (TTA) techniques typically rely on predefined geometric and photometric transformations, limiting their adaptability and effectiveness in complex medical scenarios. In this study, we introduced Test-Time Generative Augmentation (TTGA), a novel augmentation strategy specifically tailored for medical image segmentation at inference time. Different from conventional augmentation strategies that suffer from excessive randomness or limited flexibility, TTGA leverages a domain-fine-tuned generative model to produce contextually relevant and diverse augmentations tailored to the characteristics of each test image. Built upon diffusion model inversion, a masked null-text inversion method is proposed to enable region-specific augmentations during sampling. Furthermore, a dual denoising pathway is designed to balance precise identity preservation with controlled variability. We demonstrate the efficacy of our TTGA through

Determining whether two sets of images belong to the same or different distributions or domains is a crucial task in modern medical image analysis and deep learning; for example, to evaluate the output quality of image generative models. Currently, metrics used for this task either rely on the (potentially biased) choice of some downstream task, such as segmentation, or adopt task-independent perceptual metrics (e.g., Fréchet Inception Distance/FID) from natural imaging, which we show insufficiently capture anatomical features. To this end, we introduce a new perceptual metric tailored for medical images, FRD (Fréchet Radiomic Distance), which utilizes standardized, clinically meaningful, and interpretable image features. We show that FRD is superior to other image distribution metrics for a range of medical imaging applications, including out-of-domain (OOD) detection, the evaluation of image-to-image translation (by correlating more with downstream task performance as well as anatomical consistency and realism), and the evaluation of unconditional image generation. Moreover, FRD offers additional benefits such as stability and computational efficiency at low sample sizes, sensiti

Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of disparities in performance between subgroups. Since not all sources of biases in real-world medical imaging data are easily identifiable, it is challenging to comprehensively assess how those biases are encoded in models, and how capable bias mitigation methods are at ameliorating performance disparities. In this article, we introduce a novel analysis framework for systematically and objectively investigating the impact of biases in medical images on AI models. We developed and tested this framework for conducting controlled in silico trials to assess bias in medical imaging AI using a tool for generating synthetic magnetic resonance images with known disease effects and sources of bias. The feasibility is showcased by using three counterfactual bias scenarios to measure the impact of simulated bias effects on a convolutional neural network (CNN) classifier and the efficacy of three bias mitigation strategies. The analysis revealed that the simulated biases resulted in expected subgroup performance disparities when the CNN was trained on the synthetic datasets. More

Training segmentation models for medical images continues to be challenging due to the limited availability of data annotations. Segment Anything Model (SAM) is a foundation model that is intended to segment user-defined objects of interest in an interactive manner. While the performance on natural images is impressive, medical image domains pose their own set of challenges. Here, we perform an extensive evaluation of SAM's ability to segment medical images on a collection of 19 medical imaging datasets from various modalities and anatomies. We report the following findings: (1) SAM's performance based on single prompts highly varies depending on the dataset and the task, from IoU=0.1135 for spine MRI to IoU=0.8650 for hip X-ray. (2) Segmentation performance appears to be better for well-circumscribed objects with prompts with less ambiguity and poorer in various other scenarios such as the segmentation of brain tumors. (3) SAM performs notably better with box prompts than with point prompts. (4) SAM outperforms similar methods RITM, SimpleClick, and FocalClick in almost all single-point prompt settings. (5) When multiple-point prompts are provided iteratively, SAM's performance ge