搜索结果：Forensic science, medicine, and pathology

CONTENTS: Accident Investigation (a) Aircraft. Accident Investigation (b) Motor vehicle (including biomechanics of injuries). Accident Investigation (c) Rail. Accident Investigation (d) Reconstruction. Accident Investigation (e) Airbag related injuries and deaths. Accident Investigation (f) Determination of cause. Accident Investigation (g) Driver versus passenger in motor vehicle collisions. Accident Investigation (h) Tachographs. Accreditation of Forensic Science Laboratories. Administration of Forensic Science (a) An international perspective. Administration of Forensic Science (b) Organisation of laboratories. Alcohol (a) Blood. Alcohol (b) Body fluids. Alcohol (c) Breath. Alcohol (d) Post-mortem. Alcohol (e) Interpretation. CONTENTS: Alcohol (f) Congener analysis. Analytical Techniques (a) Separation techniques. Analytical Techniques (b) Microscopy. Analytical Techniques (c) Spectroscopy. Analytical Techniques (d) Mass spectrometry. Anthropology: Archaeology. Anthropology: Skeletal Analysis (a) Overview. Anthropology: Skeletal Analysis (b) Morphological age estimation. Anthropology: Skeletal Analysis (c) Sex determination. Anthropology: Skeletal Analysis (d) Determination of racial affinity. Anthropology: Skeletal Analysis (e) Excavation/retrieval of forensic remains. Anthropology: Skeletal Analysis (f) Bone pathology and ante-mortem trauma in forensic cases. Anthropology: Skeletal Analysis (g) Skeletal trauma. Anthropology: Skeletal Analysis (h) Animal effects on human remains. Anthropology: Skeletal Analysis (i) Assessment of occupational stress. Anthropology: Skeletal Analysis (j) Stature estimation from the skeleton. Art and Antique Forgery and Fraud. Autoerotic Death. Basic Principles of Forensic Science. Biochemical Analysis (a) Capillary electrophoresis in forensic science. Biochemical Analysis (b) Capillary electrophoresis in forensic biology. Blood Identification. Blood Stain Pattern Analysis and Interpretation. Causes of Death (a) Post-mortem changes. Causes of Death (b) Sudden natural death. Causes of Death (c) Blunt injury. Causes of Death (d) Sharp injury. Causes of Death (e) Gunshot wounds. Causes of Death (f) Asphyctic deaths. Causes of Death (g) Burns and scalds. Causes of Death (h) Traffic deaths. Causes of Death (i) Systemic response to trauma. Causes of Death (j) Poisonings. Cheiloscopy. Clinical Forensic Medicine (a) Overview. Clinical Forensic Medicine (b) Defence wounds. Clinical Forensic Medicine (c) Self-inflicted injury. Clinical Forensic Medicine (d) Child abuse. Clinical Forensic Medicine (e) Sexual assault and semen persistence. Clinical Forensic Medicine (f) Evaluation of gunshot wounds. Clinical Forensic Medicine (g) Recognition of pattern injuries in domestic violence victims. Computer Crime. Credit Cards: Forgery and Fraud. Crime-Scene Investigation and Examination (a) Recording. Crime-Scene Investigation and Examination (b) Collection and chain of evidence. Crime-Scene Investigation and Examination (c) Recovery. Crime-Scene Investigation and Examination (d) Packaging. Crime-Scene Investigation and Examination (e) Preservation. Crime-Scene Investigation and Examination (f) Contamination. Crime-Scene Investigation and Examination (g) Fingerprints. Crime-Scene Investigation and Examination (h) Suspicious deaths. Crime-Scene Investigation and Examination (i) Major incident scene management. Crime-Scene Investigation and Examination (j) Serial and series crimes. Crime-Scene Investigation and Examination (k) Scene analysis/reconstruction. Crime-Scene Investigation and Examination (l) Criminal analysis. Crime-Scene Investigation and Examination (m) Decomposing and skeletonized cases. Criminal Profiling. Criminalistics. Detection of Deception. Disaster Victim Identification. DNA (a) Basic principles. DNA (b) RFLP. DNA (c) PCR. DNA (d) PCR-STR. DNA (e) Future analytical techniques. DNA (f) Paternity testing. DNA (g) Significance. DNA (h) Mitochondrial. Document Analysis (a) Handwriting. Document Analysis (b) Analytical methods. Document Analysis (c) Forgery and counterfeits. Document Analysis (d) Ink analysis. Document Analysis (e) Printer types. Document Analysis (f) Document dating. Drugs of Abuse (a) Blood. Drugs of Abuse (b) Body fluids. Drugs of Abuse (c) Ante-mortem. Drugs of Abuse (d) Post-mortem. Drugs of Abuse (e) Drugs and driving. Drugs of Abuse (f) Urine. Drugs of Abuse (g) Hair. Drugs of Abuse (h) Methods of analysis. Drugs of Abuse (i) Designer drugs. Dust. Ear Prints. Education, An International Perspective. Electronic Communication and Information. Entomology. Ethics. Evidence (a) Classification of evidence. Evidence (b)The philosophy of sequential analysis. Evidence (c) Statistical interpretation of evidence/Bayesian analysis. Expert Witnesses, Qualifications and Testimony. Explosives, Methods of Analysis. Facial Identification (a) The lineup, mugshot search and composite. Facial Identification (b) Photo image identification. Facial Identification (c) Computerized facial reconstruction. Facial Identification (d) Skull-photo superimposition. Facial Identification (e) Facial tissue thickness in facial reconstruction. Fibres (a) Types. Fibres (b) Transfer and persistence. Fibres (c) Recovery. Fibres (d) Identification and comparison. Fibres (e) Significance. Fingerprints (Dactyloscopy) (a) Visualisation. Fingerprints (Dactyloscopy) (b) Sequential treatment and enhancement. Fingerprints (Dactyloscopy) (c) Identification and classification. Fingerprints (Dactyloscopy) (d) Standards of proof. Fingerprints (Dactyloscopy) (e) Chemistry of print residue. Fire Investigation (a) Types of fire. Fire Investigation (b) Physics/Thermodynamics. Fire Investigation (c) Chemistry of fire. Fire Investigation (d) The fire scene. Fire Investigation (e) Evidence recovery. Fire Investigation (f) Fire scene patterns. Fire Investigation (g) The laboratory. Firearms (a) Types of weapons and ammunitions. Firearms (b) Range and penetration. Firearms (c) CS Gas. Firearms (df) Humane killing tools. Firearms (e) Laboratory analysis. Forensic Anthropology. Forensic Engineering. Forensic Nursing. Forensic Psycholinguistics. Forensic Toxicology (a) Overview. Forensic Toxicology (b) Methods of analysis - ante-mortem. Forensic Toxicology (c) Methods of analysis - post-mortem. Forensic Toxicology (d) Interpretation of results. Forensic Toxicology (e) Inhalants. Forensic Toxicology (f) Equine drug testing. Forgery and Fraud (a) Overview (including counterfeit currency). Forgery and Fraud (b) Auditing and accountancy. Gas Chromatography, Methodology in Forensic Sciences. Genetics (a) Serology. Genetics (b) DNA - statistical probability. Glass. Hair (a) Background. Hair (b) Hair transfer, persistence and recovery. Hair (c) Identification of human and animal hair. Hair (d) Microscopic comparison. Hair (e) Other comparison methods. Hair (f) Significance of hair evidence. Hair (g) DNA typing. Health and Safety (including Risk Assessment). History (a) Crime scene sciences. History (b) Fingerprint sciences. Identification/Individualization, Overview and Meaning. Investigative Psychology. Legal Aspects of Forensic Science. Lie Detection (Polygraph). Literature and the Forensic Sciences (a) Resources. Literature and the Forensic Sciences (b) Fiction. Microchemistry. Modus Operandi. Odontology. Offender Signature. Paints and Coatings: Commercial, Domestic and Automotive. Pathology (a) Overview. Pathology (b) Victim recovery. Pathology (c) Autopsy. Pathology (d) Preservation of evidence. Pathology (e) Post-mortem changes. Pathology (f) Post-mortem interval. Pattern Evidence (a) Footmarks (footwear). Pattern Evidence (b) Footmarks (bare footprints). Pattern Evidence (c) Shotgun ammunition on a target. Pattern Evidence (d) Tools. Pattern Evidence (e) Plastic bag striations. Pattern Evidence (f) Serial number. Pharmacology. Post-Mortem Examination, Procedures and Standards. Psychological Autopsies. Psychology and Psychiatry (a) Overview. Psychology and Psychiatry (b) Psychiatry. Psychology and Psychiatry (c) Psychology. Quality Assurance/Control. Serial Killing. Soil and Geology. Stalking. Statistical Interpretation of Evidence. Time Factor Analysis. Voice Analysis. Wildlife. Wood Analysis.

Part I. Two Sciences, One Objective Introduction to Forensic Anthropology Douglas H. Ubelaker Introduction to Forensic Medicine and Pathology Joao Pinheiro Forensic Anthropology and Forensic Pathology: The State of the Art Eugenia Cunha and Cristina Cattaneo Part II. Aging Living Young Individuals Biological vs Legal Age of Living Individuals Francesco Introna and Carlo P. Campobasso Part III. Pathophysiology of Death and Forensic Investigation: From Recovery to the Cause of Death Decay Process of a Cadaver Joao Pinheiro Understanding the Circumstances of Decomposition When the Body Is Skeletonized Henri Duday and Mark Guillon Forensic Investigation of Corpses in Various States of Decomposition: A Multidisciplinary Approach Joao Pinheiro and Eugenia Cunha Identification and Differential Diagnosis of Traumatic Lesions of the Skeleton Conrado Rodriguez-Martin Part IV. Biological Identity Methodology and Reliability of Sex Determination From the Skeleton Jaroslav Bruzek and Pascal Murail Age Assessment of Child Skeletal Remains in Forensic Contexts Mary E. Lewis and Ambika Flavel Determination of Adult Age at Death in the Forensic Context Eric Baccino and Aurore Schmitt Is It Possible to Escape Racial Typology in Forensic Identification? John Albanese and Shelley R. Saunders Estimation and Evidence in Forensic Anthropology: Determining Stature Lyle W. Konigsberg, Ann H. Ross, and William L. Jungers Pathology as a Factor of Personal Identity in Forensic Anthropology Eugenia Cunha Personal Identification of Cadavers and Human Remains Cristina Cattaneo, Danilo De Angelis, Davide Porta, and Marco Grandi Part V. Particular Contexts: Crimes Against Humanity and MassDisasters Forensic Investigations Into the Missing: Recommendations and Operational Best Practices Morris Tidball-Binz Crimes Against Humanity Dario M. Olmo Mass Disasters Cristina Cattaneo, Danilo De Angelis, and Marco Grandi Index

Pathology, a cornerstone of medical diagnostics and research, is undergoing a revolutionary transformation fueled by digital technology, molecular biology advancements, and big data analytics. Digital pathology converts conventional glass slides into high-resolution digital images, enhancing collaboration and efficiency among pathologists worldwide. Integrating artificial intelligence (AI) and machine learning (ML) algorithms with digital pathology improves diagnostic accuracy, particularly in complex diseases like cancer. Molecular pathology, facilitated by next-generation sequencing (NGS), provides comprehensive genomic, transcriptomic, and proteomic insights into disease mechanisms, guiding personalized therapies. Immunohistochemistry (IHC) plays a pivotal role in biomarker discovery, refining disease classification and prognostication. Precision medicine integrates pathology's molecular findings with individual genetic, environmental, and lifestyle factors to customize treatment strategies, optimizing patient outcomes. Telepathology extends diagnostic services to underserved areas through remote digital pathology. Pathomics leverages big data analytics to extract meaningful insights from pathology images, advancing our understanding of disease pathology and therapeutic targets. Virtual autopsies employ non-invasive imaging technologies to revolutionize forensic pathology. These innovations promise earlier diagnoses, tailored treatments, and enhanced patient care. Collaboration across disciplines is essential to fully realize the transformative potential of these advancements in medical practice and research.

In the dynamic landscape of digital forensics, the integration of Artificial Intelligence (AI) and Machine Learning (ML) stands as a transformative technology, poised to amplify the efficiency and precision of digital forensics investigations. However, the use of ML and AI in digital forensics is still in its nascent stages. As a result, this paper gives a thorough and in-depth analysis that goes beyond a simple survey and review. The goal is to look closely at how AI and ML techniques are used in digital forensics and incident response. This research explores cutting-edge research initiatives that cross domains such as data collection and recovery, the intricate reconstruction of cybercrime timelines, robust big data analysis, pattern recognition, safeguarding the chain of custody, and orchestrating responsive strategies to hacking incidents. This endeavour digs far beneath the surface to unearth the intricate ways AI-driven methodologies are shaping these crucial facets of digital forensics practice. While the promise of AI in digital forensics is evident, the challenges arising from increasing database sizes and evolving criminal tactics necessitate ongoing collaborative researc

The Go programming language has become increasingly popular among malware developers due to its ability to produce statically linked, cross-platform executables that challenge traditional analysis techniques. These binaries embed a substantial runtime and compiler-generated metadata and are compiled with aggressive optimizations that discard type information for function parameters and local variables. Go's design further complicates analysis by representing strings as pointer-length pairs rather than null-terminated sequences, employing a caller-allocated stack model that obscures argument boundaries, and fragmenting program state across concurrent goroutines. Although existing static analysis and reverse engineering tools provide Go-specific support, they remain limited to compile-time artifacts and cannot recover runtime execution state and artifacts that persist solely in memory. To address this gap, we present the first memory forensics framework for runtime analysis of Go binaries. By parsing Go's internal structures, our framework reconstructs type and function metadata, recovers heap-allocated and static strings, and distinguishes application-level functions. Through ABI-aw

Cybercrime and the market for cyber-related compromises are becoming attractive revenue sources for state-sponsored actors, cybercriminals and technical individuals affected by financial hardships. Due to burgeoning cybercrime on new technological frontiers, efforts have been made to assist digital forensic investigators (DFI) and law enforcement agencies (LEA) in their investigative efforts. Forensic tool innovations and ontology developments, such as the Unified Cyber Ontology (UCO) and Cyber-investigation Analysis Standard Expression (CASE), have been proposed to assist DFI and LEA. Although these tools and ontologies are useful, they lack extensive information sharing and tool interoperability features, and the ontologies lack the latest Smart City Infrastructure (SCI) context that was proposed. To mitigate the weaknesses in both solutions and to ensure a safer cyber-physical environment for all, we propose the Smart City Ontological Paradigm Expression (SCOPE), an expansion profile of the UCO and CASE ontology that implements SCI threat models, SCI digital forensic evidence, attack techniques, patterns and classifications from MITRE. We showcase how SCOPE could present complex

BACKGROUND: The integration of artificial intelligence (AI) into various fields has ushered in a new era of multidisciplinary progress. Defined as the ability of a system to interpret external data, learn from it, and adapt to specific tasks, AI is poised to revolutionize the world. In forensic medicine and pathology, algorithms play a crucial role in data analysis, pattern recognition, anomaly identification, and decision making. This review explores the diverse applications of AI in forensic medicine, encompassing fields such as forensic identification, ballistics, traumatic injuries, postmortem interval estimation, forensic toxicology, and more. RESULTS: A thorough review of 113 articles revealed a subset of 32 papers directly relevant to the research, covering a wide range of applications. These included forensic identification, ballistics and additional factors of shooting, traumatic injuries, post-mortem interval estimation, forensic toxicology, sexual assaults/rape, crime scene reconstruction, virtual autopsy, and medical act quality evaluation. The studies demonstrated the feasibility and advantages of employing AI technology in various facets of forensic medicine and pathology. CONCLUSIONS: The integration of AI in forensic medicine and pathology offers promising prospects for improving accuracy and efficiency in medico-legal practices. From forensic identification to post-mortem interval estimation, AI algorithms have shown the potential to reduce human subjectivity, mitigate errors, and provide cost-effective solutions. While challenges surrounding ethical considerations, data security, and algorithmic correctness persist, continued research and technological advancements hold the key to realizing the full potential of AI in forensic applications. As the field of AI continues to evolve, it is poised to play an increasingly pivotal role in the future of forensic medicine and pathology.

Forensic nurse examiners (FNE) are becoming integral partners in contemporary medicolegal systems worldwide. Existing forensic services have been proven inadequate to sufficiently address the vast crimes against women and children, victims of sexual and domestic violence, sociocultural crimes, abusive religious rituals, and atrocities that accompany armed conflict. Considering that nurses comprise the largest group of healthcare providers worldwide, forensic nurse examiners represent a previously unrecognized resource in universal healthcare and embody an ideal group to advance international considerations in global healthcare and social justice. Although specific legal concerns within the healthcare communities vary from country to country, all nations struggle with issues of public health and safety. A comprehensive multidisciplinary forensic education and training program for nurses will facilitate improved management of existing interpersonal and sexual violence crises while reducing an unnecessary back log of cases for forensic physicians. The addition of a forensic specialist in nursing science will provide a valuable resource to assist in the substantiation of prosecutors’ claims or aid in the exoneration of suspects who are falsely accused. Their unique contributions increase coordination and cooperation, share medical/forensic expertise, enhance the care of victims of crimes while augmenting forensic services, and act as a liaison in applicable responsibilities between healthcare institutions and law enforcement agencies. The relevant literature indicates that once the Forensic Nurse Examiner Response Team is trained, specialists in forensic nursing science practice independently under the auspices of a Director of Clinical Forensic Medicine or Chief Medical Examiner. This new generation of health and justice professionals will produce affirmative outcomes where the science of forensic nursing is practiced. The positive treatment of victims of gender based crime, an increase in successful prosecution, and the assurance of best specimens in evidence recovery will provide confidence in the community at large that justice has been served through these combined forensic services...medicine, nursing and the law.

The advancement of technology and its developments have provided the forensic sciences with many cutting-edge tools, devices, and applications, allowing forensics a better and more accurate understanding of the crime scene, a better and optimal acquisition of data and information, and faster processing, allowing more reliable conclusions to be obtained and substantially improving the scientific investigation of crime. This article describes the technological advances, their impacts, and the challenges faced by forensic specialists in using and implementing these technologies as tools to strengthen their field and laboratory investigations. The systematic review of the scientific literature used the PRISMA® methodology, analyzing documents from databases such as SCOPUS, Web of Science, Taylor & Francis, PubMed, and ProQuest. Studies were selected using a Cohen Kappa coefficient of 0.463. In total, 63 reference articles were selected. The impact of technology on investigations by forensic science experts presents great benefits, such as a greater possibility of digitizing the crime scene, allowing remote analysis through extended reality technologies, improvements in the accuracy and identification of biometric characteristics, portable equipment for on-site analysis, and Internet of things devices that use artificial intelligence and machine learning techniques. These alternatives improve forensic investigations without diminishing the investigator’s prominence and responsibility in the resolution of cases.

Computational Pathology CPath is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges fac

Forensic pathology is critical in determining the cause and manner of death through post-mortem examinations, both macroscopic and microscopic. The field, however, grapples with issues such as outcome variability, laborious processes, and a scarcity of trained professionals. This paper presents SongCi, an innovative visual-language model (VLM) designed specifically for forensic pathology. SongCi utilizes advanced prototypical cross-modal self-supervised contrastive learning to enhance the accuracy, efficiency, and generalizability of forensic analyses. It was pre-trained and evaluated on a comprehensive multi-center dataset, which includes over 16 million high-resolution image patches, 2,228 vision-language pairs of post-mortem whole slide images (WSIs), and corresponding gross key findings, along with 471 distinct diagnostic outcomes. Our findings indicate that SongCi surpasses existing multi-modal AI models in many forensic pathology tasks, performs comparably to experienced forensic pathologists and significantly better than less experienced ones, and provides detailed multi-modal explainability, offering critical assistance in forensic investigations. To the best of our knowled

Large language models (LLMs) have seen widespread adoption in many domains including digital forensics. While prior research has largely centered on case studies and examples demonstrating how LLMs can assist forensic investigations, deeper explorations remain limited, i.e., a standardized approach for precise performance evaluations is lacking. Inspired by the NIST Computer Forensic Tool Testing Program, this paper proposes a standardized methodology to quantitatively evaluate the application of LLMs for digital forensic tasks, specifically in timeline analysis. The paper describes the components of the methodology, including the dataset, timeline generation, and ground truth development. Additionally, the paper recommends using BLEU and ROUGE metrics for the quantitative evaluation of LLMs through case studies or tasks involving timeline analysis. Experimental results using ChatGPT demonstrate that the proposed methodology can effectively evaluate LLM-based forensic timeline analysis. Finally, we discuss the limitations of applying LLMs to forensic timeline analysis.

A number of initiatives are underway in the United States in response to the 2009 critique of forensic science by a National Academy of Sciences committee. This article provides a broad review of activities including efforts of the White House National Science and Technology Council Subcommittee on Forensic Science and a partnership between the Department of Justice (DOJ) and the National Institute of Standards and Technology (NIST) to create the National Commission on Forensic Science and the Organization of Scientific Area Committees. These initiatives are seeking to improve policies and practices of forensic science. Efforts to fund research activities and aid technology transition and training in forensic science are also covered. The second portion of the article reviews standards in place or in development around the world for forensic DNA. Documentary standards are used to help define written procedures to perform testing. Physical standards serve as reference materials for calibration and traceability purposes when testing is performed. Both documentary and physical standards enable reliable data comparison, and standard data formats and common markers or testing regions are crucial for effective data sharing. Core DNA markers provide a common framework and currency for constructing DNA databases with compatible data. Recent developments in expanding core DNA markers in Europe and the United States are discussed.

Detection engineering and digital forensics have evolved in parallel rather than in partnership, leaving a gap between real-time alerting and forensic analysis. This paper develops a unified detection-forensics methodology using Velociraptor, where detection logic directly initiates targeted evidence acquisition at the point of detection. The contribution is threefold: (1) a four-stage methodology (baseline establishment, evidence correlation, attack chain analysis, and scenario labelling with confidence) that converts artefact knowledge into reusable and testable detection rules suitable for both post-incident triage and live monitoring; (2) a practical demonstration, using three Velociraptor BaseVQL log sources (/forensics/windows/prefetch, /forensics/windows/usn, and /windows/wmi) that practitioners can deploy today, showing that artefact-based detections enable scalable forensic triage without full disk acquisition; and (3) evidence that periodic artefact analysis offers continuous monitoring while substantially reducing data volume compared to conventional endpoint logging. Two case studies illustrate the approach: a Prefetch/USN baseline for triage when Windows Event Logs are

Over the past decade, artificial intelligence (AI) methods in pathology have advanced substantially. However, integration into routine clinical practice has been slow due to numerous challenges, including technical and regulatory hurdles in translating research results into clinical diagnostic products and the lack of standardized interfaces. The open and vendor-neutral EMPAIA initiative addresses these challenges. Here, we provide an overview of EMPAIA's achievements and lessons learned. EMPAIA integrates various stakeholders of the pathology AI ecosystem, i.e., pathologists, computer scientists, and industry. In close collaboration, we developed technical interoperability standards, recommendations for AI testing and product development, and explainability methods. We implemented the modular and open-source EMPAIA platform and successfully integrated 14 AI-based image analysis apps from 8 different vendors, demonstrating how different apps can use a single standardized interface. We prioritized requirements and evaluated the use of AI in real clinical settings with 14 different pathology laboratories in Europe and Asia. In addition to technical developments, we created a forum fo

Diagnoses in forensic science cover many disciplinary and technical fields, including thanatology and clinical forensic medicine, as well as all the disciplines mobilized by these two major poles: criminalistics, ballistics, anthropology, entomology, genetics, etc. A diagnosis covers three major interrelated concepts: a categorization of pathologies (the diagnosis); a space of signs or symptoms; and the operation that makes it possible to match a set of signs to a category (the diagnostic approach). The generalization of digitization in all sectors of activity-including forensic science, the acculturation of our societies to data and digital devices, and the development of computing, storage, and data analysis capacities-constitutes a favorable context for the increasing adoption of artificial intelligence (AI). AI can intervene in the three terms of diagnosis: in the space of pathological categories, in the space of signs, and finally in the operation of matching between the two spaces. Its intervention can take several forms: it can improve the performance (accuracy, reliability, robustness, speed, etc.) of the diagnostic approach, better define or separate known diagnostic categories, or better associate known signs. But it can also bring new elements, beyond the mere improvement of performance: AI takes advantage of any data (data here extending the concept of symptoms and classic signs, coming either from the five senses of the human observer, amplified or not by technical means, or from complementary examination tools, such as imaging). Through its ability to associate varied and large-volume data sources, but also its ability to uncover unsuspected associations, AI may redefine diagnostic categories, use new signs, and implement new diagnostic approaches. We present in this article how AI is already mobilized in forensic science, according to an approach that focuses primarily on improving current techniques. We also look at the issues related to its generalization, the obstacles to its development and adoption, and the risks related to the use of AI in forensic diagnostics.

Agentic Al systems are increasingly deployed as personal assistants and are likely to become a common object of digital investigations. However, little is known about how their internal state and actions can be reconstructed during forensic analysis. Despite growing popularity, systematic forensic approaches for such systems remain largely unexplored. This paper presents an empirical study of OpenClaw a widely used single-agent assistant. We examine OpenClaw's technical design via static code analysis and apply differential forensic analysis to identify recoverable traces across stages of the agent interaction loop. We classify and correlate these traces to assess their investigative value in a systematic way. Based on these observations, we propose an agent artifact taxonomy that captures recurring investigative patterns. Finally, we highlight a foundational challenge for agentic Al forensics: agent-mediated execution introduces an additional layer of abstraction and substantial nondeterminism in trace generation. The large language model (LLM), the execution environment, and the evolving context can influence tool choice and state transitions in ways that are largely absent from

Current medical school curricula predominantly facilitate early integration of basic science principles into clinical practice to strengthen diagnostic skills and the ability to make treatment decisions. In addition, they promote life-long learning and understanding of the principles of medical practice. The Pathology Competencies for Medical Education (PCME) were developed in response to a call to action by pathology course directors nationwide to teach medical students pathology principles necessary for the practice of medicine. The PCME are divided into three competencies: 1) Disease Mechanisms and Processes, 2) Organ System Pathology, and 3) Diagnostic Medicine and Therapeutic Pathology. Each of these competencies is broad and contains multiple learning goals with more specific learning objectives. The original competencies were designed to be a living document, meaning that they will be revised and updated periodically, and have undergone their first revision with this publication. The development of teaching cases, which have a classic case-based design, for the learning objectives is the next step in providing educational content that is peer-reviewed and readily accessible for pathology course directors, medical educators, and medical students. Application of the PCME and cases promotes a minimum standard of exposure of the undifferentiated medical student to pathophysiologic principles. The publication of the PCME and the educational cases will create a current educational resource and repository published through Academic Pathology.

Current watermark removal methods are evaluated on two axes: attack success rate and perceptual quality. We show this is insufficient. While state-of-the-art attacks successfully degrade the watermark signal without visible distortion, they leave distinct statistical artifacts that betray the removal attempt. We name this overlooked axis Watermark Removal Detection (WRD) and demonstrate that a modern classifier trained on these artifacts achieves state-of-the-art detection rates at $10^{-3}$ FPR across every removal method tested. No existing attack accounts for this forensic leakage. We benchmark leading watermarking schemes against standard removal pipelines under the extended evaluation triple of attack success, perceptual quality, and forensic detectability, and find that no current method balances all three. Our results establish forensic stealthiness as a necessary requirement for watermark removal.