自引率: 5%
被引量: 13799
通过率: 暂无数据
审稿周期: 1.33
版面费用: 暂无数据
国人发稿量: 35
投稿须知/期刊简介:
Modern Pathology provides a forum for the presentation of advances in the understanding of pathological processes. The practical complement to Laboratory Investigation, this journal provides authoritative, clinically oriented articles to keep clinical pathologists up-to-date on human diagnostic pathology. Through a careful selection and review process, the journal publishes the best papers covering the spectrum of applied pathology.
期刊描述简介:
Modern Pathology provides a forum for the presentation of advances in the understanding of pathological processes. The practical complement to Laboratory Investigation, this journal provides authoritative, clinically oriented articles to keep clinical pathologists up-to-date on human diagnostic pathology. Through a careful selection and review process, the journal publishes the best papers covering the spectrum of applied pathology.
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Unraveling the Molecular Landscape of Uterine Tumor Resembling Ovarian Sex Cord Tumor: Insights From A Clinicopathological, Morphologic, Immunohistochemical, and Molecular Analysis of 35 Cases.
被引量：- 发表：1970
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MYC Rearrangement Prediction From LYSA Whole Slide Images in Large B-Cell Lymphoma: A Multicentric Validation of Self-supervised Deep Learning Models.
被引量：- 发表：1970
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Abnormal p53 Immunohistochemical Patterns Are Associated with Regional Lymph Node Metastasis in Oral Cavity Squamous Cell Carcinoma at Time of Surgery.
被引量：- 发表：1970
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p53 Abnormal Oral Epithelial Dysplasias are Associated With High Risks of Progression and Local Recurrence-A Retrospective Study in a Longitudinal Cohort.
被引量：1 发表：1970
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Regulatory Aspects of Artificial Intelligence and Machine Learning.
In the realm of health care, numerous generative and nongenerative artificial intelligence and machine learning (AI-ML) tools have been developed and deployed. Simultaneously, manufacturers of medical devices are leveraging AI-ML. However, the adoption of AI in health care raises several concerns, including safety, security, ethical biases, accountability, trust, economic impact, and environmental effects. Effective regulation can mitigate some of these risks, promote fairness, establish standards, and advocate for more sustainable AI practices. Regulating AI tools not only ensures their safe and effective adoption but also fosters public trust. It is important that regulations remain flexible to accommodate rapid advances in this field to support innovation and also not to add additional burden to some of our preexisting and well-established frameworks. This study covers regional and global regulatory aspects of AI-ML including data privacy, software as a medical device, agency approval and clearance pathways, reimbursement, and laboratory-developed tests.
被引量：1 发表：1970
