Institution
Charité
Healthcare•Berlin, Germany•
About: Charité is a healthcare organization based out in Berlin, Germany. It is known for research contribution in the topics: Population & Transplantation. The organization has 30624 authors who have published 64507 publications receiving 2437322 citations. The organization is also known as: Charite & Charité – University Medicine Berlin.
Topics: Population, Transplantation, Medicine, Cancer, Immune system
Papers published on a yearly basis
Papers
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QIMR Berghofer Medical Research Institute1, University Medical Center Groningen2, University of Kiel3, Charité4, Max Delbrück Center for Molecular Medicine5, University of California, San Francisco6, University of Amsterdam7, Karolinska Institutet8, University of Bristol9, Norwegian University of Science and Technology10, University of Melbourne11, Stanford University12, University of Michigan13, Greifswald University Hospital14, University of Greifswald15, Harry Perkins Institute of Medical Research16, University Hospital Bonn17, Ludwig Maximilian University of Munich18, Kaiser Permanente19, VU University Medical Center20, Karolinska University Hospital21, GlaxoSmithKline22
TL;DR: A genome-wide association study of a broad allergic disease phenotype that considers the presence of any one of these three diseases identified 136 independent risk variants, including 73 not previously reported, which implicate 132 nearby genes in allergic disease pathophysiology.
Abstract: Asthma, hay fever (or allergic rhinitis) and eczema (or atopic dermatitis) often coexist in the same individuals, partly because of a shared genetic origin. To identify shared risk variants, we performed a genome-wide association study (GWAS; n = 360,838) of a broad allergic disease phenotype that considers the presence of any one of these three diseases. We identified 136 independent risk variants (P < 3 × 10-8), including 73 not previously reported, which implicate 132 nearby genes in allergic disease pathophysiology. Disease-specific effects were detected for only six variants, confirming that most represent shared risk factors. Tissue-specific heritability and biological process enrichment analyses suggest that shared risk variants influence lymphocyte-mediated immunity. Six target genes provide an opportunity for drug repositioning, while for 36 genes CpG methylation was found to influence transcription independently of genetic effects. Asthma, hay fever and eczema partly coexist because they share many genetic risk variants that dysregulate the expression of immune-related genes.
378 citations
TL;DR: A composite atlas based on manual segmentations of a multimodal high resolution brain template, histology and structural connectivity is presented that can be used to segment DBS targets in single subjects, yielding more accurate results compared to priorly published atlases.
378 citations
University of Paris1, Paris Descartes University2, Charité3, King Saud University4, Harvard University5, University of Manchester6, Kyushu University7, Vanderbilt University8, University of Freiburg9, University of Debrecen10, Alfaisal University11, University of South Florida12, National Institutes of Health13, University of Oxford14, University of British Columbia15, University of Toronto16, Boston Children's Hospital17, National Defense Medical College18, State University of New York Upstate Medical University19, Dalhousie University20, Royal Children's Hospital21, François Rabelais University22, Cambridge University Hospitals NHS Foundation Trust23, University College London24, Istanbul University25, Autonomous University of Barcelona26, University of Barcelona27, University of Zurich28, Medical College of Wisconsin29, Katholieke Universiteit Leuven30
TL;DR: IRAK-4 and MyD88 deficiencies predispose patients to recurrent life-threatening bacterial diseases, such as invasive pneumococcal disease in particular, in infancy and early childhood, with weak signs of inflammation.
377 citations
TL;DR: There is a need to sensitize developers, healthcare professionals, and legislators to the challenges and limitations of opaque algorithms in medical AI and to foster multidisciplinary collaboration moving forward to ensure that medical AI lives up to its promises.
Abstract: Explainability is one of the most heavily debated topics when it comes to the application of artificial intelligence (AI) in healthcare. Even though AI-driven systems have been shown to outperform humans in certain analytical tasks, the lack of explainability continues to spark criticism. Yet, explainability is not a purely technological issue, instead it invokes a host of medical, legal, ethical, and societal questions that require thorough exploration. This paper provides a comprehensive assessment of the role of explainability in medical AI and makes an ethical evaluation of what explainability means for the adoption of AI-driven tools into clinical practice. Taking AI-based clinical decision support systems as a case in point, we adopted a multidisciplinary approach to analyze the relevance of explainability for medical AI from the technological, legal, medical, and patient perspectives. Drawing on the findings of this conceptual analysis, we then conducted an ethical assessment using the “Principles of Biomedical Ethics” by Beauchamp and Childress (autonomy, beneficence, nonmaleficence, and justice) as an analytical framework to determine the need for explainability in medical AI. Each of the domains highlights a different set of core considerations and values that are relevant for understanding the role of explainability in clinical practice. From the technological point of view, explainability has to be considered both in terms how it can be achieved and what is beneficial from a development perspective. When looking at the legal perspective we identified informed consent, certification and approval as medical devices, and liability as core touchpoints for explainability. Both the medical and patient perspectives emphasize the importance of considering the interplay between human actors and medical AI. We conclude that omitting explainability in clinical decision support systems poses a threat to core ethical values in medicine and may have detrimental consequences for individual and public health. To ensure that medical AI lives up to its promises, there is a need to sensitize developers, healthcare professionals, and legislators to the challenges and limitations of opaque algorithms in medical AI and to foster multidisciplinary collaboration moving forward.
377 citations
St. Agnes Hospital1, University of North Dakota2, MedStar Washington Hospital Center3, Wake Forest University4, University of Maryland, Baltimore5, University of Pittsburgh6, Sharp HealthCare7, Autonomous University of Barcelona8, Kaiser Permanente9, St George's Hospital10, Louisiana State University in Shreveport11, Institut Gustave Roussy12, Creighton University13, University of Lyon14, Complutense University of Madrid15, University of Texas MD Anderson Cancer Center16, Mercy Medical Center (Baltimore, Maryland)17, Charité18, Rutgers University19, Roswell Park Cancer Institute20, University of South Florida21, Baylor University22, Johns Hopkins University23, Uppsala University24, University of Washington25, University of Louisville26, Christiana Care Health System27, National Institutes of Health28, University of Regensburg29, Mount Sinai St. Luke's and Mount Sinai Roosevelt30, Maine Medical Center31, Miami Valley Hospital32, Tel Aviv Sourasky Medical Center33, Walter Reed Army Institute of Research34, Netherlands Cancer Institute35, University of Iowa36, Ben-Gurion University of the Negev37
TL;DR: Cytoreductive surgery and hyperthermic intraperitoneal chemotherapy in the management of peritoneal surface malignancies of colonic origin : a consensus statement.
Abstract: Cytoreductive surgery and hyperthermic intraperitoneal chemotherapy in the management of peritoneal surface malignancies of colonic origin : a consensus statement
377 citations
Authors
Showing all 30787 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| JoAnn E. Manson | 270 | 1819 | 258509 |
| Yi Chen | 217 | 4342 | 293080 |
| David J. Hunter | 213 | 1836 | 207050 |
| Raymond J. Dolan | 196 | 919 | 138540 |
| John P. A. Ioannidis | 185 | 1311 | 193612 |
| Stefan Schreiber | 178 | 1233 | 138528 |
| Kenneth C. Anderson | 178 | 1138 | 126072 |
| Eric J. Nestler | 178 | 748 | 116947 |
| Klaus Rajewsky | 154 | 504 | 88793 |
| Charles B. Nemeroff | 149 | 979 | 90426 |
| Andreas Pfeiffer | 149 | 1756 | 131080 |
| Rinaldo Bellomo | 147 | 1714 | 120052 |
| Jean Bousquet | 145 | 1288 | 96769 |
| Christopher Hill | 144 | 1562 | 128098 |
| Holger J. Schünemann | 141 | 810 | 113169 |