Relevant bibliographies by topics / Medical XAI

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Medical XAI'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Medical XAI"

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Zhang, Yiming, Ying Weng, and Jonathan Lund. "Applications of Explainable Artificial Intelligence in Diagnosis and Surgery." Diagnostics 12, no. 2 (January 19, 2022): 237. http://dx.doi.org/10.3390/diagnostics12020237.

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In recent years, artificial intelligence (AI) has shown great promise in medicine. However, explainability issues make AI applications in clinical usages difficult. Some research has been conducted into explainable artificial intelligence (XAI) to overcome the limitation of the black-box nature of AI methods. Compared with AI techniques such as deep learning, XAI can provide both decision-making and explanations of the model. In this review, we conducted a survey of the recent trends in medical diagnosis and surgical applications using XAI. We have searched articles published between 2019 and 2021 from PubMed, IEEE Xplore, Association for Computing Machinery, and Google Scholar. We included articles which met the selection criteria in the review and then extracted and analyzed relevant information from the studies. Additionally, we provide an experimental showcase on breast cancer diagnosis, and illustrate how XAI can be applied in medical XAI applications. Finally, we summarize the XAI methods utilized in the medical XAI applications, the challenges that the researchers have met, and discuss the future research directions. The survey result indicates that medical XAI is a promising research direction, and this study aims to serve as a reference to medical experts and AI scientists when designing medical XAI applications.
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Qian, Jinzhao, Hailong Li, Junqi Wang, and Lili He. "Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging." Diagnostics 13, no. 9 (April 27, 2023): 1571. http://dx.doi.org/10.3390/diagnostics13091571.

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Advances in artificial intelligence (AI), especially deep learning (DL), have facilitated magnetic resonance imaging (MRI) data analysis, enabling AI-assisted medical image diagnoses and prognoses. However, most of the DL models are considered as “black boxes”. There is an unmet need to demystify DL models so domain experts can trust these high-performance DL models. This has resulted in a sub-domain of AI research called explainable artificial intelligence (XAI). In the last decade, many experts have dedicated their efforts to developing novel XAI methods that are competent at visualizing and explaining the logic behind data-driven DL models. However, XAI techniques are still in their infancy for medical MRI image analysis. This study aims to outline the XAI applications that are able to interpret DL models for MRI data analysis. We first introduce several common MRI data modalities. Then, a brief history of DL models is discussed. Next, we highlight XAI frameworks and elaborate on the principles of multiple popular XAI methods. Moreover, studies on XAI applications in MRI image analysis are reviewed across the tissues/organs of the human body. A quantitative analysis is conducted to reveal the insights of MRI researchers on these XAI techniques. Finally, evaluations of XAI methods are discussed. This survey presents recent advances in the XAI domain for explaining the DL models that have been utilized in MRI applications.
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Chaddad, Ahmad, Jihao Peng, Jian Xu, and Ahmed Bouridane. "Survey of Explainable AI Techniques in Healthcare." Sensors 23, no. 2 (January 5, 2023): 634. http://dx.doi.org/10.3390/s23020634.

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Artificial intelligence (AI) with deep learning models has been widely applied in numerous domains, including medical imaging and healthcare tasks. In the medical field, any judgment or decision is fraught with risk. A doctor will carefully judge whether a patient is sick before forming a reasonable explanation based on the patient’s symptoms and/or an examination. Therefore, to be a viable and accepted tool, AI needs to mimic human judgment and interpretation skills. Specifically, explainable AI (XAI) aims to explain the information behind the black-box model of deep learning that reveals how the decisions are made. This paper provides a survey of the most recent XAI techniques used in healthcare and related medical imaging applications. We summarize and categorize the XAI types, and highlight the algorithms used to increase interpretability in medical imaging topics. In addition, we focus on the challenging XAI problems in medical applications and provide guidelines to develop better interpretations of deep learning models using XAI concepts in medical image and text analysis. Furthermore, this survey provides future directions to guide developers and researchers for future prospective investigations on clinical topics, particularly on applications with medical imaging.
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Ahmed, Saad Bin, Roberto Solis-Oba, and Lucian Ilie. "Explainable-AI in Automated Medical Report Generation Using Chest X-ray Images." Applied Sciences 12, no. 22 (November 18, 2022): 11750. http://dx.doi.org/10.3390/app122211750.

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The use of machine learning in healthcare has the potential to revolutionize virtually every aspect of the industry. However, the lack of transparency in AI applications may lead to the problem of trustworthiness and reliability of the information provided by these applications. Medical practitioners rely on such systems for clinical decision making, but without adequate explanations, diagnosis made by these systems cannot be completely trusted. Explainability in Artificial Intelligence (XAI) aims to improve our understanding of why a given output has been produced by an AI system. Automated medical report generation is one area that would benefit greatly from XAI. This survey provides an extensive literature review on XAI techniques used in medical image analysis and automated medical report generation. We present a systematic classification of XAI techniques used in this field, highlighting the most important features of each one that could be used by future research to select the most appropriate XAI technique to create understandable and reliable explanations for decisions made by AI systems. In addition to providing an overview of the state of the art in this area, we identify some of the most important issues that need to be addressed and on which research should be focused.
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Payrovnaziri, Seyedeh Neelufar, Zhaoyi Chen, Pablo Rengifo-Moreno, Tim Miller, Jiang Bian, Jonathan H. Chen, Xiuwen Liu, and Zhe He. "Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review." Journal of the American Medical Informatics Association 27, no. 7 (May 17, 2020): 1173–85. http://dx.doi.org/10.1093/jamia/ocaa053.

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Abstract Objective To conduct a systematic scoping review of explainable artificial intelligence (XAI) models that use real-world electronic health record data, categorize these techniques according to different biomedical applications, identify gaps of current studies, and suggest future research directions. Materials and Methods We searched MEDLINE, IEEE Xplore, and the Association for Computing Machinery (ACM) Digital Library to identify relevant papers published between January 1, 2009 and May 1, 2019. We summarized these studies based on the year of publication, prediction tasks, machine learning algorithm, dataset(s) used to build the models, the scope, category, and evaluation of the XAI methods. We further assessed the reproducibility of the studies in terms of the availability of data and code and discussed open issues and challenges. Results Forty-two articles were included in this review. We reported the research trend and most-studied diseases. We grouped XAI methods into 5 categories: knowledge distillation and rule extraction (N = 13), intrinsically interpretable models (N = 9), data dimensionality reduction (N = 8), attention mechanism (N = 7), and feature interaction and importance (N = 5). Discussion XAI evaluation is an open issue that requires a deeper focus in the case of medical applications. We also discuss the importance of reproducibility of research work in this field, as well as the challenges and opportunities of XAI from 2 medical professionals’ point of view. Conclusion Based on our review, we found that XAI evaluation in medicine has not been adequately and formally practiced. Reproducibility remains a critical concern. Ample opportunities exist to advance XAI research in medicine.
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Wang, Mini Han, Kelvin Kam-lung Chong, Zhiyuan Lin, Xiangrong Yu, and Yi Pan. "An Explainable Artificial Intelligence-Based Robustness Optimization Approach for Age-Related Macular Degeneration Detection Based on Medical IOT Systems." Electronics 12, no. 12 (June 16, 2023): 2697. http://dx.doi.org/10.3390/electronics12122697.

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AI-based models have shown promising results in diagnosing eye diseases based on multi-sources of data collected from medical IOT systems. However, there are concerns regarding their generalization and robustness, as these methods are prone to overfitting specific datasets. The development of Explainable Artificial Intelligence (XAI) techniques has addressed the black-box problem of machine learning and deep learning models, which can enhance interpretability and trustworthiness and optimize their performance in the real world. Age-related macular degeneration (AMD) is currently the primary cause of vision loss among elderly individuals. In this study, XAI methods were applied to detect AMD using various ophthalmic imaging modalities collected from medical IOT systems, such as colorful fundus photography (CFP), optical coherence tomography (OCT), ultra-wide fundus (UWF) images, and fluorescein angiography fundus (FAF). An optimized deep learning (DL) model and novel AMD identification systems were proposed based on the insights extracted by XAI. The findings of this study demonstrate that XAI not only has the potential to improve the transparency, reliability, and trustworthiness of AI models for ophthalmic applications, but it also has significant advantages for enhancing the robustness performance of these models. XAI could play a crucial role in promoting intelligent ophthalmology and be one of the most important techniques for evaluating and enhancing ophthalmic AI systems.
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Antoniadi, Anna Markella, Yuhan Du, Yasmine Guendouz, Lan Wei, Claudia Mazo, Brett A. Becker, and Catherine Mooney. "Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review." Applied Sciences 11, no. 11 (May 31, 2021): 5088. http://dx.doi.org/10.3390/app11115088.

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Machine Learning and Artificial Intelligence (AI) more broadly have great immediate and future potential for transforming almost all aspects of medicine. However, in many applications, even outside medicine, a lack of transparency in AI applications has become increasingly problematic. This is particularly pronounced where users need to interpret the output of AI systems. Explainable AI (XAI) provides a rationale that allows users to understand why a system has produced a given output. The output can then be interpreted within a given context. One area that is in great need of XAI is that of Clinical Decision Support Systems (CDSSs). These systems support medical practitioners in their clinic decision-making and in the absence of explainability may lead to issues of under or over-reliance. Providing explanations for how recommendations are arrived at will allow practitioners to make more nuanced, and in some cases, life-saving decisions. The need for XAI in CDSS, and the medical field in general, is amplified by the need for ethical and fair decision-making and the fact that AI trained with historical data can be a reinforcement agent of historical actions and biases that should be uncovered. We performed a systematic literature review of work to-date in the application of XAI in CDSS. Tabular data processing XAI-enabled systems are the most common, while XAI-enabled CDSS for text analysis are the least common in literature. There is more interest in developers for the provision of local explanations, while there was almost a balance between post-hoc and ante-hoc explanations, as well as between model-specific and model-agnostic techniques. Studies reported benefits of the use of XAI such as the fact that it could enhance decision confidence for clinicians, or generate the hypothesis about causality, which ultimately leads to increased trustworthiness and acceptability of the system and potential for its incorporation in the clinical workflow. However, we found an overall distinct lack of application of XAI in the context of CDSS and, in particular, a lack of user studies exploring the needs of clinicians. We propose some guidelines for the implementation of XAI in CDSS and explore some opportunities, challenges, and future research needs.
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Petrauskas, Vytautas, Gyte Damuleviciene, Algirdas Dobrovolskis, Juozas Dovydaitis, Audrone Janaviciute, Raimundas Jasinevicius, Egidijus Kazanavicius, Jurgita Knasiene, Vita Lesauskaite, and Agnius Liutke. "XAI-based Medical Decision Support System Model." International Journal of Scientific and Research Publications (IJSRP) 10, no. 12 (December 24, 2020): 598–607. http://dx.doi.org/10.29322/ijsrp.10.12.2020.p10869.

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Alkhalaf, Salem, Fahad Alturise, Adel Aboud Bahaddad, Bushra M. Elamin Elnaim, Samah Shabana, Sayed Abdel-Khalek, and Romany F. Mansour. "Adaptive Aquila Optimizer with Explainable Artificial Intelligence-Enabled Cancer Diagnosis on Medical Imaging." Cancers 15, no. 5 (February 27, 2023): 1492. http://dx.doi.org/10.3390/cancers15051492.

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Explainable Artificial Intelligence (XAI) is a branch of AI that mainly focuses on developing systems that provide understandable and clear explanations for their decisions. In the context of cancer diagnoses on medical imaging, an XAI technology uses advanced image analysis methods like deep learning (DL) to make a diagnosis and analyze medical images, as well as provide a clear explanation for how it arrived at its diagnoses. This includes highlighting specific areas of the image that the system recognized as indicative of cancer while also providing data on the fundamental AI algorithm and decision-making process used. The objective of XAI is to provide patients and doctors with a better understanding of the system’s decision-making process and to increase transparency and trust in the diagnosis method. Therefore, this study develops an Adaptive Aquila Optimizer with Explainable Artificial Intelligence Enabled Cancer Diagnosis (AAOXAI-CD) technique on Medical Imaging. The proposed AAOXAI-CD technique intends to accomplish the effectual colorectal and osteosarcoma cancer classification process. To achieve this, the AAOXAI-CD technique initially employs the Faster SqueezeNet model for feature vector generation. As well, the hyperparameter tuning of the Faster SqueezeNet model takes place with the use of the AAO algorithm. For cancer classification, the majority weighted voting ensemble model with three DL classifiers, namely recurrent neural network (RNN), gated recurrent unit (GRU), and bidirectional long short-term memory (BiLSTM). Furthermore, the AAOXAI-CD technique combines the XAI approach LIME for better understanding and explainability of the black-box method for accurate cancer detection. The simulation evaluation of the AAOXAI-CD methodology can be tested on medical cancer imaging databases, and the outcomes ensured the auspicious outcome of the AAOXAI-CD methodology than other current approaches.
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Srinivasu, Parvathaneni Naga, N. Sandhya, Rutvij H. Jhaveri, and Roshani Raut. "From Blackbox to Explainable AI in Healthcare: Existing Tools and Case Studies." Mobile Information Systems 2022 (June 13, 2022): 1–20. http://dx.doi.org/10.1155/2022/8167821.

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Introduction. Artificial intelligence (AI) models have been employed to automate decision-making, from commerce to more critical fields directly affecting human lives, including healthcare. Although the vast majority of these proposed AI systems are considered black box models that lack explainability, there is an increasing trend of attempting to create medical explainable Artificial Intelligence (XAI) systems using approaches such as attention mechanisms and surrogate models. An AI system is said to be explainable if humans can tell how the system reached its decision. Various XAI-driven healthcare approaches and their performances in the current study are discussed. The toolkits used in local and global post hoc explainability and the multiple techniques for explainability pertaining the Rational, Data, and Performance explainability are discussed in the current study. Methods. The explainability of the artificial intelligence model in the healthcare domain is implemented through the Local Interpretable Model-Agnostic Explanations and Shapley Additive Explanations for better comprehensibility of the internal working mechanism of the original AI models and the correlation among the feature set that influences decision of the model. Results. The current state-of-the-art XAI-based and future technologies through XAI are reported on research findings in various implementation aspects, including research challenges and limitations of existing models. The role of XAI in the healthcare domain ranging from the earlier prediction of future illness to the disease’s smart diagnosis is discussed. The metrics considered in evaluating the model’s explainability are presented, along with various explainability tools. Three case studies about the role of XAI in the healthcare domain with their performances are incorporated for better comprehensibility. Conclusion. The future perspective of XAI in healthcare will assist in obtaining research insight in the healthcare domain.
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Dissertations / Theses on the topic "Medical XAI"

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Nilsson, Tilde. "Metodjämförelse mellan Immulite® 2000 XPi och Phadia® 250 för analys av IgE-antikroppar i serum : En studie med avseende på IgE-medierad allergi." Thesis, Linnéuniversitetet, Institutionen för kemi och biomedicin (KOB), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-71473.

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Allergi förekommer hos cirka 20 % av befolkningen i Sverige. Av de 20 % bedöms 40 % vara allergiska mot pollen. Allergi uppstår då en individ upprepade gånger exponeras för ett allergiframkallande ämne sk allergen. IgE-antikroppar bildas och binder till basofila granulocyter och mastceller. Den allergiska reaktionen eller överkänslighetsreaktionen av typ I uppkommer när ett allergen exempelvis pollen, kommer i direkt kontakt med IgE-antikroppar på mastceller eller basofila granulocyter. Allergiska individer har högre koncentration av IgE-antikroppar i blodet än icke allergiska. Normal koncentration av IgE i serum hos icke allergiker är <0.35 kU/L. Syftet med studien var att genom analys av serumprover från 50 patienter jämföra två immunokemiska metoder på analysinstrumenten, Siemens immulite® 2000 XPi och Thermo Fisher Phadia® 250, för detektion av IgE-antikroppar riktade mot en inhalationspanel respektive björkpollen. Immulite® 2000 XPi användes som referensmetod. Jämförelsen gjordes för att bedöma om metoderna gav överensstämmande resultat och om Phadia® 250 skulle kunna användas som ett alternativ till rutinmetoden. Studiematerialet bestod av 50 patienter i åldrarna 18–80 år, 17  män (34 %) och 33 kvinnor (66 %). Resultatet visade att metoderna överlag gav överrensstämmande resultat med avseende på positiva och negativa IgE-värden, men att de uppmätta värdena för metoderna saknade överrensstämmelse. Analysresultaten från de båda metoderna gav överlag god korrelation vid analys av specifikt IgE mot björk (R=0,977) men en svagare korrelation vid analys av inhalationspanel (R=0,609). Med hjälp av Man-Whitney u-test sågs en signifikant skillnad vid IgE-analys mot björk (p= 0,0006) däremot sågs inte en signifikant skillnad mellan metodernas IgE-värden mot inhalationspanelerna (p=0,2398). För att kunna utvärdera om Phadia® 250 skulle kunna användas som ett alternativ till rutinmetoden, krävs en större population av prover samt fler analystillfällen.
Allergy is nowadays a public health problem that’s affecting 20 % of the Swedish population. A very common allergen that is related to 40% of all the allergic cases is pollen. Allergic reactions occur when an  individual is repeatedly exposed to an allergenic substance called allergene. IgE antibodies then form and bind to basophilic granulocytes and mast cells. The allergic reaction or type I hypersensitivity reaction, occurs when an allergen such as pollen comes into direct contact with IgE antibodies on mast cells or basophilic granulocytes. IgE is produced by plasma cells under the influence of CD4+ T lymphocytes. The IgE antibodies synthesized then sensitize the mast cells and basophilic granulocytes which are the most active cell types involved in allergic reactions. Allergic individuals have a higher concentration of IgE antibodies in the blood than non-allergic. Normal serum IgE concentration in non-allergic patients is <0.35 kU / L. The aim of the study was to compare two immunochemical instruments, Siemens Immulite® 2000 XPi and Thermo Fisher Phadia® 250, by analyzing 50 serum samples to detect possible presence of IgE antibodies directed against an inhalation panel and a single allergen (IgE against birch). The study material consisted of 50 patients between the ages of 18-80, 17 men (34%) and 33 women (66%). The results showed that overall the two methods yielded consistent results due to positive and negative IgE values, but the measured values of the methods lacked concordance. The analysis results from the two methods generally yielded good correlation in the analysis of specific IgE against birch (R = 0.977) but a weaker correlation in the inhalation panel analysis (R = 0.609). Man-Whitney u-test showed a significant difference in IgE analysis against birch (p = 0.0006). However, a significant difference between the IgE values of the methods against the inhalation panels (p = 0.22398) was not observed. To be able to evaluate whether Phadia® 250 could be used as an alternative to the routine method, a larger population of samples and more analytical occasions is required.
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Cubas-Peña, Rebeca. "'Every practitioner his own compiler' : practitioners and the compilation of Middle English medical books, with special reference to York Minster Library, XVI E. 32." Thesis, University of Birmingham, 2017. http://etheses.bham.ac.uk//id/eprint/7222/.

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By analysing the codicology and other features of a group of fifteenth-century medical manuscripts, my thesis aims to shed some light into the unexplored role of the compilers of medical material. These individuals assembled a number of booklets mostly produced in Middle English, and put them together with the intention to create volumes entirely dedicated to medicine. The thesis proposes a typology that will show the existence of a type of medical codices, whose substantial codicological alteration evince the involvement of the medical practitioners and earliest owners of the volumes in their making. I will argue that the manuscripts were gathered, arranged, and sometimes copied by these practitioners, who, in an attempt to create their own medical handbooks, customised their books in a manner they considered to be pertinent and useful for their practice. A comprehensive analysis of York Minster Library, MS XVI E, one of these practitioner-compiled composites (as I have labelled them) will offer new insights into a codex which has never been examined in detail. The study will eventually demonstrate that medical practitioners played a significant role in the production of fifteenth-century English medical books, especially in the compilation and arrangement of the codices’ booklets.
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Lu, Xia [Verfasser], Faikah [Akademischer Betreuer] Güler, and Hermann [Akademischer Betreuer] Haller. "Hypoxia induced renal fibrosis is mediated by PKC-[alpha]/[beta] signaling / Xia Lu. Department of Nephrology Hannover Medical School. Betreuer: Faikah Güler ; Hermann Haller." Hannover : Bibliothek der Medizinischen Hochschule Hannover, 2011. http://d-nb.info/1011308223/34.

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Laennec, René Théophile Hyacinthe. "Propositions sur la doctrine d'Hippocrate relativement à la médecine-pratique présentées à l'école de médecine de Paris et soutenues le 22 prairial an XII /." Paris : BIUM, 2003. http://www.bium.univ-paris5.fr/histmed/medica/cote?TPAR1804x241.

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Zhang, Jieneng. "Jin shi liu nian (1989-2005) Xianggang Zhong yao bu liang fan ying shi gu de fen xi yan jiu /." click here to view the abstract and table of contents, 2006. http://net3.hkbu.edu.hk/~libres/cgi-bin/thesisab.pl?pdf=b1998697xa.pdf.

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Roux, Philibert-Joseph. "Coup-d'œil physiologique sur les sécrétions présenté et soutenu à l'école de médecine de Paris le 10 germinal an XI /." Paris : BIUM, 2003. http://www.bium.univ-paris5.fr/histmed/medica/cote?TPAR1803x243.

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Hruša, Aljaksandr I. "Kancyljaryja Vjalikaha knjastva Litoŭskaha 40-ch hadoŭ XV - peršaj palovy XVI st." Minsk "Belaruskaja Navuka", 2006. http://bvbr.bib-bvb.de:8991/F?func=service&docl̲ibrary=BVB01&docn̲umber=015414469&linen̲umber=0002&funcc̲ode=DBR̲ECORDS&servicet̲ype=MEDIA.

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Guerbois, Denis François Noël. "Essai sur la nostalgie, appelée vulgairement maladie du pays présenté et soutenu à l'école de médecine de Paris le 29 prairial an XI /." Paris : BIUM, 2004. http://www.bium.univ-paris5.fr/histmed/medica/cote?TPAR1803x290.

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Petit, Édouard. "Essai sur l'influence de quelques affections morales dans les maladies chirurgicales des armées présenté et soutenu à l'école de médecine de Paris le 9 ventose an XI /." Paris : BIUM, 2004. http://www.bium.univ-paris5.fr/histmed/medica/cote?TPAR1803x203.

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Désortiaux, P. "Des signes de l'infanticide et des moyens de la constater dissertation médico-légale présentée et soutenue à l'Ecole de médecine de Paris le 24 messidor an XI /." Paris : BIUM, 2004. http://www.bium.univ-paris5.fr/histmed/medica/cote?TPAR1803x313.

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Books on the topic "Medical XAI"

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Testi medico-farmaceutici tedeschi nell'XI e XII secolo. Alessandria: Edizioni dell'Orso, 2009.

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Dao xia tian kong. Xianggang: Jing ji ri bao chu ban she, 2017.

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Corte, Francesco Della. Opuscula XII. [Genova]: Università di Genova, 1990.

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Lordkipanidze, M. D. Georgia in the XI-XII centuries. Edited by Hewitt George B. Tbilisi (Georgian S.S.R.): Ganatleba Publishers, 1987.

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G, Hewitt B., ed. Georgia in the XI-XII centuries. Tbilisi [Georgian S.S.R.]: Ganatleba Publishers, 1987.

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Bamidis, Panagiotis D., and Nicolas Pallikarakis, eds. XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13039-7.

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Dong chong xia cao jian kang fa: Bao fa huo li, jing li mi mi da gong kai! Xinbei Shi: Xin chao she wen hua shi ye you xian gong si, 2014.

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Hunyuan huang qi jia tian xia. Taiyuan Shi: Shanxi ren min chu ban she, 2012.

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Khersones XII-XIV vv.: Istoricheskai͡a︡ topografii͡a︡. Krasnoi͡a︡rsk: Izd-vo Krasnoi͡a︡rskogo universiteta, 1986.

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Nogués, Miquel Forcada. Ética e ideologia de la ciencia: El médico-filósofo en el Al-andalus : (siglos X-XII). Almeria: Fundación Ibn Tufayl de Estudios Árabes, 2011.

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Book chapters on the topic "Medical XAI"

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Boudanga, Zineb, Siham Benhadou, and Jean Philippe Leroy. "IoT- and XAI-Based Smart Medical Waste Management." In Explainable Artificial Intelligence for Smart Cities, 31–46. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003172772-3.

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Muddamsetty, Satya M., Mohammad N. S. Jahromi, and Thomas B. Moeslund. "Expert Level Evaluations for Explainable AI (XAI) Methods in the Medical Domain." In Pattern Recognition. ICPR International Workshops and Challenges, 35–46. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68796-0_3.

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Bunde, Enrico, Daniel Eisenhardt, Daniel Sonntag, Hans-Jürgen Profitlich, and Christian Meske. "Giving DIAnA More TIME – Guidance for the Design of XAI-Based Medical Decision Support Systems." In Lecture Notes in Computer Science, 107–22. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-32808-4_7.

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Yang, Wu, James R. Corte, and Joseph M. Luettgen. "RECENT PROGRESS IN FACTOR XI/XIA INHIBITOR DISCOVERY." In 2022 Medicinal Chemistry Reviews, 117–41. Washington, DC: MEDI, Inc. Published by American Chemical Society., 2022. http://dx.doi.org/10.1021/mc-2022-vol57.ch05.

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Rogenhofer, Edith, Sylvie de Laborderie, Jorieke Vyncke, Andreas Braun, and Gina Maricela Schwendemann. "EO Data Supporting MSF’s Medical Interventions." In Space Capacity Building in the XXI Century, 147–63. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-21938-3_14.

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Tabakov, S., D. Parker, F. Schlindwein, and A. Nisbett. "Accreditation of Medical Physics and Medical Engineering Programmes in the UK." In XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010, 953–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13039-7_241.

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Crossan, Andrew, Stephen Brewster, Stuart Reid, and Dominic Mellor. "Multi-session VR Medical Training: The HOPS Simulator." In People and Computers XVI - Memorable Yet Invisible, 213–25. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0105-5_13.

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Sweeney, Latanya. "Datafly: a system for providing anonymity in medical data." In Database Security XI, 356–81. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-0-387-35285-5_22.

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Wiederhold, Gio, Michel Bilello, and Chris Donahue. "Web implementation of a security mediator for medical databases." In Database Security XI, 60–72. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-0-387-35285-5_4.

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Dzogang, Fabon, James Goulding, Stafford Lightman, and Nello Cristianini. "Seasonal Variation in Collective Mood via Twitter Content and Medical Purchases." In Advances in Intelligent Data Analysis XVI, 63–74. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68765-0_6.

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Conference papers on the topic "Medical XAI"

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Hao, Jingbo. "Towards reliable medical image analysis based on deep learning with XAI." In International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), edited by Paulo Batista and Ram Bilas Pachori. SPIE, 2023. http://dx.doi.org/10.1117/12.2680931.

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Abeyagunasekera, Sudil Hasitha Piyath, Yuvin Perera, Kenneth Chamara, Udari Kaushalya, Prasanna Sumathipala, and Oshada Senaweera. "LISA : Enhance the explainability of medical images unifying current XAI techniques." In 2022 IEEE 7th International conference for Convergence in Technology (I2CT). IEEE, 2022. http://dx.doi.org/10.1109/i2ct54291.2022.9824840.

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S, Priya Dharshini, Ram Kumar K, Venkatesh S, Narasimhan K, and Adalarasu K. "An Overview Of Interpretability Techniques For Explainable Artificial Intelligence (XAI) In Deep Learning-Based Medical Image Analysis." In 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2023. http://dx.doi.org/10.1109/icaccs57279.2023.10113001.

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МИНЕНКО, В. В., А. М. ФАТЬКОВ, and М. В. ЧИРКОВ. "Armament Supplies from the Excavation of the Medieval Ancient Settlement Bolshoe Savrasovo-2 in Leninsky District of Moscow Region." In Тверь, тверская земля и сопредельные территории в эпоху средневековья. Crossref, 2019. http://dx.doi.org/10.25681/iaras.2018.978-5-9906508-3-1.240-251.

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В статье представлены результаты анализа комплекса холодного наступательного вооружения и снаряжения всадника, происходящего из раскопок селища Большое Саврасово-2. Выделены три хронологические группы предметов вооружения – X–XI вв., XII–XIII вв., XV–XVI вв. Находки первых двух периодов (древнерусское время) представлены ручным метательным оружием – наконечниками стрел. Третий период характеризуется увеличением типов и видов воинского снаряжения, что, вероятно, связано с присутствием на поселении дворянина – вотчинника с отрядом вооруженных слуг. The report outlines the results of analysis of cold offensive armament and horseman equipment complex found during the excavation of the ancient settlement Bolshoe Savrasovo-2. There are three chronological groups of armament supplies: the X–XI centuries, the XII–XIII centuries, the XV–XVI centuries. Manual throwing weapon such as arrowheads represent fi ndings of the fi rst and second periods (Old Russian period). During the third period there was expansion in the number of types and sorts of military equipment apparently caused by the presence of a noblemanlandowner with a force of armed servants in the settlement.
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Макаров, Н. А., С. В. Шполянский, А. Н. Федорина, and Н. Д. Угулава. "TO THE STUDY OF THE NEAREST DISTRICT OF MEDIEVAL SUZDAL: RESEARCHES IN THE HEADWATERS OF THE KAMENKA RIVER IN 2018–2019." In Археология Владимиро-Суздальской земли. Crossref, 2019. http://dx.doi.org/10.25681/iaras.2019.978-5-94375-304-6.7-29.

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В статье рассматриваются результаты разведочных работ, проведенных Суздальской экспедицией ИА РАН в 2018 - 2019 гг. в ближней округе Суздаля, в районе села Вышеславского, в верхнем течении р. Каменки. Здесь на площади в 15 кв. км было открыто 21 средневековое селище, на которых собрана представительная коллекция вещей и керамики. Установлено, что освоение древнерусским населением региона начинается на рубеже XI - XII вв. К середине - второй половине XII в. сеть расселения в микрорегионе достигает наибольшей плотности. В это время на одном из селищ (Вышеславское 3) функционирует двор или усадьба представителей социальной элиты княжества, что выразилось в яркой серии находок на памятнике: свинцовой подвесной печати с изображением св. Козьмы и Дамиана, позолоченного колта, змеевика в форме киотца и других вещей. Основные трансформации структуры расселения в регионе во второй половине XIII - XIV в. проявились в уменьшении количества небольших (площадью до 1,5 га) поселений, концентрации населения на относительно крупных памятниках. Материалы позднего Средневековья и раннего Нового времени (вторая половина XV - XVI в.) были обнаружены только при обследовании села Вышеславского. Таким образом, можно говорить о глубоких изменениях структуры расселения во второй половине - конце XV столетия. The results of the prospecting works carried out by the Suzdal expedition of IA RAS in 2018-2019 in the near district of Suzdal, near the village of Vysheslavskoye, in the basin of the headwaters of the Kamenka river are considered in the paper.Here, 21 medieval settlements, on which the representative collection of things and ceramics is collected, were found on the area of 15 sq.km. It is established that the development of the region by the Old Russian population begins at the turn of the XI-XII centuries. The settlement network in the microregion reaches its highest density by the middle - second half of the XII century. At this time, there is a yard or estate of the social elite of the Principality in one of settlements (Vysheslavskoye 3), which was expressed in a bright series of finds on the site: a lead pendant seal with the image of St. Kozma and Damian, a gilded temple headdress pendant (Kolt), a serpentine in the form of an icon case and other things. The main transformations of the settlement structure in the region in second half of the XIII-XIV centuries manifested in a decrease in the number of small (up to 1.5 hectares) settlements, and in the concentration of population on rather large sites. Materials of the late Middle Ages and early Modern times (second half of the XV-XVI centuries) were found only in the survey of the village Vysheslavskoye. Thus, it is possible to speak about deep changes in the structure of settlement in the second half-the end of the XV century.
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"Preface: XVI Mexican Symposium on Medical Physics." In PROCEEDINGS OF THE XVI MEXICAN SYMPOSIUM ON MEDICAL PHYSICS. AIP Publishing, 2021. http://dx.doi.org/10.1063/12.0004192.

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"Committees: XVI Mexican Symposium on Medical Physics." In PROCEEDINGS OF THE XVI MEXICAN SYMPOSIUM ON MEDICAL PHYSICS. AIP Publishing, 2021. http://dx.doi.org/10.1063/12.0004790.

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Vlašković, Veljko. "Pravni značaj biomedicinske usluge čuvanja reproduktivnih ćelija maloletnog lica." In XVI Majsko savetovanje. University of Kragujevac, Faculty of Law, 2020. http://dx.doi.org/10.46793/upk20.451v.

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Serbian Law on Medically Assisted Reproduction has widened the number of persons who enjoy the right to preserve their reproductive material for postponed reproduction due to threatened infertility. In this context, that right now belongs also to underage person if her/his parents gave the explicit and written consent to harvesting, freezing and banking of their child's reproductive cells. These are the cases when the underage person currently has reproductive capability, but she/ he is threatened by loss of reproductive function in the near future due to developing illness or forthcoming medical treatment. Understandably, the child has no right to postponed usage of residual reproductive cells beyond the cases of threatened infertility, since the underage person does not meet the legal requirements concerning personal and family status necessary for enjoying services of medically assisted reproduction. Frozen reproductive cells of underage persons will be stored without time limits, but the underage person cannot use them for conception before acquisition of legal conditions to enjoy the services of medically assisted reproduction (majority and full legal capacity, conclusion of marriage or establishing cohabitation). Such approach intends to make balance between the interests of an underage person whose gametes are stored and the „the best interets of the prospective child“ who should be conceived and born. Frozen reproductive cells of an underage person cannot be used in any other purpose except postponed homologous reproduction. Although it is not directly mentioned in Law on Medically Assisted Reproduction, reproductive cells may be harvested from an underage persons if she/he does not object to it. Such rule derives from the analogous application to the rule of Law on Human Cells and Tissues that human cell cannot be harvested from a person who has not attained majority if such person objects to it. Parents of the child decide on giving consent to harvesting, freezing and banking of their child's reproductive cells by their mutual agreement, which has the legal significance of the issue that greatly affects the child's life. The absence of consent of one or both parents cannot be replaced by state authority decision. Furthermore, the parents are not allowed to revoke their consent to their child's gamete banking.
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Vlašković, Veljko. "Pravni značaj biomedicinske usluge čuvanja reproduktivnih ćelija maloletnog lica." In XVI Majsko savetovanje. University of Kragujevac, Faculty of Law, 2020. http://dx.doi.org/10.46793/upk20.451v.

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Serbian Law on Medically Assisted Reproduction has widened the number of persons who enjoy the right to preserve their reproductive material for postponed reproduction due to threatened infertility. In this context, that right now belongs also to underage person if her/his parents gave the explicit and written consent to harvesting, freezing and banking of their child's reproductive cells. These are the cases when the underage person currently has reproductive capability, but she/ he is threatened by loss of reproductive function in the near future due to developing illness or forthcoming medical treatment. Understandably, the child has no right to postponed usage of residual reproductive cells beyond the cases of threatened infertility, since the underage person does not meet the legal requirements concerning personal and family status necessary for enjoying services of medically assisted reproduction. Frozen reproductive cells of underage persons will be stored without time limits, but the underage person cannot use them for conception before acquisition of legal conditions to enjoy the services of medically assisted reproduction (majority and full legal capacity, conclusion of marriage or establishing cohabitation). Such approach intends to make balance between the interests of an underage person whose gametes are stored and the „the best interets of the prospective child“ who should be conceived and born. Frozen reproductive cells of an underage person cannot be used in any other purpose except postponed homologous reproduction. Although it is not directly mentioned in Law on Medically Assisted Reproduction, reproductive cells may be harvested from an underage persons if she/he does not object to it. Such rule derives from the analogous application to the rule of Law on Human Cells and Tissues that human cell cannot be harvested from a person who has not attained majority if such person objects to it. Parents of the child decide on giving consent to harvesting, freezing and banking of their child's reproductive cells by their mutual agreement, which has the legal significance of the issue that greatly affects the child's life. The absence of consent of one or both parents cannot be replaced by state authority decision. Furthermore, the parents are not allowed to revoke their consent to their child's gamete banking.
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Soković, Snežana. "Falsifikovanje lekova i medicinskih sredstava – opravdanost i karakteristike krivično-pravne reakcije." In XVI Majsko savetovanje. University of Kragujevac, Faculty of Law, 2020. http://dx.doi.org/10.46793/upk20.741s.

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In the activities of suppression of counterfeit medical products, the interests and interventions of various branches of private and public law, intellectual property law, medical law of the public health protection and criminal law, overlap. Considering that actions of suppression of counterfeit medicines are often reduced to protection of commercial interests and court procedure of determining the right to compensation, and less to protection of public health interests and procedure of criminal intervention, the paper discusses the legitimacy of criminal law measures in suppression of counterfeiting of medical products. The aim of the paper is to point to the role and mechanisms of criminal law in the global system of suppression of medical products counterfeiting, bearing in mind that last year the Republic of Serbia signed the CoE Convention on the counterfeiting of medical products and similar crimes involving threats to public health (MediCrime Convention). After analyzing the scope and consequences of counterfeiting of medical products and problems in suppression of this phenomenon, it is concluded that the legitimacy of criminal law intervention justifies the evident "pandemic" expansion of the market of counterfeit medicines and the almost complete lack of incrimination of counterfeiting of medicines in national legislation. The paper presents the system of criminal law measures established by the Convention and analyzes the relationship of substandard, unregistered and generic medicines in relation to the category of counterfeit medicines defined by the Convention.
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Reports on the topic "Medical XAI"

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Escudero Gómez, Luis Alfonso. La segunda ola de la España vaciada: la despoblación de las ciudades medias en el siglo XXI. Edicions de la Universitat de Lleida, 2022. http://dx.doi.org/10.21001/cme_02.

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Meisel-Roca, Adolfo, and María Aguilera-Díaz. Cartagena, 2005-2018: Lo bueno, lo regular y lo malo. Banco de la República de Colombia, December 2020. http://dx.doi.org/10.32468/dtseru.294.

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En las dos primeras décadas del siglo XXI Cartagena presentó un crecimiento económico sin precedentes. No obstante, la prosperidad fue desigual y un alto porcentaje de la población aún presenta enormes carencias y vive en la pobreza. Esta investigación se enfoca en el estudio del avance económico de la ciudad y, a la vez, en el estancamiento relativo en materia social, durante el periodo 2005 y 2018, usando la información de los dos últimos censos de población y comparando con cinco de las principales ciudades de Colombia. En la ciudad se realizaron grandes inversiones de capital en los sectores industrial, construcción y logística portuaria que ampliaron la capacidad productiva local. Por el lado de los indicadores sociales, se dieron avances en la cobertura en educación y salud, pero sin mejorías significativas en la calidad de esos servicios. En buena medida, lo anterior podría ser un resultado de los constantes cambios de alcalde, lo cual llevó a una baja gobernabilidad que impediría que la administración distrital pudiera proveer los bienes y servicios necesarios para mejorar la calidad de vida de la población más vulnerable.
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CIOMS Cumulative Glossary, with a focus on pharmacovigilance. Council for International Organizations of Medical Sciences (CIOMS), September 2022. http://dx.doi.org/10.56759/ocef1297.

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Since its inception, CIOMS has published numerous reports on pharmaco¬vigilance and related fields, and this CIOMS Cumulative Pharmacovigilance Glossary compiles all the definitions within these reports. Version 1.1 newly includes the terms and definitions from the report of CIOMS Working Group XI on Patient involvement in the development, regulation and safe use of medicines. Reflecting the fact that recent CIOMS Working Groups have been dealing with topics that extend beyond pharmacovigilance, the title of Version 2.0 has been adapted to read ‘CIOMS Cumulative glossary, with a focus on pharmacovigilance terms’. The CIOMS Glossary does not cover CIOMS reports on the subjects of ethics, clinical pharmacology, product development, the Medical Dictionary for Regulatory Activities (MedDRA), or publications resulting from CIOMS Roundtable Discussions.
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Patient involvement in the development, regulation and safe use of medicines. Council for International Organizations of Medical Sciences (CIOMS), 2022. http://dx.doi.org/10.56759/iiew8982.

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This report from the Council for International Organizations of Medical Sciences (CIOMS) describes the importance of systematically involving patients throughout a medicine’s life – from its early development, through regulatory process to ongoing monitoring and safe use in everyday healthcare. It provides a comprehensive overview of the current knowledge about the benefits of patient involvement and existing initiatives, gives many examples and recommendations, and addresses the remaining challenges and practice gaps. The report will prompt readers to implement its best practice recommendations according to how well they fit in with their organisational and national needs. The report combines the experience and expertise of the CIOMS Working Group XI on Patient involvement in the development, regulation and safe use of medicines. It also incorporates views gathered from an open meeting in Switzerland and a workshop in Uganda, which both brought together members of the public, patient organisation representatives, regulators, drug development experts, industry, academia, health professionals and other related stakeholders. The report was finalised following a public consultation.
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