1
|
Khan M, Banerjee S, Muskawad S, Maity R, Chowdhury SR, Ejaz R, Kuuzie E, Satnarine T. The Impact of Artificial Intelligence on Allergy Diagnosis and Treatment. Curr Allergy Asthma Rep 2024; 24:361-372. [PMID: 38954325 DOI: 10.1007/s11882-024-01152-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/19/2024] [Indexed: 07/04/2024]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI), be it neuronal networks, machine learning or deep learning, has numerous beneficial effects on healthcare systems; however, its potential applications and diagnostic capabilities for immunologic diseases have yet to be explored. Understanding AI systems can help healthcare workers better assimilate artificial intelligence into their practice and unravel its potential in diagnostics, clinical research, and disease management. RECENT FINDINGS We reviewed recent advancements in AI systems and their integration in healthcare systems, along with their potential benefits in the diagnosis and management of diseases. We explored machine learning as employed in allergy diagnosis and its learning patterns from patient datasets, as well as the possible advantages of using AI in the field of research related to allergic reactions and even remote monitoring. Considering the ethical challenges and privacy concerns raised by clinicians and patients with regard to integrating AI in healthcare, we explored the new guidelines adapted by regulatory bodies. Despite these challenges, AI appears to have been successfully incorporated into various healthcare systems and is providing patient-centered solutions while simultaneously assisting healthcare workers. Artificial intelligence offers new hope in the field of immunologic disease diagnosis, monitoring, and management and thus has the potential to revolutionize healthcare systems.
Collapse
Affiliation(s)
- Maham Khan
- Fatima Jinnah Medical University, Lahore, Pakistan.
| | | | | | - Rick Maity
- Institute of Post Graduate Medical Education and Research, Kolkata, West Bengal, India
| | | | - Rida Ejaz
- Shifa College of Medicine, Islamabad, Pakistan
| | | | | |
Collapse
|
2
|
Wang Y, Fu W, Gu Y, Fang W, Zhang Y, Jin C, Yin J, Wang W, Xu H, Ge X, Ye C, Tang L, Fang J, Wang D, Su L, Wang J, Zhang X, Feng R. Comparative survey among paediatricians, nurses and health information technicians on ethics implementation knowledge of and attitude towards social experiments based on medical artificial intelligence at children's hospitals in Shanghai: a cross-sectional study. BMJ Open 2023; 13:e071288. [PMID: 37989373 PMCID: PMC10668289 DOI: 10.1136/bmjopen-2022-071288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 11/05/2023] [Indexed: 11/23/2023] Open
Abstract
OBJECTIVES Implementing ethics is crucial to prevent harm and promote widespread benefits in social experiments based on medical artificial intelligence (MAI). However, insufficient information is available concerning this within the paediatric healthcare sector. We aimed to conduct a comparative survey among paediatricians, nurses and health information technicians regarding ethics implementation knowledge of and attitude towards MAI social experiments at children's hospitals in Shanghai. DESIGN AND SETTING A cross-sectional electronic questionnaire was administered from 1 July 2022 to 31 July 2022, at tertiary children's hospitals in Shanghai. PARTICIPANTS All the eligible individuals were recruited. The inclusion criteria were as follows: (1) should be a paediatrician, nurse and health information technician, (2) should have been engaged in or currently participating in social experiments based on MAI, and (3) voluntary participation in the survey. PRIMARY OUTCOME Ethics implementation knowledge of and attitude to MAI social experiments among paediatricians, nurses and health information technicians. RESULTS There were 137 paediatricians, 135 nurses and 60 health information technicians who responded to the questionnaire at tertiary children's hospitals. 2.4-9.6% of participants were familiar with ethics implementation knowledge of MAI social experiments. 31.9-86.1% of participants held an 'agree' ethics implementation attitude. Health information technicians accounted for the highest proportion of the participants who were familiar with the knowledge of implementing ethics, and paediatricians or nurses accounted for the highest proportion among those who held 'agree' attitudes. CONCLUSIONS There is a significant knowledge gap and variations in attitudes among paediatricians, nurses and health information technicians, which underscore the urgent need for individualised education and training programmes to enhance MAI ethics implementation in paediatric healthcare.
Collapse
Affiliation(s)
- Yingwen Wang
- Nursing Department, Children's Hospital of Fudan University, Shanghai, China
| | - Weijia Fu
- Medical Information Center, Children's Hospital of Fudan University, Shanghai, China
| | - Ying Gu
- Nursing Department, Children's Hospital of Fudan University, Shanghai, China
| | - Weihan Fang
- Shanghai Pinghe Bilingual School, Shanghai, China
| | - Yuejie Zhang
- School of Computer Science, Fudan University, Shanghai, China
| | - Cheng Jin
- School of Computer Science, Fudan University, Shanghai, China
| | - Jie Yin
- School of Philosophy, Fudan University, Shanghai, China
| | - Weibing Wang
- School of Public Health, Fudan University, Shanghai, China
| | - Hong Xu
- Nephrology Department, Children's Hospital of Fudan University, Shanghai, China
| | - Xiaoling Ge
- Statistical and Data Management Center, Children's Hospital of Fudan University, Shanghai, China
| | - Chengjie Ye
- Medical Information Center, Children's Hospital of Fudan University, Shanghai, China
| | - Liangfeng Tang
- Medical Information Center, Children's Hospital of Fudan University, Shanghai, China
| | - Jinwu Fang
- School of Public Health, Fudan University, Shanghai, China
| | - Daoyang Wang
- School of Computer Science, Fudan University, Shanghai, China
| | - Ling Su
- Children's Hospital of Fudan University, Shanghai, China
| | - Jiayu Wang
- Medical Information Center, Children's Hospital of Fudan University, Shanghai, China
| | - Xiaobo Zhang
- Respiratory Department, Children's Hospital of Fudan University, Shanghai, China
| | - Rui Feng
- Medical Information Center, Children's Hospital of Fudan University, Shanghai, China
- School of Computer Science, Fudan University, Shanghai, China
| |
Collapse
|
3
|
Eigenmann P, Akenroye A, Atanaskovic Markovic M, Candotti F, Ebisawa M, Genuneit J, Kalayci Ö, Kollmann D, Leung ASY, Peters RL, Riggioni C. Pediatric Allergy and Immunology (PAI) is for polishing with artificial intelligence, but careful use. Pediatr Allergy Immunol 2023; 34:e14023. [PMID: 37747752 DOI: 10.1111/pai.14023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 08/28/2023] [Indexed: 09/26/2023]
Affiliation(s)
- Philippe Eigenmann
- Pediatric Allergy Unit, Department of Pediatrics, Gynecology and Obstetrics, University Hospitals of Geneva, Geneva, Switzerland
| | - Ayobami Akenroye
- Division of Allergy & Clinical Immunology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | | | - Fabio Candotti
- Division of Allergy and Clinical Immunology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Motohiro Ebisawa
- Clinical Research Center for Allergy and Rheumatology, National Hospital Organization Sagamihara National Hospital, Kanagawa, Japan
| | - Jon Genuneit
- Pediatric Epidemiology, Department of Pediatrics, Medical Faculty, Leipzig University, Leipzig, Germany
| | - Ömer Kalayci
- Department of Pediatric Allergy and Asthma, Hacettepe University School of Medicine, Ankara, Turkey
| | | | - Agnes Sze Yin Leung
- Department of Paediatrics, Faculty of Medicine, The Chinese University of Hong Kong, China
| | - Rachel L Peters
- Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Department of Pediatrics, The University of Melbourne, Parkville, Victoria, Australia
| | - Carmen Riggioni
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore City, Singapore
- Khoo Teck Puat-National University Children's Medical Institute, National University Hospital, National University Health System, Singapore City, Singapore
| |
Collapse
|
4
|
Cerci P. Allergic to ChatGPT? Introducing a Desensitization Protocol to Embrace Artificial Intelligence. Int Arch Allergy Immunol 2023; 184:903-905. [PMID: 37557096 DOI: 10.1159/000531785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 06/29/2023] [Indexed: 08/11/2023] Open
Affiliation(s)
- Pamir Cerci
- Division of Immunology and Allergy, Department of Internal Medicine/Eskisehir City Hospital, Eskisehir, Turkey
| |
Collapse
|
5
|
MacMath D, Chen M, Khoury P. Artificial Intelligence: Exploring the Future of Innovation in Allergy Immunology. Curr Allergy Asthma Rep 2023:10.1007/s11882-023-01084-z. [PMID: 37160554 PMCID: PMC10169188 DOI: 10.1007/s11882-023-01084-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/21/2023] [Indexed: 05/11/2023]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) has increasingly been used in healthcare. Given the capacity of AI to handle large data and complex relationships between variables, AI is well suited for applications in healthcare. Recently, AI has been applied to allergy research. RECENT FINDINGS In this article, we review how AI technologies have been utilized in basic science and clinical allergy research for asthma, atopic dermatitis, rhinology, adverse reactions to drugs and vaccines, food allergy, anaphylaxis, urticaria, and eosinophilic gastrointestinal disorders. We discuss barriers for AI adoption to improve the care of patients with atopic diseases. These studies demonstrate the utility of applying AI to the field of allergy to help investigators expand their understanding of disease pathogenesis, improve diagnostic accuracy, enable prediction for treatments and outcomes, and for drug discovery.
Collapse
Affiliation(s)
- Derek MacMath
- Department of Medicine, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Meng Chen
- Division of Pulmonary, Allergy & Critical Care Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Paneez Khoury
- National Institutes of Allergic and Infectious Diseases, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, USA.
| |
Collapse
|
6
|
Yuriev S, Rodinkova V, Mokin V, Varchuk I, Sharikadze O, Marushko Y, Halushko B, Kurchenko A. Molecular sensitization pattern to house dust mites is formed from the first years of life and includes group 1, 2, Der p 23, Der p 5, Der p 7 and Der p 21 allergens. Clin Mol Allergy 2023; 21:1. [PMID: 36737770 PMCID: PMC9898923 DOI: 10.1186/s12948-022-00182-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 12/28/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND As the process and nature of developing sensitivity to house dust mites (HDMs) remain not fully studied, our goal was to establish the pattern, nature and timeframe of house dust mite (HDM) sensitization development in patients in Ukraine as well as the period when treatment of such patients would be most effective. METHODS The data of the multiplex allergy test Alex2 was collected from 20,033 patients. To determine age specifics of sensitization, descriptive statistics were used. Bayesian Network analysis was used to build probabilistic patterns of individual sensitization. RESULTS Patients from Ukraine were most often sensitized to HDM allergens of group 1 (Der p 1, Der f 1) and group 2 (Der p 2, Der f 2) as well as to Der p 23 (55%). A considerable sensitivity to Der p 5, Der p 7 and Der p 21 allergens was also observed. The overall nature of sensitization to HDM allergens among the population of Ukraine is formed within the first year of life. By this time, there is a pronounced sensitization to HDM allergens of groups 1 and 2 as well as to Der p 23. Significance of sensitization to Der p 5, Der p 7 and Der p 21 allergens grows starting from the age of 3-6. Bayesian Network data analysis indicated the leading role of sensitization to Der p 1 and Der f 2. While developing the sensitivity to group 5 allergens, the leading role may belong to Der p 21 allergen. CONCLUSION The results obtained indicate the importance of determining the sensitization profile using the multi-component approach. A more detailed study of the optimal age for AIT prescription is required as the pattern of sensitization to HDMs is formed during the first year of life.
Collapse
Affiliation(s)
- Serhii Yuriev
- Medical Centre, DIVERO, Kiev, Ukraine ,grid.412081.eDepartment of Clinical Immunology and Allergology, Bohomolets National Medical University, Kiev, Ukraine
| | - Victoria Rodinkova
- grid.446037.2Department of Pharmacy, National Pirogov Memorial Medical University, 56, Pirogov Street, Vinnytsia, 21018 Ukraine
| | - Vitalii Mokin
- grid.446046.40000 0000 9939 744XDepartment of System Analysis and Information Technologies, Vinnytsia National Technical University, Vinnytsia, Ukraine
| | - Ilona Varchuk
- grid.446046.40000 0000 9939 744XDepartment of System Analysis and Information Technologies, Vinnytsia National Technical University, Vinnytsia, Ukraine
| | - Olena Sharikadze
- Paediatric Department, Shupyk National Healthcare University, Kiev, Ukraine
| | - Yuriy Marushko
- Department of Pediatrics of Postgraduate Education, O.O. Bohomolets Medical University, Kiev, Ukraine
| | - Bohdan Halushko
- Department of Pediatrics of Postgraduate Education, O.O. Bohomolets Medical University, Kiev, Ukraine
| | - Andrii Kurchenko
- grid.412081.eDepartment of Clinical Immunology and Allergology, Bohomolets National Medical University, Kiev, Ukraine
| |
Collapse
|
7
|
Movaghar A, Page D, Brilliant M, Mailick M. Advancing artificial intelligence-assisted pre-screening for fragile X syndrome. BMC Med Inform Decis Mak 2022; 22:152. [PMID: 35689224 PMCID: PMC9185893 DOI: 10.1186/s12911-022-01896-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 06/01/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Fragile X syndrome (FXS), the most common inherited cause of intellectual disability and autism, is significantly underdiagnosed in the general population. Diagnosing FXS is challenging due to the heterogeneity of the condition, subtle physical characteristics at the time of birth and similarity of phenotypes to other conditions. The medical complexity of FXS underscores an urgent need to develop more efficient and effective screening methods to identify individuals with FXS. In this study, we evaluate the effectiveness of using artificial intelligence (AI) and electronic health records (EHRs) to accelerate FXS diagnosis. METHODS The EHRs of 2.1 million patients served by the University of Wisconsin Health System (UW Health) were the main data source for this retrospective study. UW Health includes patients from south central Wisconsin, with approximately 33 years (1988-2021) of digitized health data. We identified all participants who received a code for FXS in the form of International Classification of Diseases (ICD), Ninth or Tenth Revision (ICD9 = 759.83, ICD10 = Q99.2). Only individuals who received the FXS code on at least two occasions ("Rule of 2") were classified as clinically diagnosed cases. To ensure the availability of sufficient data prior to clinical diagnosis to test the model, only individuals who were diagnosed after age 10 were included in the analysis. A supervised random forest classifier was used to create an AI-assisted pre-screening tool to identify cases with FXS, 5 years earlier than the time of clinical diagnosis based on their medical records. The area under receiver operating characteristic curve (AUROC) was reported. The AUROC shows the level of success in identification of cases and controls (AUROC = 1 represents perfect classification). RESULTS 52 individuals were identified as target cases and matched with 5200 controls. AI-assisted pre-screening tool successfully identified cases with FXS, 5 years earlier than the time of clinical diagnosis with an AUROC of 0.717. A separate model trained and tested on UW Health cases achieved the AUROC of 0.798. CONCLUSIONS This result shows the potential utility of our tool in accelerating FXS diagnosis in real clinical settings. Earlier diagnosis can lead to more timely intervention and access to services with the goal of improving patients' health outcomes.
Collapse
Affiliation(s)
- Arezoo Movaghar
- Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Madison, WI, 53705, USA.
| | - David Page
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Murray Brilliant
- Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Madison, WI, 53705, USA
| | - Marsha Mailick
- Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Madison, WI, 53705, USA
| |
Collapse
|
8
|
Artificial Intelligence in Clinical Immunology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_83] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
9
|
Malizia V, Cilluffo G, Fasola S, Ferrante G, Landi M, Montalbano L, Licari A, La Grutta S. Endotyping allergic rhinitis in children: A machine learning approach. Pediatr Allergy Immunol 2022; 33 Suppl 27:18-21. [PMID: 35080305 PMCID: PMC9546471 DOI: 10.1111/pai.13620] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 07/24/2021] [Accepted: 08/06/2021] [Indexed: 12/30/2022]
Abstract
INTRODUCTION The diversity of allergic rhinitis (AR) phenotypes is particularly evident in childhood, suggesting the need to analyze and identify new approaches to capture such clinical heterogeneity. Nasal cytology (NC) is a very useful diagnostic tool for identifying and quantifying nasal inflammation. Data-driven approaches such as latent class analysis (LCA) assign subjects to classes based on their characteristics. We hypothesized that LCA based on NC, including the assessment of neutrophils, eosinophils, and mast cells, may be helpful for identifying AR endotypes in children. METHODS A total of 168 children were enrolled. Sociodemographic characteristics and detailed medical history were obtained from their parents. All children performed NC and skin prick tests. LCA was applied for identifying AR endotypes based on NC, using the R package poLCA. All the statistical analyses were performed using R 4.0.5 software. Statistical significance was set at p ≤ .05. RESULTS LCA identified two classes: Class 1 (n = 126, 75%): higher frequency of children with moderate/large number of neutrophils (31.45%); almost all the children in this class had no mast cells (91.27%) and Class 2 (n = 42, 25%): higher frequency of children with moderate/large number of eosinophils (45.24%) and moderate/large number of mast cells (50%). CONCLUSIONS The present study used a machine learning approach for endotyping childhood AR, which may contribute to improve the diagnostic accuracy and to deliver personalized health care in the context of precision medicine.
Collapse
Affiliation(s)
- Velia Malizia
- Institute for Biomedical Research and Innovation, National Research Council, Palermo, Italy
| | - Giovanna Cilluffo
- Institute for Biomedical Research and Innovation, National Research Council, Palermo, Italy
| | - Salvatore Fasola
- Institute for Biomedical Research and Innovation, National Research Council, Palermo, Italy
| | - Giuliana Ferrante
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, Palermo, Italy
| | - Massimo Landi
- Institute for Biomedical Research and Innovation, National Research Council, Palermo, Italy.,Pediatric National Healthcare System, Turin, Italy
| | - Laura Montalbano
- Institute for Biomedical Research and Innovation, National Research Council, Palermo, Italy
| | - Amelia Licari
- Pediatric Clinic, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.,Department of Clinical, Chirurgical, Diagnostic and Pediatric Science, University of Pavia, Pavia, Italy
| | - Stefania La Grutta
- Institute for Biomedical Research and Innovation, National Research Council, Palermo, Italy
| |
Collapse
|
10
|
Pawankar R, Thong BYH, Wang JY. APAAACI 2021 International Conference: a new era of allergy and clinical immunology in digital. Asia Pac Allergy 2022; 12:e5. [PMID: 35174056 PMCID: PMC8819418 DOI: 10.5415/apallergy.2022.12.e5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 01/17/2022] [Indexed: 12/05/2022] Open
Affiliation(s)
- Ruby Pawankar
- Division of Allergy, Department of Pediatrics, Nippon Medical School, Tokyo, Japan
| | - Bernard Yu-Hor Thong
- Department of Rheumatology, Allergy and Immunology, Tan Tock Seng Hospital, Singapore
| | - Jiu-Yao Wang
- Allergy, Immunology and Microbiome (AIM) Research Center, China Medical University Children’s Hospital (CMUCH), Taichung, Taiwan
| |
Collapse
|
11
|
Zheng Y, Tang N, Omar R, Hu Z, Duong T, Wang J, Wu W, Haick H. Smart Materials Enabled with Artificial Intelligence for Healthcare Wearables. ADVANCED FUNCTIONAL MATERIALS 2021; 31. [DOI: 10.1002/adfm.202105482] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Indexed: 08/30/2023]
Abstract
AbstractContemporary medicine suffers from many shortcomings in terms of successful disease diagnosis and treatment, both of which rely on detection capacity and timing. The lack of effective, reliable, and affordable detection and real‐time monitoring limits the affordability of timely diagnosis and treatment. A new frontier that overcomes these challenges relies on smart health monitoring systems that combine wearable sensors and an analytical modulus. This review presents the latest advances in smart materials for the development of multifunctional wearable sensors while providing a bird's eye‐view of their characteristics, functions, and applications. The review also presents the state‐of‐the‐art on wearables fitted with artificial intelligence (AI) and support systems for clinical decision in early detection and accurate diagnosis of disorders. The ongoing challenges and future prospects for providing personal healthcare with AI‐assisted support systems relating to clinical decisions are presented and discussed.
Collapse
Affiliation(s)
- Youbin Zheng
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute Technion‐Israel Institute of Technology Haifa 3200003 Israel
| | - Ning Tang
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute Technion‐Israel Institute of Technology Haifa 3200003 Israel
| | - Rawan Omar
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute Technion‐Israel Institute of Technology Haifa 3200003 Israel
| | - Zhipeng Hu
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute Technion‐Israel Institute of Technology Haifa 3200003 Israel
- School of Chemistry Xi'an Jiaotong University Xi'an 710126 P. R. China
| | - Tuan Duong
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute Technion‐Israel Institute of Technology Haifa 3200003 Israel
| | - Jing Wang
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute Technion‐Israel Institute of Technology Haifa 3200003 Israel
| | - Weiwei Wu
- School of Advanced Materials and Nanotechnology Interdisciplinary Research Center of Smart Sensors Xidian University Xi'an 710126 P. R. China
| | - Hossam Haick
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute Technion‐Israel Institute of Technology Haifa 3200003 Israel
- School of Advanced Materials and Nanotechnology Interdisciplinary Research Center of Smart Sensors Xidian University Xi'an 710126 P. R. China
| |
Collapse
|
12
|
Eigenmann P. Comments on vitamin D in asthma, milk allergy diagnosis, and stem cell transplantation in chronic granulomatous disease. Pediatr Allergy Immunol 2021; 32:401-404. [PMID: 33792989 DOI: 10.1111/pai.13468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 02/05/2021] [Indexed: 11/27/2022]
Affiliation(s)
- Philippe Eigenmann
- Department of Women-Children-Teenagers, University Hospital of Geneva, Geneva, Switzerland
| |
Collapse
|
13
|
Artificial Intelligence in Clinical Immunology. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_83-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|