1
|
Visual Resolve of Modern Educational Technology Based on Artificial Intelligence under the Digital Background. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1924138. [PMID: 36188712 PMCID: PMC9525196 DOI: 10.1155/2022/1924138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 08/03/2022] [Indexed: 12/04/2022]
Abstract
With the development of Internet technology and the arrival of the knowledge-driven era, the breadth and depth of educational informatization are increasing day by day. Educational technology is not only a subject but also a career adapted to education and teaching. The growth speed of modern educational technology and the size of its benefits determine its management level to a large extent. With new technologies, new ideas, and new social needs, it is difficult for new ideas, new thoughts, and new methods to make the traditional e-learning management to accommodate the demands of the new era. At present, the work efficiency of modern educational technology visualization systems is generally not high, and modern distance teaching has an increasing demand for management informatization. However, there is a lack of a management platform for distance education that adapts to organizational characteristics such as openness, dynamics, flexibility, individualization, and decentralization. Therefore, this study introduces machine learning and BP neural network, establishes a visual modern distance teaching management system model, and uses machine learning algorithms to learn the visual process. The experimental results show that the system efficiency after learning is higher, and the time required for visualization of different groups in the experiment is 14.32 s, 13.18 s, 12.27 s, and 13.64 s, respectively, which effectively improves the efficiency of visualization and reduces the consumption of human resources.
Collapse
|
2
|
Soellner M, Koenigstorfer J. Motive perception pathways to the release of personal information to healthcare organizations. BMC Med Inform Decis Mak 2022; 22:240. [PMID: 36100876 PMCID: PMC9468521 DOI: 10.1186/s12911-022-01986-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 09/07/2022] [Indexed: 11/23/2022] Open
Abstract
Background The goal of the study is to assess the downstream effects of who requests personal information from individuals for artificial intelligence-(AI) based healthcare research purposes—be it a pharmaceutical company (as an example of a for-profit organization) or a university hospital (as an example of a not-for-profit organization)—as well as their boundary conditions on individuals’ likelihood to release personal information about their health. For the latter, the study considers two dimensions: the tendency to self-disclose (which is aimed to be high so that AI applications can reach their full potential) and the tendency to falsify (which is aimed to be low so that AI applications are based on both valid and reliable data). Methods Across three experimental studies with Amazon Mechanical Turk workers from the U.S. (n = 204, n = 330, and n = 328, respectively), Covid-19 was used as the healthcare research context. Results University hospitals (vs. pharmaceutical companies) score higher on altruism and lower on egoism. Individuals were more willing to disclose data if they perceived that the requesting organization acts based on altruistic motives (i.e., the motives function as gate openers). Individuals were more likely to protect their data by intending to provide false information when they perceived egoistic motives to be the main driver for the organization requesting their data (i.e., the motives function as a privacy protection tool). Two moderators, namely message appeal (Study 2) and message endorser credibility (Study 3) influence the two indirect pathways of the release of personal information. Conclusion The findings add to Communication Privacy Management Theory as well as Attribution Theory by suggesting motive-based pathways to the release of correct personal health data. Compared to not-for-profit organizations, for-profit organizations are particularly recommended to match their message appeal with the organizations’ purposes (to provide personal benefit) and to use high-credibility endorsers in order to reduce inherent disadvantages in motive perceptions. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-022-01986-4.
Collapse
|
3
|
Xie J, Tian S, Liu J, Cao R, Yue P, Cai X, Shang Q, Yang M, Han L, Zhang DK. Dual role of the nasal microbiota in neurological diseases—An unignorable risk factor or a potential therapy carrier. Pharmacol Res 2022; 179:106189. [DOI: 10.1016/j.phrs.2022.106189] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 03/06/2022] [Accepted: 03/17/2022] [Indexed: 12/11/2022]
|
4
|
Secinaro S, Calandra D, Secinaro A, Muthurangu V, Biancone P. The role of artificial intelligence in healthcare: a structured literature review. BMC Med Inform Decis Mak 2021; 21:125. [PMID: 33836752 PMCID: PMC8035061 DOI: 10.1186/s12911-021-01488-9] [Citation(s) in RCA: 108] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 04/01/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND/INTRODUCTION Artificial intelligence (AI) in the healthcare sector is receiving attention from researchers and health professionals. Few previous studies have investigated this topic from a multi-disciplinary perspective, including accounting, business and management, decision sciences and health professions. METHODS The structured literature review with its reliable and replicable research protocol allowed the researchers to extract 288 peer-reviewed papers from Scopus. The authors used qualitative and quantitative variables to analyse authors, journals, keywords, and collaboration networks among researchers. Additionally, the paper benefited from the Bibliometrix R software package. RESULTS The investigation showed that the literature in this field is emerging. It focuses on health services management, predictive medicine, patient data and diagnostics, and clinical decision-making. The United States, China, and the United Kingdom contributed the highest number of studies. Keyword analysis revealed that AI can support physicians in making a diagnosis, predicting the spread of diseases and customising treatment paths. CONCLUSIONS The literature reveals several AI applications for health services and a stream of research that has not fully been covered. For instance, AI projects require skills and data quality awareness for data-intensive analysis and knowledge-based management. Insights can help researchers and health professionals understand and address future research on AI in the healthcare field.
Collapse
Affiliation(s)
| | - Davide Calandra
- Department of Management, University of Turin, Turin, Italy.
| | | | - Vivek Muthurangu
- Institute of Child Health, University College London, London, UK
| | - Paolo Biancone
- Department of Management, University of Turin, Turin, Italy
| |
Collapse
|
5
|
Relationship between Artificial Intelligence-Based General Anesthetics and Postoperative Cognitive Dysfunction. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:5553029. [PMID: 33868620 PMCID: PMC8032528 DOI: 10.1155/2021/5553029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 03/08/2021] [Accepted: 03/20/2021] [Indexed: 11/23/2022]
Abstract
Postoperative cognitive dysfunction (POCD) refers to the complications of the central nervous system before and after surgery in patients without mental disorders. Many studies have shown that surgical anesthesia may cause POCD, especially in elderly patients. This article aims to study the relationship between artificial intelligence-based general anesthetics and postoperative cognitive dysfunction. This article first describes and classifies artificial intelligence, introduces its realization method, machine learning algorithms, and briefly introduces the basic principles of regression and classification methods in machine learning; then, the principles and techniques of general anesthetics are proposed. The pathogenesis of postoperative cognitive dysfunction (POCD) is explained in detail. Finally, the effect of anesthetics on postoperative cognitive dysfunction is obtained from both inhaled anesthetics and intravenous anesthetics. The impact on postoperative cognitive function is explained. The experimental results in this article show that there is no statistically significant difference in the two groups of patients' age, gender ratio, body mass index, education level, preoperative comorbidities, and other general indicators. Through the use of EEG bispectral index monitors to monitor the depth of anesthesia and postoperative cognitive dysfunction, first, there was no obvious relationship between the occurrence of postoperative cognitive dysfunction at 1, 5, 10, and 50 days and discharge time. The comprehensive monitoring group can reduce the clinical dose of preventive medication and cis-atracurium and shorten the patient's recovery time, extubation time, and recovery time. In addition, it can also reduce the increase of serum protein S100β in elderly patients and reduce the incidence of early postoperative cognitive dysfunction.
Collapse
|
6
|
Smart diagnostics devices through artificial intelligence and mechanobiological approaches. 3 Biotech 2020; 10:351. [PMID: 32728518 DOI: 10.1007/s13205-020-02342-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 07/15/2020] [Indexed: 10/23/2022] Open
Abstract
The present work illustrates the promising intervention of smart diagnostics devices through artificial intelligence (AI) and mechanobiological approaches in health care practices. The artificial intelligence and mechanobiological approaches in diagnostics widen the scope for point of care techniques for the timely revealing of diseases by understanding the biomechanical properties of the tissue of interest. Smart diagnostic device senses the physical parameters due to change in mechanical, biological, and luidic properties of the cells and to control these changes, supply the necessary drugs immediately using AI techniques. The latest techniques like sweat diagnostics to measure the overall health, Photoplethysmography (PPG) for real-time monitoring of pulse waveform by capturing the reflected signal due to blood pulsation), Micro-electromechanical systems (MEMS) and Nano-electromechanical systems (NEMS) smart devices to detect disease at its early stage, lab-on-chip and organ-on-chip technologies, Ambulatory Circadian Monitoring device (ACM), a wrist-worn device for Parkinson's disease have been discussed. The recent and futuristic smart diagnostics tool/techniques like emotion recognition by applying machine learning algorithms, atomic force microscopy that measures the fibrinogen and erythrocytes binding force, smartphone-based retinal image analyser system, image-based computational modeling for various neurological disorders, cardiovascular diseases, tuberculosis, predicting and preventing of Zika virus, optimal drugs and doses for HIV using AI, etc. have been reviewed. The objective of this review is to examine smart diagnostics devices based on artificial intelligence and mechanobiological approaches, with their medical applications in healthcare. This review determines that smart diagnostics devices have potential applications in healthcare, but more research work will be essential for prospective accomplishments of this technology.
Collapse
|
7
|
|
8
|
Ho D, Quake SR, McCabe ERB, Chng WJ, Chow EK, Ding X, Gelb BD, Ginsburg GS, Hassenstab J, Ho CM, Mobley WC, Nolan GP, Rosen ST, Tan P, Yen Y, Zarrinpar A. Enabling Technologies for Personalized and Precision Medicine. Trends Biotechnol 2020; 38:497-518. [PMID: 31980301 PMCID: PMC7924935 DOI: 10.1016/j.tibtech.2019.12.021] [Citation(s) in RCA: 109] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 12/16/2019] [Accepted: 12/17/2019] [Indexed: 02/06/2023]
Abstract
Individualizing patient treatment is a core objective of the medical field. Reaching this objective has been elusive owing to the complex set of factors contributing to both disease and health; many factors, from genes to proteins, remain unknown in their role in human physiology. Accurately diagnosing, monitoring, and treating disorders requires advances in biomarker discovery, the subsequent development of accurate signatures that correspond with dynamic disease states, as well as therapeutic interventions that can be continuously optimized and modulated for dose and drug selection. This work highlights key breakthroughs in the development of enabling technologies that further the goal of personalized and precision medicine, and remaining challenges that, when addressed, may forge unprecedented capabilities in realizing truly individualized patient care.
Collapse
Affiliation(s)
- Dean Ho
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore; The Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Department of Biomedical Engineering, NUS Engineering, National University of Singapore, Singapore; Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Stephen R Quake
- Department of Bioengineering, Stanford University, CA, USA; Department of Applied Physics, Stanford University, CA, USA; Chan Zuckerberg Biohub, San Francisco, CA, USA
| | | | - Wee Joo Chng
- Department of Haematology and Oncology, National University Cancer Institute, National University Health System, Singapore; Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Edward K Chow
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Xianting Ding
- Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Bruce D Gelb
- Mindich Child Health and Development Institute, Departments of Pediatrics and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY, USA
| | - Geoffrey S Ginsburg
- Center for Applied Genomics and Precision Medicine, Duke University, NC, USA
| | - Jason Hassenstab
- Department of Neurology, Washington University in St. Louis, MO, USA; Psychological & Brain Sciences, Washington University in St. Louis, MO, USA
| | - Chih-Ming Ho
- Department of Mechanical Engineering, University of California, Los Angeles, CA, USA
| | - William C Mobley
- Department of Neurosciences, University of California, San Diego, CA, USA
| | - Garry P Nolan
- Department of Microbiology & Immunology, Stanford University, CA, USA
| | - Steven T Rosen
- Comprehensive Cancer Center and Beckman Research Institute, City of Hope, CA, USA
| | - Patrick Tan
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Yun Yen
- College of Medical Technology, Center of Cancer Translational Research, Taipei Cancer Center of Taipei Medical University, Taipei, Taiwan
| | - Ali Zarrinpar
- Department of Surgery, Division of Transplantation & Hepatobiliary Surgery, University of Florida, FL, USA
| |
Collapse
|
9
|
Ding X, Chang VHS, Li Y, Li X, Xu H, Ho C, Ho D, Yen Y. Harnessing an Artificial Intelligence Platform to Dynamically Individualize Combination Therapy for Treating Colorectal Carcinoma in a Rat Model. ADVANCED THERAPEUTICS 2019. [DOI: 10.1002/adtp.201900127] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Xianting Ding
- Institute for Personalized Medicine, State Key Laboratory of Oncogenes and Related Genes of Biomedical Engineering School Shanghai Jiao Tong University Shanghai 200030 China
| | - Vincent H. S. Chang
- Department of Physiology, School of Medicine, College of Medicine Taipei Medical University Taipei 110 Taiwan
- The PhD Program for Translational Medicine, College of Medical Science and Technology Taipei Medical University Taipei 110 Taiwan
| | - Yulong Li
- Institute for Personalized Medicine, State Key Laboratory of Oncogenes and Related Genes of Biomedical Engineering School Shanghai Jiao Tong University Shanghai 200030 China
| | - Xin Li
- Institute for Personalized Medicine, State Key Laboratory of Oncogenes and Related Genes of Biomedical Engineering School Shanghai Jiao Tong University Shanghai 200030 China
| | - Hongquan Xu
- Department of Statistics University of California Los Angeles CA 90095 USA
| | - Chih‐Ming Ho
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science University of California Los Angeles CA 90095 USA
- Department of Mechanical and Aerospace Engineering, Henry Samueli School of Engineering and Applied Science University of California Los Angeles CA 90095 USA
| | - Dean Ho
- The N.1 Institute for Health (N.1) National University of Singapore Singapore 117456
- Department of Biomedical Engineering, NUS Engineering National University of Singapore Singapore 117583
- Department of Pharmacology, Yong Loo Lin School of Medicine National University of Singapore Singapore 117600
| | - Yun Yen
- The PhD Program for Translational Medicine, College of Medical Science and Technology Taipei Medical University Taipei 110 Taiwan
- Chemical Engineering, Division of Chemistry and Chemical Engineering California Institute of Technology California 91125 USA
| |
Collapse
|
10
|
Abstract
The field of nanomedicine has made substantial strides in the areas of therapeutic and diagnostic development. For example, nanoparticle-modified drug compounds and imaging agents have resulted in markedly enhanced treatment outcomes and contrast efficiency. In recent years, investigational nanomedicine platforms have also been taken into the clinic, with regulatory approval for Abraxane® and other products being awarded. As the nanomedicine field has continued to evolve, multifunctional approaches have been explored to simultaneously integrate therapeutic and diagnostic agents onto a single particle, or deliver multiple nanomedicine-functionalized therapies in unison. Similar to the objectives of conventional combination therapy, these strategies may further improve treatment outcomes through targeted, multi-agent delivery that preserves drug synergy. Also, similar to conventional/unmodified combination therapy, nanomedicine-based drug delivery is often explored at fixed doses. A persistent challenge in all forms of drug administration is that drug synergy is time-dependent, dose-dependent and patient-specific at any given point of treatment. To overcome this challenge, the evolution towards nanomedicine-mediated co-delivery of multiple therapies has made the potential of interfacing artificial intelligence (AI) with nanomedicine to sustain optimization in combinatorial nanotherapy a reality. Specifically, optimizing drug and dose parameters in combinatorial nanomedicine administration is a specific area where AI can actionably realize the full potential of nanomedicine. To this end, this review will examine the role that AI can have in substantially improving nanomedicine-based treatment outcomes, particularly in the context of combination nanotherapy for both N-of-1 and population-optimized treatment.
Collapse
Affiliation(s)
- Dean Ho
- Department of Biomedical Engineering, NUS Engineering, National University of Singapore, Singapore.
| | | | | |
Collapse
|
11
|
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019; 25:44-56. [PMID: 30617339 DOI: 10.1038/s41591-018-0300-7] [Citation(s) in RCA: 2107] [Impact Index Per Article: 421.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Accepted: 11/12/2018] [Indexed: 11/08/2022]
Abstract
The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.
Collapse
Affiliation(s)
- Eric J Topol
- Department of Molecular Medicine, Scripps Research, La Jolla, CA, USA.
| |
Collapse
|
12
|
Verma M, Hontecillas R, Tubau-Juni N, Abedi V, Bassaganya-Riera J. Challenges in Personalized Nutrition and Health. Front Nutr 2018; 5:117. [PMID: 30555829 PMCID: PMC6281760 DOI: 10.3389/fnut.2018.00117] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2018] [Accepted: 11/14/2018] [Indexed: 12/11/2022] Open
Affiliation(s)
- Meghna Verma
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, Blacksburg, VA, United States.,Graduate Program in Translational Biology, Medicine and Health, Virginia Tech, Blacksburg, VA, United States
| | - Raquel Hontecillas
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, Blacksburg, VA, United States
| | - Nuria Tubau-Juni
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, Blacksburg, VA, United States
| | - Vida Abedi
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, Blacksburg, VA, United States.,Department of Biomedical and Translational Informatics, Geisinger Health System, Danville, PA, United States
| | - Josep Bassaganya-Riera
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, Blacksburg, VA, United States
| |
Collapse
|
13
|
Pantuck AJ, Lee DK, Kee T, Wang P, Lakhotia S, Silverman MH, Mathis C, Drakaki A, Belldegrun AS, Ho CM, Ho D. Modulating BET Bromodomain Inhibitor ZEN-3694 and Enzalutamide Combination Dosing in a Metastatic Prostate Cancer Patient Using CURATE.AI, an Artificial Intelligence Platform. ADVANCED THERAPEUTICS 2018. [DOI: 10.1002/adtp.201800104] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Affiliation(s)
- Allan J. Pantuck
- Ronald Reagan UCLA Medical Center; Department of Urology; David Geffen School of Medicine; Institute of Urologic Oncology; University of California; 757 Westwood Plaza Los Angeles CA 90095 USA
- Jonsson Comprehensive Cancer Center; University of California; 10833 Le Conte Ave Los Angeles CA 90095 USA
| | - Dong-Keun Lee
- Department of Bioengineering; Henry Samueli School of Engineering and Applied Science; University of California; 410 Westwood Plaza Los Angeles CA 90095 USA
| | - Theodore Kee
- Department of Biomedical Engineering; National University of Singapore; Singapore 117583 Singapore
- Department of Bioengineering; Henry Samueli School of Engineering and Applied Science; University of California; 410 Westwood Plaza Los Angeles CA 90095 USA
| | - Peter Wang
- Department of Chemical and Biomolecular Engineering; Henry Samueli School of Engineering and Applied Science; University of California; 5531 Boelter Hall Los Angeles CA 90095 USA
| | - Sanjay Lakhotia
- Zenith Epigenetics; Suite 4010, 44 Montgomery Street San Francisco CA 94104 USA
- Zenith Epigenetics; 300, 4820 Richard Road SW Calgary AB T3E 6L1 Canada
| | - Michael H. Silverman
- Zenith Epigenetics; Suite 4010, 44 Montgomery Street San Francisco CA 94104 USA
- Zenith Epigenetics; 300, 4820 Richard Road SW Calgary AB T3E 6L1 Canada
| | - Colleen Mathis
- Ronald Reagan UCLA Medical Center; Department of Urology; David Geffen School of Medicine; Institute of Urologic Oncology; University of California; 757 Westwood Plaza Los Angeles CA 90095 USA
| | - Alexandra Drakaki
- Ronald Reagan UCLA Medical Center; Department of Urology; David Geffen School of Medicine; Institute of Urologic Oncology; University of California; 757 Westwood Plaza Los Angeles CA 90095 USA
- Department of Medicine; Division of Hematology & Oncology; David Geffen School of Medicine; University of California; 10833 Le Conte Ave. 11-934 Factor Bldg. Los Angeles CA 90095 USA
| | - Arie S. Belldegrun
- Ronald Reagan UCLA Medical Center; Department of Urology; David Geffen School of Medicine; Institute of Urologic Oncology; University of California; 757 Westwood Plaza Los Angeles CA 90095 USA
| | - Chih-Ming Ho
- Jonsson Comprehensive Cancer Center; University of California; 10833 Le Conte Ave Los Angeles CA 90095 USA
- Department of Bioengineering; Henry Samueli School of Engineering and Applied Science; University of California; 410 Westwood Plaza Los Angeles CA 90095 USA
- Department of Mechanical and Aerospace Engineering; Henry Samueli School of Engineering and Applied Science; University of California; 420 Westwood Plaza Los Angeles CA 90095 USA
| | - Dean Ho
- Department of Biomedical Engineering; National University of Singapore; Singapore 117583 Singapore
| |
Collapse
|
14
|
Dafoe DC, Tantisattamo E, Reddy U. Precision Medicine and Personalized Approach to Renal Transplantation. Semin Nephrol 2018; 38:346-354. [DOI: 10.1016/j.semnephrol.2018.05.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
|
15
|
Fiocchi C. Inflammatory Bowel Disease: Complexity and Variability Need Integration. Front Med (Lausanne) 2018; 5:75. [PMID: 29619371 PMCID: PMC5873363 DOI: 10.3389/fmed.2018.00075] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Accepted: 03/07/2018] [Indexed: 12/16/2022] Open
Affiliation(s)
- Claudio Fiocchi
- Department of Pathobiology, Lerner Research Institute, Cleveland, OH, United States.,Department of Gastroenterology and Hepatology, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH, United States
| |
Collapse
|
16
|
Integrated Artificial Intelligence Approaches for Disease Diagnostics. Indian J Microbiol 2018; 58:252-255. [PMID: 29651188 DOI: 10.1007/s12088-018-0708-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Accepted: 01/06/2018] [Indexed: 12/13/2022] Open
Abstract
Mechanocomputational techniques in conjunction with artificial intelligence (AI) are revolutionizing the interpretations of the crucial information from the medical data and converting it into optimized and organized information for diagnostics. It is possible due to valuable perfection in artificial intelligence, computer aided diagnostics, virtual assistant, robotic surgery, augmented reality and genome editing (based on AI) technologies. Such techniques are serving as the products for diagnosing emerging microbial or non microbial diseases. This article represents a combinatory approach of using such approaches and providing therapeutic solutions towards utilizing these techniques in disease diagnostics.
Collapse
|
17
|
Affiliation(s)
- James C Fackler
- Department of Anesthesiology and Critical Care Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD
| |
Collapse
|
18
|
Correction. Nat Med 2017; 23:1391. [PMID: 29216038 DOI: 10.1038/nm1217-1391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This corrects the article DOI: 10.1038/nm1117-1244.
Collapse
|