CS158-2 Activity Instructions

 

CS158-2 Activity #6 Instructions:

Research three (3) studies related to Artificial Intelligence (Medicine) and another three (3) studies related to Artificial Intelligence ( Education). For each study, provide answers to the following items:        

Title Author(s) ISBN / Reference URL Problem Procedure Conclusion/Findings Benefits Opinion about the study

 

Artificial Intelligence Study on Medicine #1 Title: SUN-476 The Human Cell Atlas and Artificial Intelligence: Kidney Medicine in the Digital Age and   Beyond  Author(s): S. Osadolor, K. Solez, I. Moghe ISBN / Reference URL: https://doi. https://doi.org/10.1016/j.ekir org/10.1016/j.ekir.2020.02.1019 .2020.02.1019 Problem: The Human Cell Atlas (HCA) Project - Single-cell RNA sequencing (scRNA-seq) is now a clinically viable technology that can differentiate between cells based on active gene transcription that are morpho mor pholog logica ically lly simila similarr but gen geneti etica cally lly and fun functi ctiona onally lly distin distinct. ct. Ove Overr the las lastt decade decade,, the rapid rapid advancement of sequencing technologies helps us to sequence millions of cells at a time, opening the door  to assemble genetic libraries of entire organisms using the single-cell method. Procedure: This study analyzed the latest findings, analyzing different applications of kidney medicine of  modern medical technology. The need for a new Banff Classification of TEP is stimulated by exponentially improving technologies such as scRNA-seq, and HCA in particular. Conclusion/Findings: Together, the HCA and the current TEP Banff Classification would increase the effectiveness of bioengineered organ transplantation and decrease the number of lost grafts. Benefits: Benefi ts: The combin combinati ation on of med medica icall techno technolog logy y and mac machin hinee lea learni rning ng wit with h hig high-t h-thro hrough ughput put has  promising results, some of which are already a reality. In order to help build precision therapeutics, scRNA-seq studies have detected novel cells, linked unique altered gene expression to human illnesses, and mapped disease progression. Opinion about the study: A new wave of research has arisen, using techniques such as digital pathology, gamification, and crowd-sourcing to combine the abilities of medical experts and computer scientists. We will begin to understand how kidney medicine can develop in the Digital Era and beyond, with the assistance of the HCA and other related ventures.

 

Artificial Intelligence Study on Medicine #2 Title: Artificial Title:  Artificial intelligence intelligence:: Developments iin n medicine in the last two years Author(s): Rezida Maratovna Galimova, Igor Vyacheslavovich Buzaev, Kireev Ayvar Ramilevich, Lev Khadyevich, Aigul Fazirovna Shaykhulova ISBN / Reference URL: https://doi. https://doi.org/10.1016/j.cdtm org/10.1016/j.cdtm.2018.11.004 .2018.11.004 Problem: There are some functions of comprehension and reasoning that humans can not accomplish as  perfectly as they they like or should be able to. S Such uch activities activities are closely li linked nked to protect protection ion and transpare transparency. ncy. Procedure: Computer analysis of the dataset was carried out in this study. The dataset contains the names of  all the contributors, all the mesh tags, the year, the language, the title and abstract terms. This knowledge was extracted from tables that used a unique key to connect to a table of abstracts. Conclusion/Findings: In historically challenging areas of machine learning, deep learning has achieved  breakthroughs, such as near-human-level image detection, near-human-level speech recognition, digital assistants such as Google Now and Amazon Alexa, near-human-level autonomous driving, enhanced Web search performance, the ability to answer questions about natural language, and superhuman Go play. Benefits: In current practice, examples of AI implementation are now open to more individuals than just experts exper ts in math mathemat ematics. ics. For more conventional conventional scie scientist ntists, s, methods methods for machine machine lear learning ning are read readily ily accessible. They were primarily licensed for medical image processing and displayed comparable precision to human specialists. Opinion about the study: Transferring a mind from a sick and mortal human body to the upgradable, connectable, and easy-to-fix computer body by scanning parameters of neurons and synapses and then replicating them in the AI machine will be the most amazing possible use of AI in medicine.

 

Artificial Intelligence Study on Medicine #3 Tit Title: le:  Rapid Response Electroence Electroencephalograph phalograph with Artificial Intelligence for Diagnosing Seizures and   Highly Epileptiform Epileptiform Patterns Patterns in Emer Emergency gency Medicine Author(s): J. Parvizi, B. Kamousi, S. Karunakaran, J. Quinn, R. Woo ISBN / Reference URL: https://doi. https://doi.org/10.1016/j.anne org/10.1016/j.annemergmed.2020.09. mergmed.2020.09.120 120 Problem: Testing the efficiency of a newly FDA-cleared machine learning system which produces bedside warnings for potential epileptic status and tests the burden of seizure activity in real time. Procedure: A retrospective analysis of electroencephalographs electroencephalographs from adult patients (n=353) who underwent Rapid Response EEG system monitoring was planned. Throughout each recording, they created a machine learning system for automatic detection of seizure activity and seizure burden. Sensitivity and specificity of   potential epileptic status (seizures> 5 minutes) and measurements of real-time seizure burden were measured according to the majority opinion of at least 2 specialist neurologists examining the same EEGs. Conclusion/Fin Conclusion /Findings dings:: Our new algo algorith rithm m is a valuable valuable method for triaging EEGs in emergenc emergency y care settings by incorporating high sensitivity for epileptic status events coupled with a high negative predictive value for negative incidents. The triage of these critically ill patients will be expedited and their care will be expedited by confirming cases of non-convulsive subclinical epileptic status cases within minutes of their  arrival in the emergency department and independently of neurologists and EEG technicians. Benefits: During EEG reco Benefits: recording rdingss for the identifi identificati cation on of patients patients with seizure seizures, s, the sens sensitiv itivity ity and  precision of different seizure burden levels were 100 percent and 82 percent for 50 percent seizure burden and 88 percent and 60 percent for 10 percent seizure burden. The specific exclusion of seizures in cases suspected of having seizures will help avoid excessive or aggressive over-treatment over-treatment of such patients. Opinion about the study: This AI solution is efficient in diagnosing seiz seizures ures in an early manner that will  benefit the patient patient in the long run as well as as the future patients patients to come. come.

 

Artificial Intelligence Study on Education #1 Title: Artificial Title:  Artificial intelligence innovation in educ education: ation: A twe twenty-year nty-year data-dri data-driven ven historical analysis Author(s): Chong Guan, Jian Mou, Zhiying Jiang ISBN / Reference URL: https://doi. https://doi.org/10.1016/j.ij org/10.1016/j.ijis.2020.09.001 is.2020.09.001 Problem: The weak Problem: weakness ness of AIE AIEd d as a research research area with single-jour single-journal nal bibliome bibliometric tric analysi analysiss is its representativeness. The problem with a shorter time-based analysis is that it only represents the current developme devel opments nts in AIE AIEd. d. Therefor Therefore, e, this thes thesis is believes believes that a temporar temporary y multiple multiple-jour -journal nal bibliome bibliometric tric analysis is necessary to compile the evolution of AIEd research over the past two decades. Procedure Proced ure:: In the pre presen sentt rev review iew,, throug through h a system systemati aticc exa examin minati ation on of the liter literatu ature re pub publis lished hed in authorita autho ritative tive journal journalss over the last two decades, decades, we sough soughtt to explore the past researc research h themes themes and developments in the field of AI and DL applications in education. To retrieve and compile journal papers on major publishers' databases, including the Association for Computing Machinery (ACM), EBSCO, Emerald, IEEE, JSTOR, ScienceDirect, Taylor & Francis, and Wiley, we use an automated process (webscrapping).

Conclusion/Findings: It is important to recognize that EdTech is not only about technology, but also about education in its pedagogical, cultural, social, economic, ethical and psychological aspects. In this sense, AIEd research is cross-disciplinary in nature and a paradigm shift in AIEd research may be caused by any significant development in the above disciplines. Benefits: The knowledge of the teaching-learning processes may be expanded by learning analytics and help to improve learning activities. Studies have explored at a macro level how the insights extracted from  big data analytics analytics on pedagogical ttrends rends could help inform the des design ign of instruction. instruction. Opinion about the study: Learning analytics allow students to determine their own individual competencies and thus directly receive help. The 'technology-centric' approach would work effectively, with a stronger  focus on the use of software to solve particular pedagogical issues, such as instructional agents and  personalized learning. learning.

 

Artificial Intelligence Study on Education #2 Titl Title: e:  Intelligent information processing for language education: The use of artificial intelligence in language learning apps Author(s): Marcel Pikhart ISBN / Reference URL: https://doi. https://doi.org/10.1016/j.proc org/10.1016/j.procs.2020.09.151 s.2020.09.151 Problem: When talking about education, smartphones must be taken into account as part of their existence,  because all their free time is dedicated to these smart devices, educational apps could incorporate any aspect of learning when using these devices. Procedure: The study focused on 10 smartphone applications based on both the Android platform and iOS that are used for learning a foreign language. Based on the number of downloads, they were selected, i.e., ten of the most downloaded apps were checked. Some of them have over 100 million downloads and we can say they will possibly have more than 100 million downloads. There are no exact user numbers available, so the number of downloads was the primary predictor for evaluating these applications. Conclusion/Findings: Testing the user on the basis of their progressive growth will be the most critical feature of AI applied in language apps, i.e., grammar exercises, for example, which would be changed and replicated until the user can apply the specified rules effectively. The same can be achieved in the learning of vocabulary, i.e., the new word will be checked in different contexts by the user before they know the use and context of the given word. Benefits: A big advantage of AI in mobile apps, perhaps the most important thing is that no human instructor can process so much data about individual students and the words or grammar required by each of them. Opinion about the study: As can be seen in other areas of human life, the potential growth of the use of AI in mobile apps for learning a foreign language would definitely be very abrupt. It would be very demanding to introduce AI in them in terms of time and resources, but the advantages are significant in the long run,  both for the consumer consumer and the com company pany providing the AI solution.

 

Artificial Intelligence Study on Education#3 Title: Vision, challenges, roles and research issues of Artificial Intelligence in Education Author(s): Gwo-Jen Hwang, Haoran Xie, Benjamin Wah, Dragan Gasevic ISBN / Reference URL: https://doi. https://doi.org/10.1016/j.cae org/10.1016/j.caeai.2020.100001 ai.2020.100001 Problem: From the perspective of precision education, which emphasizes the need to provide individual learners with prevention and intervention practices by analyzing their learning status or behaviors, allowing learning lear ning systems to serv servee as an inte intellig lligent ent tutor by incorporat incorporating ing the knowledg knowledgee and intelligence intelligence of  experienced teachers into the system's decision-making process is a crucial issue. Procedure: New appearances and opportunities for applying AI to teaching and learning design have been generated by the development of emerging computer technologies, such as quantum computing, wearable devices, robot control, and sensing devices, as well as by the popularity of mobile and 5G wireless communication technologies. Therefore, Therefore, several future AIED research questions are posed. Conclusion/Findings: Conclusion/Findi ngs: The growth of AI has taken computer-supported education to a new age. A computer  system may serve as an intelligent teacher, instrume instrument, nt, or tutor by integrating human intelligence, intelligence, as well as  promoting decision making in educational settings. The incorporation of AI and education would open up new possibilities for the standard of teaching and learning to be greatly improved. Benefits: Intelligent systems that assist in evaluation, data collection, improving learning success, and implementing new techniques will help teachers. In supporting learning outcomes, students may benefit from smart tutors and asynchronous learning. Opinion about the study: In addition to transforming education, the incorporation of AI and education is a transformation of human intelligence, cognition, and cultures as well. As such, in the field of computers and education, AI in Education is becoming a primary research subject.