scholarly journals Artificial Intelligence in Pharma

2021 ◽  
Author(s):  
Keziah Ann Babu ◽  
Shirlin MS ◽  
Manjula Devi AS
Keyword(s):  
Artificial Intelligence ◽  
Dosage Forms ◽  
Ethical Concerns ◽  
The Past ◽  
Complex Information ◽  
Rational Drug ◽  
Healthcare It ◽  
Complex Information Processing ◽  
Multiple Levels ◽  
Human Intellect

This paper aims to provide a better understanding of the possible applications of Artificial Intelligence (AI) in the field of Pharmacy. Artificial intelligence basically involves complex information processing. It enables machines to carry out certain functions which were originally done by human and which required human intellect. Over the past few years, the use of artificial intelligence in the pharma has grown unimaginably and is now able to revolutionize the way drugs are discovered and handled. AI is capable of impacting the field of pharmacy at multiple levels. Pharmacy and medical education, drug development, personalization of drug therapy, drug safety, rational drug use, manufacture and formulation of dosage forms are areas of impact by AI. The fact that AI can displace humans at their own tasks is a matter of concern. Although there are ethical concerns regarding the implementation of AI in healthcare, it can be assured that no machine or system can replace many of the humanitarian duties.

The Changing Roles of Teachers With AI

2020 ◽  
pp. 1-25
Author(s):  
Libi Shen ◽  
Anchi Su
Keyword(s):  
Artificial Intelligence ◽  
Technology In Education ◽  
Human Beings ◽  
Educational Settings ◽  
Ethical Concerns ◽  
The Past ◽  
Changing Roles

Artificial intelligence (AI) is ubiquitous in our lives and is progressing at an accelerated rate in the past 60 years. AI application is diverse and AI technology continues to grow. It enables a machine to think like human beings and has opened a new horizon for industries, businesses, transportation, hospitals, and schools. How is AI applied to educational settings? How will the emergence of AI technology assist teachers' teaching and improve students' learning? Will the implementation of AI technology in education replace schoolteachers? What would be the ethical concerns of AI technology? What role do teachers play with AI in education? The purpose of this chapter is to explore the roles that teachers play in the innovation and evolution of AI and to seek approaches teachers should take in coping with AI technology. Issues and problems of teaching with AI will be discussed; solutions will be recommended.

Search, sense making and learning: closing gaps

2019 ◽  
Vol 120 (1/2) ◽  
pp. 74-86 ◽  
Author(s):  
Gary Marchionini
Keyword(s):  
Information Needs ◽  
New Technologies ◽  
Sense Making ◽  
Content Type ◽  
The Past ◽  
Complex Information ◽  
Electronic Search ◽  
Multiple Levels ◽  
Diverse Data ◽  
Search Systems

PurposeThis paper aims to discuss how search, sense making and learning have become more closely integrated, as search services have leveraged new technologies and large and media-diverse data streams.Design/methodology/approachThe paper reviews progress in search over the past 60 years, summarizes different theories of sense making and learning and proposes a framework for integrating these activities.FindingsThe arguments are supported with examples from search in 2018 and suggest that even as search becomes an automated process during learning, search strategies must continue to evolve to insure that complex information needs can be met.Research limitations/implicationsThe work is limited to search that uses electronic search systems. Implications include the need to understand that multiple levels of system inferences/estimates are used to present search results and that different kinds of learning processes are affected by search systems.Social implicationsThe importance of information literacy is implied.Originality/valueThis paper will provide readers with an understanding of how search services and systems have evolved and their implications for human learning.

A Qualified Assertion Database for the History of Places

2019 ◽  
Vol 13 (1-2) ◽  
pp. 95-115
Author(s):  
Brandon Plewe
Keyword(s):  
Data Model ◽  
Temporal Change ◽  
Religious Congregations ◽  
Historical Gis ◽  
The Past ◽  
Model Based ◽  
Complex Information ◽  
Invaluable Tool ◽  
History Of ◽  
The Rich

Historical place databases can be an invaluable tool for capturing the rich meaning of past places. However, this richness presents obstacles to success: the daunting need to simultaneously represent complex information such as temporal change, uncertainty, relationships, and thorough sourcing has been an obstacle to historical GIS in the past. The Qualified Assertion Model developed in this paper can represent a variety of historical complexities using a single, simple, flexible data model based on a) documenting assertions of the past world rather than claiming to know the exact truth, and b) qualifying the scope, provenance, quality, and syntactics of those assertions. This model was successfully implemented in a production-strength historical gazetteer of religious congregations, demonstrating its effectiveness and some challenges.

A Review on Fast Dissolving Tablet

2017 ◽  
Vol 8 (03) ◽  
Author(s):  
Sagar T. Malsane ◽  
Smita S. Aher ◽  
R. B. Saudagar
Keyword(s):  
Drug Delivery ◽  
Patient Compliance ◽  
Research Area ◽  
Oral Route ◽  
Dosage Forms ◽  
The Novel ◽  
The Past ◽  
Orally Disintegrating Tablets ◽  
Novel Drug ◽  
Experience Difficulty

Oral route is presently the gold standard in the pharmaceutical industry where it is regarded as the safest, most economical and most convenient method of drug delivery resulting in highest patient compliance. Over the past three decades, orally disintegrating tablets (FDTs) have gained considerable attention due to patient compliance. Usually, elderly people experience difficulty in swallowing the conventional dosage forms like tablets, capsules, solutions and suspensions because of tremors of extremities and dysphagia. In some cases such as motion sickness, sudden episodes of allergic attack or coughing, and an unavailability of water, swallowing conventional tablets may be difficult. One such problem can be solved in the novel drug delivery system by formulating “Fast dissolving tablets” (FDTs) which disintegrates or dissolves rapidly without water within few seconds in the mouth due to the action of superdisintegrant or maximizing pore structure in the formulation. The review describes the various formulation aspects, superdisintegrants employed and technologies developed for FDTs, along with various excipients, evaluation tests, marketed formulation and drugs used in this research area.

The Future of Work and the Changing Workplace

2018 ◽  
Author(s):  
Mahesh K. Joshi ◽  
J.R. Klein
Keyword(s):  
Artificial Intelligence ◽  
Machine Intelligence ◽  
Political Issue ◽  
Digital Innovation ◽  
Future Of Work ◽  
The Past ◽  
The World ◽  
Public Debates ◽  
The Future ◽  
Market Opportunities

The world of work has been impacted by technology. Work is different than it was in the past due to digital innovation. Labor market opportunities are becoming polarized between high-end and low-end skilled jobs. Migration and its effects on employment have become a sensitive political issue. From Buffalo to Beijing public debates are raging about the future of work. Developments like artificial intelligence and machine intelligence are contributing to productivity, efficiency, safety, and convenience but are also having an impact on jobs, skills, wages, and the nature of work. The “undiscovered country” of the workplace today is the combination of the changing landscape of work itself and the availability of ill-fitting tools, platforms, and knowledge to train for the requirements, skills, and structure of this new age.

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

scholarly journals Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI

2021 ◽  
pp. medethics-2020-106820 ◽  
Author(s):  
Juan Manuel Durán ◽  
Karin Rolanda Jongsma
Keyword(s):  
Artificial Intelligence ◽  
Medical Informatics ◽  
Patient Autonomy ◽  
Black Box ◽  
Epistemic Reasons ◽  
Potential Bias ◽  
Ethical Concerns ◽  
Normative Issues ◽  
Ethical Basis ◽  
Computational Processes

The use of black box algorithms in medicine has raised scholarly concerns due to their opaqueness and lack of trustworthiness. Concerns about potential bias, accountability and responsibility, patient autonomy and compromised trust transpire with black box algorithms. These worries connect epistemic concerns with normative issues. In this paper, we outline that black box algorithms are less problematic for epistemic reasons than many scholars seem to believe. By outlining that more transparency in algorithms is not always necessary, and by explaining that computational processes are indeed methodologically opaque to humans, we argue that the reliability of algorithms provides reasons for trusting the outcomes of medical artificial intelligence (AI). To this end, we explain how computational reliabilism, which does not require transparency and supports the reliability of algorithms, justifies the belief that results of medical AI are to be trusted. We also argue that several ethical concerns remain with black box algorithms, even when the results are trustworthy. Having justified knowledge from reliable indicators is, therefore, necessary but not sufficient for normatively justifying physicians to act. This means that deliberation about the results of reliable algorithms is required to find out what is a desirable action. Thus understood, we argue that such challenges should not dismiss the use of black box algorithms altogether but should inform the way in which these algorithms are designed and implemented. When physicians are trained to acquire the necessary skills and expertise, and collaborate with medical informatics and data scientists, black box algorithms can contribute to improving medical care.

scholarly journals Boundaries Between Research Ethics and Ethical Research Use in Artificial Intelligence Health Research

2021 ◽  
pp. 155626462110027
Author(s):  
Gabrielle Samuel ◽  
Jenn Chubb ◽  
Gemma Derrick
Keyword(s):  
Artificial Intelligence ◽  
Health Research ◽  
Decision Makers ◽  
Societal Impact ◽  
The Past ◽  
Research And Innovation ◽  
The United Kingdom ◽  
Responsible Research ◽  
Past Half

The governance of ethically acceptable research in higher education institutions has been under scrutiny over the past half a century. Concomitantly, recently, decision makers have required researchers to acknowledge the societal impact of their research, as well as anticipate and respond to ethical dimensions of this societal impact through responsible research and innovation principles. Using artificial intelligence population health research in the United Kingdom and Canada as a case study, we combine a mapping study of journal publications with 18 interviews with researchers to explore how the ethical dimensions associated with this societal impact are incorporated into research agendas. Researchers separated the ethical responsibility of their research with its societal impact. We discuss the implications for both researchers and actors across the Ethics Ecosystem.

The Future of the Planetary Trust in a Kaleidoscopic World

2021 ◽  
pp. 1-8
Author(s):  
Edith Brown Weiss
Keyword(s):  
Artificial Intelligence ◽  
Natural Resource ◽  
Human Activities ◽  
New Technologies ◽  
Future Generations ◽  
Nuclear War ◽  
Human Environment ◽  
The Past ◽  
Resource Degradation ◽  
Shared Goal

Today, it is evident that we are part of a planetary trust. Conserving our planet represents a public good, global as well as local. The threats to future generations resulting from human activities make applying the normative framework of a planetary trust even more urgent than in the past decades. Initially, the planetary trust focused primarily on threats to the natural system of our human environment such as pollution and natural resource degradation, and on threats to cultural heritage. Now, we face a higher threat of nuclear war, cyber wars, and threats from gene drivers that can cause inheritable changes to genes, potential threats from other new technologies such as artificial intelligence, and possible pandemics. In this context, it is proposed that in the kaleidoscopic world, we must engage all the actors to cooperate with the shared goal of caring for and maintaining planet Earth in trust for present and future generations.

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