scholarly journals Automation in pharmaceutical sector by implementation of artificial intelligence platform: a way forward

2021 ◽  
Vol 10 (7) ◽  
pp. 863
Author(s):  
Amol Shinde ◽  
Dilip Pawar ◽  
Kunal Sonawane
Keyword(s):  
Artificial Intelligence ◽  
Drug Safety ◽  
Health Care Professionals ◽  
Medical Information ◽  
Safety Data ◽  
Individual Case ◽  
Customer Engagement ◽  
Innovative Technology ◽  
Trial Quality ◽  
Technological Advances

Worldwide, there are technological advances that swift automation in several aspects of the pharmaceutical industry such as pharmacovigilance, clinical research, medical affairs, and marketing. Innovative technology like artificial intelligence (AI) emphasizes the massive use of the internet for drug development, drug safety, data analytics, communication marketing, and customer engagement to achieve the goal of pharmaceuticals and patient-centric healthcare. Presently, escalating the number of individual case safety reports (ICSRs) necessitate the support of AI in the transformation of drug safety professional. AI can be transformed and evolve the clinical trial process from the conventional method alongside benefited the cutting cost, enhancing the trial quality, and alleviate trial time by almost half. Today, AI may be efficiently implemented to lower the cost of medical information requests, besides the online chatbots to communicate with health care professionals (HCPs) and consumers. There are numerous forthcoming uses of AI which need to be executed for renovation in the field of pharmaceuticals.

scholarly journals Legislation and current developments in adverse drug reaction reporting in Mongolia: how far are we?

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Zuzaan Zulzaga ◽  
Erdenetuya Myagmarsuren ◽  
Herman J. Woerdenbag ◽  
Eugene P. van Puijenbroek
Keyword(s):  
Adverse Drug Reaction ◽  
Drug Reaction ◽  
Drug Safety ◽  
Reporting System ◽  
Adverse Drug Reaction Reporting ◽  
Safety Data ◽  
Regulatory System ◽  
Basic Structure ◽  
Reaction Reporting ◽  
Adr Reporting

AbstractMonitoring adverse drug reactions is a vital issue to ensure drug safety and to protect the general public from medication-related harmful effects. In order to properly monitor drug safety, a regulatory system needs to be in place as well as an infrastructure that allows for analyzing national and international safety data. In Mongolia, adverse drug reaction (ADR) reporting activities have been implemented in the past decade. During this period, the basic structure and legal basis of an adverse drug reaction monitoring system was established. Because of the fragmented but growing healthcare system and the complexity of pharmaceutical issues in Mongolia, a sustainable process for the development of the adverse drug reaction reporting system is a key issue. The aim of this article is to disclose the Mongolian situation for the rest of the world and to share experiences on how an ADR reporting system can be developed towards a higher and more advanced level to contribute to both national and international drug safety issues. In this article, we review the features of the Mongolian health care and pharmaceutical systems, as well as the current development of the adverse drug reaction reporting system.

scholarly journals Reviewing the relationship between machines and radiology: the application of artificial intelligence

2021 ◽  
Vol 10 (2) ◽  
pp. 205846012199029
Author(s):  
Rani Ahmad
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Health Care Professionals ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Health Science ◽  
Computer Algorithms ◽  
Learning Models ◽  
Specificity And Sensitivity

Background The scope and productivity of artificial intelligence applications in health science and medicine, particularly in medical imaging, are rapidly progressing, with relatively recent developments in big data and deep learning and increasingly powerful computer algorithms. Accordingly, there are a number of opportunities and challenges for the radiological community. Purpose To provide review on the challenges and barriers experienced in diagnostic radiology on the basis of the key clinical applications of machine learning techniques. Material and Methods Studies published in 2010–2019 were selected that report on the efficacy of machine learning models. A single contingency table was selected for each study to report the highest accuracy of radiology professionals and machine learning algorithms, and a meta-analysis of studies was conducted based on contingency tables. Results The specificity for all the deep learning models ranged from 39% to 100%, whereas sensitivity ranged from 85% to 100%. The pooled sensitivity and specificity were 89% and 85% for the deep learning algorithms for detecting abnormalities compared to 75% and 91% for radiology experts, respectively. The pooled specificity and sensitivity for comparison between radiology professionals and deep learning algorithms were 91% and 81% for deep learning models and 85% and 73% for radiology professionals (p < 0.000), respectively. The pooled sensitivity detection was 82% for health-care professionals and 83% for deep learning algorithms (p < 0.005). Conclusion Radiomic information extracted through machine learning programs form images that may not be discernible through visual examination, thus may improve the prognostic and diagnostic value of data sets.

scholarly journals From Data Journalism to Artificial Intelligence: Challenges Faced by La Nación in Implementing Computer Vision in News Reporting

2021 ◽  
Vol 24 (3) ◽  
pp. 1-40
Author(s):  
Mathias-Felipe de-Lima-Santos ◽  
Ramón Salaverría
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Latin American ◽  
Implementation Process ◽  
Media Industry ◽  
Technological Infrastructure ◽  
News Reporting ◽  
Technological Advances ◽  
High Resolution Imagery ◽  
Data Journalism

Journalism is at a radical point of change that requires organizations to come up with new ideas and formats for news reporting. Additionally, the notable surge of data, sensors and technological advances in the mobile segment has brought immeasurable benefits to many fields of journalistic practice (data journalism in particular). Given the relative novelty and complexity of implementing artificial intelligence (AI) in journalism, few areas have managed to deploy tailored AI solutions in the media industry. In this study, through a mixed-method approach that combines both participant observations and interviews, we explain the hurdles and obstacles to deploying computer vision news projects, a subset of AI, in a leading Latin American news organization, the Argentine newspaper La Nación. Our results highlight four broad difficulties in implementing computer vision projects that involve satellite imagery: a lack of high-resolution imagery, the unavailability of technological infrastructure, the absence of qualified personnel to develop such codes, and a lengthy and costly implementation process that requires significant investment. This article concludes with a discussion of the centrality of AI solutions in the hands of big tech corporations.

scholarly journals The Legal Aspects of Artificial Intelligence based on the EU Experience

Law and World ◽  
2021 ◽  
Vol 7 (5) ◽  
pp. 8-13
Keyword(s):  
Artificial Intelligence ◽  
Legal Framework ◽  
Legal Aspects ◽  
Public And Private ◽  
Legal Questions ◽  
Technological Advances ◽  
Clear Vision ◽  
Routine Tasks ◽  
The One ◽  
The Eu

In the digital era, technological advances have brought innovative opportunities. Artificial intelligence is a real instrument to provide automatic routine tasks in different fields (healthcare, education, the justice system, foreign and security policies, etc.). AI is evolving very fast. More precisely, robots as re-programmable multi-purpose devices designed for the handling of materials and tools for the processing of parts or specialized devices utilizing varying programmed movements to complete a variety of tasks.1 Regardless of opportunities, artificial intelligence may pose some risks and challenges for us. Because of the nature of AI ethical and legal questions can be pondered especially in terms of protecting human rights. The power of artificial intelligence means using it more effectively in the process of analyzing big data than a human being. On the one hand, it causes loss of traditional jobs and, on the other hand, it promotes the creation of digital equivalents of workers with automatic routine task capabilities. “Artificial intelligence must serve people, and therefore artificial intelligence must always comply with people’s rights,” said Ursula von der Leyen, President of the European Commission.2 The EU has a clear vision of the development of the legal framework for AI. In the light of the above, the article aims to explore the legal aspects of artificial intelligence based on the European experience. Furthermore, it is essential in the context of Georgia’s European integration. Analyzing legal approaches of the EU will promote an approximation of the Georgian legislation to the EU standards in this field. Also, it will facilitate to define AI’s role in the effective digital transformation of public and private sectors in Georgia.

scholarly journals The healthcare technology needs you

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Sumarno Adi Subrata ◽  
Jonathan Bayuo ◽  
Busra Sahin
Keyword(s):  
Artificial Intelligence ◽  
3D Printing ◽  
Standard Treatment ◽  
Healthcare Services ◽  
Fundamental Aspect ◽  
Innovative Technology ◽  
Community Settings ◽  
Healthcare Technology ◽  
Health Related ◽  
Digital Healthcare

The growing evidence and technology in healthcare lead to an improvement in the patient's health across a continuum of services in clinical and community settings. A multidisciplinary team should work in tandem on this phenomenon. Therefore, innovative healthcare technology must be designed intensively to optimize productivity and provide new insight along with support the standard treatment for particular diseases. In the coming years, technology is needed to change the way of caring for the patient. This is a fundamental aspect because the recent technology has shaped up in front of our practice with advances in digital healthcare services, such as 3D printing, robotics, nanotechnology and even artificial intelligence (The Medical Futurist, 2021). To respond to this, updated studies should be developed and published focusing on innovative technology including in Medicine, Nursing, Pharmacy, and other health-related topics.

Could massive open online courses improve health and medical education?

2018 ◽  
Vol 26 (1) ◽  
pp. 61-63 ◽  
Author(s):  
Stefanie Schütte ◽  
Sophie-Hélène Goulet-Ebongue ◽  
Khamsa Habouchi
Keyword(s):  
Medical Education ◽  
Health Care Professionals ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Special Focus ◽  
Massive Open Online ◽  
Middle Income ◽  
The Public ◽  
Middle Income Countries ◽  
Technological Advances

Abstract Technological advances during the last decade have provided novel opportunities for development of health and medical education. Education of health care professionals by massive open online courses (MOOCs) has been suggested in order to improve care and treatment of patients and the health literacy of the public. This article discusses the strengths, weaknesses, opportunities and threats of MOOCs in health and medical education by taking a special focus on low and middle-income countries.

Information Technology in the Search for Employment

2021 ◽  
pp. 252-267
Author(s):  
Pablo Chamoso ◽  
Alfonso González-Briones ◽  
Fancisco José García-Peñalvo
Keyword(s):  
Artificial Intelligence ◽  
Information Technology ◽  
Information Processing ◽  
State Of The Art ◽  
Developed Countries ◽  
Search Results ◽  
Technological Advances ◽  
Career Opportunities ◽  
Job Opportunities

Employability is one of the main concerns of the citizens of developed countries. Over the last 10 years, it has become popular to use technology to find employment and better career opportunities. Currently, there are many technology-powered tools available which offer their users (candidates and companies) the possibility of finding the best job opportunities/employees. However, technology is becoming increasingly advanced and current employment-oriented websites must keep up with those standards. Thanks to the computing and information processing capabilities provided by artificial intelligence, today's websites are not mere directories of jobs and candidates; instead, they make it possible to automatically filter search results according to the characteristics of candidates and jobs. This chapter presents a review of state-of-the-art technologies aimed at improving employability and analyzes the technological advances in this sector.

An artificial-intelligence-based method for assessing service quality: insights from the prosthodontics sector

2020 ◽  
Vol 31 (2) ◽  
pp. 291-312 ◽  
Author(s):  
Sara M. Martins ◽  
Fernando A.F. Ferreira ◽  
João J. M. Ferreira ◽  
Carla S.E. Marques
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Service Quality ◽  
Cognitive Mapping ◽  
Evaluation Model ◽  
Vice President ◽  
Dental Prosthesis ◽  
Content Type ◽  
Technological Advances ◽  
Definition Of

PurposeThe prosthodontics sector is facing major challenges because of scientific and technological advances that imply a clearer definition of lines of action and decision making processes. Measuring quality of service in this sector is a complex decision problem since the perceptions of three main players need to be considered: patients, dentists and dental technicians. This study sought to develop an artificial-intelligence-based (AI-based) method for assessing service quality in the dental prosthesis sector.Design/methodology/approachUsing strategic options development and analysis (SODA), which is grounded on cognitive mapping, and the measuring attractiveness by a categorical based evaluation technique (MACBETH), a constructivist decision support system was designed to facilitate the assessment of service quality in the dental prosthesis sector. The system was tested, and the results were validated both by the members of an expert panel and by the vice-president of the Portuguese association of dental prosthesis technicians.FindingsThe methodological process developed in this study is extremely versatile and its practical application facilitated the development of an empirically robust evaluation model in this study context. Specifically, the profile analyses carried out in actual clinics allowed the cases in which improvements are needed to be identified.Originality/valueAlthough already applied in the fields of AI and decision making, no prior work reporting the use of SODA and MACBETH for assessing service quality in the prosthodontics sector has been found.

scholarly journals Principles and Considerations for Responsible Sharing of Safety Information Via the Medical Information Channel

2019 ◽  
pp. 216847901987214
Author(s):  
Sashka Hristoskova ◽  
James Milligan ◽  
Jan De Wit ◽  
Jukka Pesonen ◽  
Robyn Rennick
Keyword(s):  
Patient Safety ◽  
Health Care Professionals ◽  
Medical Information ◽  
Safety Information ◽  
Public Domain ◽  
European Federation ◽  
Sources Of Information ◽  
The Public ◽  
Need To Evaluate ◽  
Pharmaceutical Industries

The approach used by medical information services in answering unsolicited safety-related questions from health care professionals regarding prescription medicines varies widely across the pharmaceutical industry. A significant amount of information is available in the public domain, but this can be difficult to filter and determine what is most appropriate for a given situation. A team representing the medical information group MILE (Medical Information Leaders Europe) and European Federation of Pharmaceutical Industries and Associations Pharmacovigilance Expert Group have partnered to develop principles and considerations on how to answer unsolicited safety questions. Essentially two key principles are important in ensuring success: (1) Effective collaboration between medical information and patient safety teams is important for an optimal outcome providing accurate, useful, and timely information. This article discusses considerations for an effective, efficient collaboration between medical information and patient safety and suggests a way of working. (2) Collaborating teams will need to evaluate and select the most appropriate sources of information to answer the question. Sources of information that may or may not be in the public domain are discussed. Adoption of principles and considerations discussed in this article may be expected to improve current safety information–sharing practices that tend to be conservative and risk averse. In addition, this presents the opportunity to initiate discussions with regulatory authorities to realize the benefits that will come through greater transparency and communication to support safe and effective use of medicines.

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