Neurologic Diagnostics in 2035

Neurology ◽  
2021 ◽  
Vol 97 (19) ◽  
pp. 902-907 ◽  
Author(s):  
Olga Ciccarelli ◽  
Massimo Pandolfo
Keyword(s):  
Artificial Intelligence ◽  
Clinical Judgment ◽  
Clinical Skills ◽  
Assistive Technologies ◽  
Diagnostic Tools ◽  
Diagnostic Process ◽  
Neurologic Diseases ◽  
Novel Technologies ◽  
The Past ◽  
Diagnostic Applications

Innovations and advances in technologies over the past few years have yielded faster and wider diagnostic applications to patients with neurologic diseases. This article focuses on the foreseeable developments of the diagnostic tools available to the neurologist in the next 15 years. Clinical judgment is and will remain the cornerstone of the diagnostic process, assisted by novel technologies, such as artificial intelligence and machine learning. Future neurologists must be educated to develop, cultivate, and rely on their clinical skills, while becoming familiar with novel, often complex, assistive technologies.

scholarly journals Is Artificial Intelligence Transforming Dentistry Today?

2021 ◽  
Vol 8 (4) ◽  
pp. 1
Author(s):  
Saman Tauqir
Keyword(s):  
Artificial Intelligence ◽  
Human Brain ◽  
Clinical Skills ◽  
Dental Education ◽  
Maxillofacial Surgery ◽  
Thin Layers ◽  
Brain Functions ◽  
The Past ◽  
Dental Procedures ◽  
The Future

Since the birth of science, the most fascinating structure of the human body is the human brain.  Over the past centuries’ researchers have been developing the latest technologies to imitate and explore how the human brain functions. However, to develop a machine that thinks like a human brain is still a dream for researchers. Aristotle’s early efforts to devise logical thinking via his syllogisms (a three-part deductive reasoning) were a source of inspiration for modern computers and technologies1. In the1950, Alan Turing designed a machine to decode encrypted messages, which was a breakthrough of super computers in the days of yore. He designed the “Turing Test” which was coined to assess whether a computer could exhibit intelligence better known as “artificial intelligence” (AI) today2. AI is “a field of science and engineering concerned with the computational understanding of what is commonly called intelligent behavior, and with the creation of artifacts that exhibit such behaviour”3. Since 1980, AI has come a long way, virtual reality is being used in dental education these days to create real life situations and promote clinical work on simulators to eliminate risk factors associated with training on live patients. Recently artificial intelligence has been integrated with tutoring systems like “Unified Medical Language System” (UMLS), which have resulted in a better quality of feedback, which the preclinical virtual patients provide to the students4,5. This interactive phase helps students to evaluate their clinical skills and compare their skills with the standard ones, thus creating an ideal and high-quality training environment. Studies have been carried out regarding the efficacy of AI systems, which have stipulated that preclinical students build higher competencies than with the use of traditional simulator units6-8. Currently AI inbuilt virtual dental assistants are present in the market. They can execute various chair side tasks with greater accuracy and less manpower ensuring minimum error during the procedures. In the world of implantology and maxillofacial surgery AI helps plan and prepare surgeries with smallest details forgoing actual surgery. Some exceptional uses of AI include robotic surgeries in the field of maxillofacial surgery and bioprinting (where tissues and organs can be reconstructed in thin layers)9. The field of AI has flourished to great extent in the past decade; AI systems are an aid to the field of dentistry and dental education.  This narrative attempts to explain possible AI-based applications in the future, it can be used for dental diagnosis, planning out treatments, conducting image analysis, and record keeping. AI-based technologies streamline and reduce laborious workforce to routine tasks, it ensures dental procedures are possible at a lower cost and ultimately makes predictive, preventive, and participatory dentistry possible. The use of AI in dental procedures needs to be guaranteed; its application with human oversight and evidence-based dentistry shall be expected. Dental education needs to be introduced to clinical AI solutions by promoting digital literacy in the future dental liveware.

scholarly journals Clinical Skill: The Ebbing Art of Medicine

2021 ◽  
Vol 28 (1) ◽  
pp. 105-108
Author(s):  
Ananda Datta
Keyword(s):  
Clinical Judgment ◽  
Clinical Medicine ◽  
Clinical Skills ◽  
Clinical Skill ◽  
Clinical History ◽  
Diagnostic Tools ◽  
Patient Relationship ◽  
Physician Patient Relationship ◽  
Tools And Techniques ◽  
Physician Patient

Clinical history taking and physical examination are the essence of clinical medicine. However, the glare of modern diagnostic tools and techniques has overshadowed these basic but indispensable steps of diagnosis. Deterioration of clinical skills is a burning issue in this era due to over-reliance on high-end technology. Poor clinical judgment not only leads to mismanagement but also results in over-utilisation of health care resources. Moreover, with lesser time at the bedside, the physician-patient relationship is also getting compromised.

scholarly journals Artificial Intelligence in Capsule Endoscopy: A Practical Guide to Its Past and Future Challenges

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1722
Author(s):  
Sang Hoon Kim ◽  
Yun Jeong Lim
Keyword(s):  
Artificial Intelligence ◽  
Capsule Endoscopy ◽  
Reading Time ◽  
Diagnostic Process ◽  
The Past ◽  
Computer Aided ◽  
Future Challenges ◽  
Medical Diagnostic ◽  
Reading System ◽  
Near Future

Artificial intelligence (AI) has revolutionized the medical diagnostic process of various diseases. Since the manual reading of capsule endoscopy videos is a time-intensive, error-prone process, computerized algorithms have been introduced to automate this process. Over the past decade, the evolution of convolutional neural network (CNN) enabled AI to detect multiple lesions simultaneously with increasing accuracy and sensitivity. Difficulty in validating CNN performance and unique characteristics of capsule endoscopy images make computer-aided reading systems in capsule endoscopy still on a preclinical level. Although AI technology can be used as an auxiliary second observer in capsule endoscopy, it is expected that in the near future, it will effectively reduce the reading time and ultimately become an independent, integrated reading system.

Artificial intelligence in molecular biology: a review and assessment

1994 ◽  
Vol 344 (1310) ◽  
pp. 353-363 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Structure Prediction ◽  
Genetic Maps ◽  
Novel Technologies ◽  
The Past ◽  
Knowledge Based ◽  
Automated Learning ◽  
Learning Techniques ◽  
Computer Scientists ◽  
Active Genes

Over the past ten years, molecular biologists and computer scientists have experimented with various computational methods developed in artificial intelligence (AI). AI research has yielded a number of novel technologies, which are typified by an emphasis on symbolic (non-numerical) programming methods aimed at problems which are not amenable to classical algorithmic solutions. Prominent examples include knowledge-based and expert systems, qualitative simulation and artificial neural networks and other automated learning techniques. These methods have been applied to problems in data analysis, construction of advanced databases and modelling of biological systems. Practical results are now being obtained, notably in the recognition of active genes in genomic sequences, the assembly of physical and genetic maps and protein structure prediction. This paper outlines the principal methods, surveys the findings to date, and identifies the promising trends and current limitations.

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 Morphological Characteristics, Classifications and Difficulties in the Use of Diagnostic Criteria for Serrated Lesions of the Large Intestine

2021 ◽  
Author(s):  
Cesar de Souza Bastos Junior ◽  
Vera Lucia Nunes Pannain ◽  
Adriana Caroli-Bottino
Keyword(s):  
Diagnostic Criteria ◽  
Morphological Characteristics ◽  
World Health ◽  
Diagnostic Tools ◽  
Serrated Adenoma ◽  
The Past ◽  
The World ◽  
Serrated Lesions ◽  
Health Organization

Abstract Introduction Colorectal carcinoma (CRC) is the most common gastrointestinal neoplasm in the world, accounting for 15% of cancer-related deaths. This condition is related to different molecular pathways, among them the recently described serrated pathway, whose characteristic entities, serrated lesions, have undergone important changes in their names and diagnostic criteria in the past thirty years. The multiplicity of denominations and criteria over the last years may be responsible for the low interobserver concordance (IOC) described in the literature. Objectives The present study aims to describe the evolution in classification of serrated lesions, based on the last three publications of the World Health Organization (WHO) and the reproducibility of these criteria by pathologists, based on the evaluation of the IOC. Methods A search was conducted in the PubMed, ResearchGate and Portal Capes databases, with the following terms: sessile serrated lesion; serrated lesions; serrated adenoma; interobserver concordance; and reproducibility. Articles published since 1990 were researched. Results and Discussion The classification of serrated lesions in the past thirty years showed different denominations and diagnostic criteria. The reproducibility and IOC of these criteria in the literature, based on the kappa coefficient, varied in most studies, from very poor to moderate. Conclusions Interobserver concordance and the reproducibility of microscopic criteria may represent a limitation for the diagnosis and appropriate management of these lesions. It is necessary to investigate diagnostic tools to improve the performance of the pathologist's evaluation, for better concordance, and, consequently, adequate diagnosis and treatment.

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.

scholarly journals Modern Diagnosis of Early Esophageal Cancer: From Blood Biomarkers to Advanced Endoscopy and Artificial Intelligence

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3162
Author(s):  
Pierfrancesco Visaggi ◽  
Brigida Barberio ◽  
Matteo Ghisa ◽  
Mentore Ribolsi ◽  
Vincenzo Savarino ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Esophageal Cancer ◽  
Clinical Symptoms ◽  
Survival Rates ◽  
Screening Strategy ◽  
Cancer Surveillance ◽  
Blood Biomarkers ◽  
Early Esophageal Cancer ◽  
Novel Technologies ◽  
Bolus Impaction

Esophageal cancer (EC) is the seventh most common cancer and the sixth cause of cancer death worldwide. Histologically, esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) account for up to 90% and 20% of all ECs, respectively. Clinical symptoms such as dysphagia, odynophagia, and bolus impaction occur late in the natural history of the disease, and the diagnosis is often delayed. The prognosis of ESCC and EAC is poor in advanced stages, being survival rates less than 20% at five years. However, when the diagnosis is achieved early, curative treatment is possible, and survival exceeds 80%. For these reasons, mass screening strategies for EC are highly desirable, and several options are currently under investigation. Blood biomarkers offer an inexpensive, non-invasive screening strategy for cancers, and novel technologies have allowed the identification of candidate markers for EC. The esophagus is easily accessible via endoscopy, and endoscopic imaging represents the gold standard for cancer surveillance. However, lesion recognition during endoscopic procedures is hampered by interobserver variability. To fill this gap, artificial intelligence (AI) has recently been explored and provided encouraging results. In this review, we provide a summary of currently available options to achieve early diagnosis of EC, focusing on blood biomarkers, advanced endoscopy, and AI.

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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