Artificial intelligence for dermatopathology: current trends and the road ahead

Author(s):  
Simon B Chen ◽  
Roberto A Novoa
2021 ◽  
Vol 5 (1) ◽  
pp. 1-15
Author(s):  
Rubina Shaheen ◽  
Mir Kasi

The report gives a presents use of artificial intelligence in few administrative agencies. In-depth thematic analysis of some institution, have been conducted to review the current trends. In thematic analysis, 12 institutions have been selected and described the details of the institutions using artificial intelligence in different departments. These analyses yielded five major findings. First, the government has a wide application of Artificial Intelligence toolkit traversing the federal administrative and state. Almost half of the federal agencies evaluated (45%) has used AI and associated machine learning (ML) tools. Also, AI tools are already enhancing agency strategies in  the full span of governance responsibilities, such as keeping regulatory assignments bordering on market efficiency, safety in workplace, health care, and protection of the environmental, protecting the privileges and benefits of the government ranging from intellectual properties to disability, accessing, verifying and analyzing all risks to public  safety and health, Extracting essential data from the data stream of government including complaints by consumer and the communicating with citizens on their rights, welfare, asylum seeking and business ownership. AI toolkit owned by government span the complete scope of Artificial Intelligence techniques, ranging from conventional machine learning to deep learning including natural language and image data. Irrespective of huge acceptance of AI, much still has to be done in this area by the government. Recommendations also discussed at the end.


Author(s):  
David Mendes ◽  
Irene Pimenta Rodrigues

The ISO/HL7 27931:2009 standard intends to establish a global interoperability framework for healthcare applications. However, being a messaging related protocol, it lacks a semantic foundation for interoperability at a machine treatable level intended through the Semantic Web. There is no alignment between the HL7 V2.xml message payloads and a meaning service like a suitable ontology. Careful application of Semantic Web tools and concepts can ease the path to the fundamental concept of Shared Semantics. In this chapter, the Semantic Web and Artificial Intelligence tools and techniques that allow aligned ontology population are presented and their applicability discussed. The authors present the coverage of HL7 RIM inadequacy for ontology mapping and how to circumvent it, NLP techniques for semi-automated ontology population, and the current trends about knowledge representation and reasoning that concur to the proposed achievement.


2021 ◽  
pp. 31-58
Author(s):  
Mumtaz Khan ◽  
Shah Khusro ◽  
Iftikhar Alam
Keyword(s):  
The Road ◽  

Author(s):  
Per E. Jørgensen

Abstract A number of current trends will affect and probably change laboratory medicine, as we know it. Scientific and technological developments, digital health with big data and artificial intelligence, and centralization will change the interfaces among the specialties of laboratory medicine. They might even challenge the identity of some specialties. Other trends such as demographic changes, increased complexity of health care, digital health with electronic health records, and more demanding and well-informed patients will change the way laboratory medicine specialties deliver their services. This paper discusses the possible changes of laboratory medicine in Denmark – a Scandinavian country where almost all hospitals are public. If Danish laboratories grasp the new possibilities instead of trying to avoid them, laboratory medicine is likely to prosper. Such a positive development will call upon good leadership and a genuine willingness among laboratory specialist to adapt to a future where their own specialty might be very different from today.


2017 ◽  
Vol 40 ◽  
Author(s):  
Robert J. Sternberg

AbstractMachines that learn and think like people should simulate how people really think in their everyday lives. The field of artificial intelligence originally traveled down two roads, one of which emphasized abstract, idealized, rational thinking and the other, which emphasized the emotionally charged and motivationally complex situations in which people often find themselves. The roads should have converged but never did. That's too bad.


Cureus ◽  
2021 ◽  
Author(s):  
Mirra Srinivasan ◽  
Santhosh Raja Thangaraj ◽  
Krishnamurthy Ramasubramanian ◽  
Padma Pradha Thangaraj ◽  
Krishna Vyas Ramasubramanian

2021 ◽  
Author(s):  
Naroa Coretti Sanchez ◽  
Juan Múgica Gonzalez ◽  
Luis Alonso Pastor ◽  
Kent Larson

The current trends towards vehicle-sharing, electrification, and autonomy are predicted to transform mobility. Combined appropriately, they have the potential of significantly improving urban mobility. However, what will come after most vehicles are shared, electric, and autonomous remains an open question, especially regarding the interactions between vehicles and how these interactions will impact system-level behaviour. Inspired by nature and supported by swarm robotics and vehicle platooning models, this paper proposes a future mobility in which shared, electric, and autonomous vehicles behave as a bio-inspired collaborative system. The collaboration between vehicles will lead to a system-level behaviour analogous to natural swarms. Natural swarms can divide tasks, cluster, build together, or transport cooperatively. In this future mobility, vehicles will cluster by connecting either physically or virtually, which will enable the possibility of sharing energy, data or computational power, provide services or transfer cargo, among others. Vehicles will collaborate either with vehicles that are part of the same fleet, or with any other vehicle on the road, by finding mutualistic relationships that benefit both parties. The field of swarm robotics has already translated some of the behaviours from natural swarms to artificial systems and, if we further translate these concepts into urban mobility, exciting ideas emerge. Within mobility-related research, the coordinated movement proposed in vehicle platooning models can be seen as a first step towards collaborative mobility. This paper contributes with the proposal of a framework for future mobility that integrates current research and mobility trends in a novel and unique way.


2021 ◽  
Vol 41 (13-14) ◽  
pp. 853-859
Author(s):  
Carlos Flavián ◽  
Luis V. Casaló

2021 ◽  
Author(s):  
Anwaar Ulhaq

Machine learning has grown in popularity and effectiveness over the last decade. It has become possible to solve complex problems, especially in artificial intelligence, due to the effectiveness of deep neural networks. While numerous books and countless papers have been written on deep learning, new researchers want to understand the field's history, current trends and envision future possibilities. This review paper will summarise the recorded work that resulted in such success and address patterns and prospects.


Sign in / Sign up

Export Citation Format

Share Document