The promise of highly personalized oncology care using artificial intelligence (AI) technologies has been forecasted since the emergence of the field. Cumulative advances across the science are bringing this promise to realization, including refinement of machine learning- and deep learning algorithms; expansion in the depth and variety of databases, including multiomics; and the decreased cost of massively parallelized computational power. Examples of successful clinical applications of AI can be found throughout the cancer continuum and in multidisciplinary practice, with computer vision-assisted image analysis in particular having several U.S. Food and Drug Administration-approved uses. Techniques with emerging clinical utility include whole blood multicancer detection from deep sequencing, virtual biopsies, natural language processing to infer health trajectories from medical notes, and advanced clinical decision support systems that combine genomics and clinomics. Substantial issues have delayed broad adoption, with data transparency and interpretability suffering from AI's "black box" mechanism, and intrinsic bias against underrepresented persons limiting the reproducibility of AI models and perpetuating health care disparities.

The comprehensive market report is inclusive of a detailed summary of the competitive analysis of this industry. As per the document, companies along the likes of IBM Corporation, Google LLC, Medtronic Plc, Koninklijke Philips N.V., Nvidia Corporation, Microsoft Corporation, iCarbonX, CloudMedx Inc., Atomwise Inc., and Next IT Corporation are encompassed in the competitive spectrum of the market.

Despite the hype surrounding artificial intelligence (AI) in radiology, paediatric imaging has been neglected compared to other sub-specialties such as breast, oncology or neuroimaging.1 This may be partly due to a comparatively larger workload in adult medicine, conveniently providing large training datasets and thereby potentially greater opportunities to automate routine tasks (e.g., cancer screening applications). There are intrinsically challenging aspects surrounding the practice of paediatric radiology, such as the need for a more'hands-on/ human' approach in many cases (e.g., fluoroscopy and ultrasound studies, keeping children calm during examinations), and greater heterogeneity in data due to wide variations of normal findings at different stages of childhood development. Nevertheless, AI could still prove helpful in enhancing children's imaging services, particularly given the current radiology workforce shortages (only 38.5% of institutions in the UK have 24/7 access to a paediatric radiology opinion)2 and national economic hardships – potentially leading to a vicious cycle of fewer job and training opportunities, with even further lack of access to specialist opinion. In this article, we discuss a variety of possible'use cases' in paediatric radiology where AI has either been implemented already or shown early-stage feasibility, while also taking inspiration from the adult literature to propose areas for future development.

Artificial intelligence (AI) literacy is a rapidly growing research area and a critical addition to K-12 education. However, support for designing tools and curriculum to teach K-12 AI literacy is still limited. There is a need for additional interdisciplinary human-computer interaction and education research investigating (1) how general AI literacy is currently implemented in learning experiences and (2) what additional guidelines are required to teach AI literacy in specifically K-12 learning contexts. In this paper, we analyze a collection of K-12 AI and education literature to show how core competencies of AI literacy are applied successfully and organize them into an educator-friendly chart to enable educators to efficiently find appropriate resources for their classrooms. We also identify future opportunities and K-12 specific design guidelines, which we synthesized into a conceptual framework to support researchers, designers, and educators in creating K-12 AI learning experiences.

Qurate Business Intelligence has recently announced the incorporation of a new report entitled Artificial Intelligence Software Industry to its growing repository. They have used different techniques for selecting graphical presentation this report, such as infographics, graphics, images and flow charts that help you get a better perspective of the readers. Well it explained the SWOT analysis has been used to understand the strength, weaknesses, opportunities and threats facing the business. We analyzed the business profiles of the main stakeholders to understand the successful strategies adopted by them. Top Leading Players Studied in Artificial Intelligence Software Industry: Google, Baidu, Microsoft, SAP, Intel, Salesforce, Brighterion, KITT.AI, IFlyTek, IBM, Megvii Technology, Albert Technologies, H2O.ai, Brainasoft, Yseop, Ipsoft, NanoRep(LogMeIn), Ada Support, Astute Solutions, IDEAL.com,

"Global Artificial Intelligence (AI) Robots Market Analysis, Forecast & Outlook (2019-2024)" provides an extensive research and detailed analysis of the present …