Artificial Intelligence Survey for 2020 by ACR Data Science Institute

Prabhakar Vagvala — Mon, 10 Jul 2023 00:00:00 +0000

An Outside of the healthcare industry, commercially available artificial intelligence (AI) algorithms have shown signs of racial, gender, and societal prejudice. The development of AI algorithms in the fields of radiologic sciences and healthcare is significantly impacted by these biases. The physician community should work with developers and regulators to create paths that guarantee algorithms sold for widespread clinical practice are secure, efficient, and free of unintended bias in order to prevent the introduction of bias in healthcare AI. Structured AI use cases with data elements have been developed by the ACR Data Science Institute to make it easier to create standardized datasets for AI testing and training at various universities. This project seeks to encourage the accessibility of a variety of data for algorithm development. Additionally, ACR Certify-AI and ACR Assess-AI, validation and monitoring services offered by the ACR Data Science Institute, integrate guidelines for minimizing algorithm bias and advancing health fairness. The ACR should support pricing methods for AI that guarantee access to AI technologies for all patients, regardless of their socioeconomic level or the resources available within their healthcare systems, in addition to promoting diversity. The ACR Data Science Institute conducted its first annual survey of ACR members to better understand how artificial intelligence (AI) is used in clinical practice and to create a baseline for tracking trends. Participants were asked to answer questions about their practice's demographics and to say whether or not they presently use AI in their clinical work, as well as in what capacity. AI has started to be used in mammography screening procedures. According to the report, larger practices were more likely to use AI than smaller ones. The majority of individuals who used AI in clinical practice did so to improve interpretation; the most frequent uses were for the early detection of cerebral hemorrhage, pulmonary emboli, and abnormalities in mammograms. According to the survey results, clinical practice has just recently begun to incorporate AI at a moderate rate. The data acquired from the poll will help researchers and business experts create AI solutions that can improve radiological practice, resulting in enhancements to the effectiveness and quality of patient care.

How Secure Is Your Cloud? A Guide to Building Confidence in Cloud Services

Prabhakar Vagvala — Thu, 05 Jan 2023 00:00:00 +0000

As more people and companies rely on cloud services for data management, processing, and storage, trustworthiness of cloud computing has become a critical problem. To guarantee the security, dependability, and privacy of data kept in the cloud, it is crucial to assess a cloud provider's credibility. In this post, we'll examine the crucial elements that go into figuring out whether a cloud service is reliable and lay out a few critical points that consumers should keep in mind when making judgements. Understanding these elements will help people and businesses evaluate the reliability of cloud services and reduce any dangers that may come with implementing cloud computing solutions. Cloud computing has become a crucial aspect of both our personal and professional life in today's digital world. However, we must carefully assess the reliability of a cloud service provider before entrusting them with our sensitive data and apps. The dependability, security, privacy, and openness of a cloud provider are just a few examples of the many factors that make them trustworthy. Before selecting a cloud service, it is crucial to consider these aspects since they have an immediate influence on the reliability and accessibility of our data. This article will examine the crucial factors that establish a cloud provider's dependability, arming readers with the information they need to make wise choices and guarantee the security of their digital assets. for people, corporations, and society at large make research on evaluating the trustworthiness of cloud computing significant. Understanding the reliability of cloud providers is essential as more businesses move their operations to the cloud and more people keep their personal data in cloud-based services. First off, by evaluating the credibility of cloud providers, businesses may decide which provider to use, taking into account aspects like security, dependability, and compliance with data protection laws. This aids businesses in safeguarding sensitive information, ensuring business continuity, and preventing security breaches and data loss. Second, people may choose which cloud services to utilise for keeping their personal data with more knowledge. Individuals may assess the dangers involved and take the necessary precautions to safeguard their privacy and the security of their personal data by being aware of how trustworthy a cloud provider is. Additionally, study on the reliability of cloud computing may help with the creation of industry standards, best practises, and rules. This can build confidence between customers and cloud service providers and promote a more dependable and secure cloud computing environment. Overall, the importance of this research rests in its potential to increase cloud computing's security, privacy, and dependability, which will be advantageous to both individual users and businesses. This study can help increase the acceptance of cloud services and open up new opportunities by solving the trustworthiness issues related to the cloud. Define the goals of the research: Outline the study's aims and aims, with an emphasis on figuring out whether cloud providers are trustworthy. Review of the literature Conduct a thorough analysis of the current literature, academic papers, business standards, and industry best practises in the field of cloud trustworthiness evaluation. Identify and explain the important elements that go into making cloud providers trustworthy, including security, privacy, dependability, transparency, compliance, and data governance. Create assessment standards: To evaluate the performance of cloud providers, establish a set of assessment standards based on the variables for trustworthiness that have been established. data gathering Collect pertinent information from a variety of sources, such as the documentation provided by cloud providers, security reports, certifications, customer evaluations, and industry polls. Data analysis: Employ both qualitative and quantitative methods to examine the data acquired in order to evaluate the dependability of various cloud service providers. Create a weighted scoring system to determine the relative value of the various trustworthiness characteristics depending on their importance. Benchmarking: Evaluate and compare the reliability outcomes of various cloud providers in light of the predetermined assessment standards. Conduct case studies with consumers and representatives of cloud service providers to learn more about their opinions of reliability and past experiences. Feedback and approval: To evaluate the evaluation process and assure its dependability, seek the advice of specialists working in the cloud computing sector.Cloud Client,Cloud Service Provider, Cloud Broker, SLA Agent, Cloud Auditor, SUM

Journal of Artificial intelligence and Machine Learning

Artificial Intelligence Survey for 2020 by ACR Data Science Institute

How Secure Is Your Cloud? A Guide to Building Confidence in Cloud Services