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The Increasing Role of Artificial Intelligence in Health Care: Will Robots Replace Doctors in the Future?

The use of AI in health care is poised for many-fold growth.4 Current applications of ai include disease diagnostics, drug development, the personalization of treatment, supportive health services, and gene editing. The use of AI in medicine can be classified into visual and physical domains.

Driving an analogy from biological neural networks, ANN is a computer program that simulates human thinking, such as with learning and retrieving data from previous experiences. An ANN observes examples of solutions to previous problems, collects information from those examples, processes this information through learning algorithms, and develops a response. Unlike a pre-program, an ANN learns, reasons, and responds as humans do. The quality of results depends on the amount of data it processes.

Future of Artificial intelligence

FES is a rule-based AI that is programmed with expert knowledge in a specific field and that mimics the response of an expert. The advantage of an FES system is that the knowledge of experts will be perpetually more accessible. EC is a computer program that is inspired by biological evaluation to provide an optimal or near-optimal solution. Essentially, EC processes data and information, suggests suitable solutions, and evolves with bigger data while eliminating unsuitable solutions.

AI is finding applications in more and more clinical areas and there has been a significant amount of research on developing new ones. The authors of a recent review synthesized three previously reported systematic reviews on the use of AI to reduce lower back pain.

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Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review

Conclusions: The primary use of AI in medical education was for learning support mainly due to its ability to provide individualized feedback. Little emphasis was placed on curriculum review and assessment of students’ learning due to the lack of digitalization and sensitive nature of examinations, respectively. Big data manipulation also warrants the need to ensure data integrity. Methodological improvements are required to increase AI adoption by addressing the technical difficulties of creating an AI application and using novel methods to assess the effectiveness of AI.

The titles and abstracts of identified articles were screened for the previously identified search criteria, and exclusion criteria were applied. All articles screened to be relevant or inconclusive were assessed in full text.

Based on the findings from our review, we propose that future research should focus on assessing the effectiveness of AI in medical education. Only one study that reviewed expert-led training in rheumatology has thus far shown the benefit of the use of an AI-driven system as compared to traditional methods.

As technology continues to advance, the potential uses of AI will continue to increase in medical education. One such development will be the use of AI, combined with immersive technologies such as virtual reality and augmented reality. As presented in our results, such studies have already been reported.

The scope of this review covers a broad spectrum of the current applications of AI in medical education. In the field of medicine, where the practices of each subspecialty vary tremendously, the use of AI in education may also vary. It may therefore be too early to make an overarching statement about the benefits of AI in medical education.

This review identified the current uses of AI in medical education, which include curriculum assessment and improvement of students’ learning, with research mainly existing on the latter.

Machine intelligence today: applications, methodology, and technology

This article is structured as follows: In the next section we present selected AI applications in various domains, namely culture, education, and industrial manufacturing. The following section focuses on AI methodology, namely aspects of machine learning and knowledge representation.

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What are the AI technologies currently used to fight the coronavirus pandemic?

Access to health care and resources at a moment’s notice is vital for battling the spread of the coronavirus.

AI in Cardiac Imaging: A UK-Based Perspective on Addressing the Ethical, Social, and Political Challenges

Imaging and cardiology are the healthcare domains which have seen the greatest number of FDA approvals for novel data-driven technologies, such as artificial intelligence, in recent years. The increasing use of such data-driven technologies in healthcare is presenting a series of important challenges to healthcare practitioners, policymakers, and patients. In this paper, we review ten ethical, social, and political challenges raised by these technologies. We also consider the approaches being taken by healthcare organizations and regulators in the European Union, the United States, and other countries.

The question also remains as to whether the use of AI will create new health inequalities.

There is still some way to go in addressing these questions. Furthermore, given the complexity of these technologies, a truly multidisciplinary approach is required.

We are extremely grateful to our colleague Nika Strukelj for her contribution to the original research cited in this paper.

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Along with the insurers and AI a third factor in the diffusion of AI in the insurance sector is the consumer. Purchasing insurance and making claims are part of a relationship between the consumer and the insurer. Most current applications of AI are used to strengthen the capabilities and knowledge of the insurer and not the consumer. As the information asymmetry increases, the insurer can provide lower quality services at lower prices. The higher quality more expensive services may stop being offered as they can no longer compete on price.

This raises the question how the user will react to their weaker position and how they should be ‘compensated’. Other approaches are to offer lower prices or a better service.

The development and use of ML and AI technologies have quickly become topics of national interest and debate. Since 2015, the federal government alone has increased funding for unclassified AI research and development by more than 40%.

Also at this summit, a new Select Committee on Artificial Intelligence comprised of the government’s most senior AI experts was announced.

There is currently no universally agreed-upon definition of artificial intelligence. The term ”intelligence” is understood as a measure of a machine’s ability to successfully achieve an intended goal. Like humans, machines exhibit varying levels of intelligence subject to the machine’s design and training data.

Such autonomous agents could open new ethical and legal complications that will need to be adequately assessed and planned for. For instance, autonomous agents or programs may, as a product of their autonomy, operate outside the expectations of their creators. In the event that the agent or program’s creators have not implemented comprehensive stopgaps, the agent or program may inadvertently cause unintended harm to allies or adversaries.

This is most certainly the general public’s understanding of AI.

Another scientific controversy surrounding machine learning and AI is the extent to which these technologies may mimic, or even exacerbate, human bias in judgment and decision making. One of the main sources of this bias is when developers use insufficiently diverse or representative datasets to initially train the AI. Media Lab showed that the accuracy of popular facial recognition programs varied by the gender and race of the individual in the photo.

In particular, the programs more accurately identified the gender and race of white men than black women. And ProPublica reported that a computer program used to rate the likelihood that a criminal defendant will commit a future crime was biased against black defendants.

Topics: artificial intelligence

In the public sector, AI-enabled governance may afford new efficiencies that have the potential to transform a wide array of public service tasks.

Barriers and Facilitators to Genetic Service Delivery Models: Scoping Review

Background: Advances in diagnostics testing and treatment of genetic conditions have led to increased demand for genetic services in the United States. At the same time, there is a shortage of genetic services professionals.

Results: There were a number of challenges identified, including the limited number of genetics specialists, wait time for appointments, delivery of services by nongenetics providers, reimbursement, and licensure.

Advances in diagnostic testing and treatment options for genetic conditions have led to increased demand for genetic services in the United States. For example, newborn screening programs test all infants shortly after birth for a variety of genetic conditions. Other avenues include clinical diagnosis from a broad array of specialists, such as neurologists, oncologists, and geneticists. We sought to understand how genetic services are provided and identify the most cost-effective methods of meeting growing needs for services.

Researchers independently reviewed each abstract and made notations related to reported challenges and potential solutions regarding the delivery of genetic services. Researchers reviewed each other’s notations, discussed any areas of disagreement, and ultimately came to a consensus on whether the article should be obtained and included in the review.

After an initial review, 93 articles related to genetic service models from across the 3 searches were selected for a full-text review. Three researchers categorized the articles by theme together and carried out full-text reviews.

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The literature review pointed out several challenges that are currently facing the field of genetics.

The use of telehealth is one of the main ways to increase access to genetic services. On the basis of the literature review, there is ample evidence to support the use of telehealth. First, because of the vastness of the United States and the dearth of genetic service providers, each of the genetic services centers serves a large catchment area. In addition, telehealth can facilitate the use of remote translation services. Finally, there is a nationwide shortage of genetic service providers across the continuum. Telehealth expansion can help with workforce issues by obviating the need for staff to travel to multiple locations.

In addition to telehealth, there are a few possible solutions related to clinical workflow that may improve access to genetic services. The use of GCAs has been rated favorably in many of the studies included in the literature review.

Another possible solution to improve the knowledge and expertise of nongenetics professionals is to embed genetics providers within primary care settings. Typically, genetic counselors are hired to be part of primary care settings to assist with referrals and provide long-term support to patients. This approach, though, may be more challenging to implement, given there is still an insufficient number of genetic counselors, although the field is growing.

The literature review illuminated the challenges and identified possible solutions that could be implemented to improve the delivery of genetic services. Options that include telehealth applications may be the most straightforward and immediate option for genetic centers to pilot. However, a more long-term investment will be to complement telehealth models with the education of nongenetics professionals.

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