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Dublin, Jan. 08, 2021 (GLOBE NEWSWIRE) -- The "Artificial intelligence in Healthcare Global Market - Forecast To 2027" report has been added to ResearchAndMarkets.com's offering.

Artificial intelligence in healthcare global market is expected to reach $35,323.5 million by 2027 growing at an exponential CAGR from 2020 to 2027 due to the gradual transition from volume to value-based healthcare

The surging need to accelerate and increase the efficiency of drug discovery and clinical trial processes, advancement of precision medicines, escalation of AI as a medical device, increasing prevalence of chronic, communicable diseases and escalating geriatric population and the increasing trend of acquisitions, collaborations, investments in the AI in healthcare market.

Artificial intelligence (AI) is the collection of computer programs or algorithms or software to make machines smarter and enable them to simulate human intelligence and perform various higher-order value-based tasks like visual perception, translation between languages, decision making and speech recognition.

The rapidly evolving vast and complex healthcare industry is slowly deploying AI solutions into its mainstream workflows to increase the productivity of various healthcare services efficiently without burdening the healthcare personnel, to streamline and optimize the various healthcare-associated administrative workflows, to mitigate the physician deficit and burnout issues effectively, to democratize the value-based healthcare services across the globe and to efficiently accelerate the drug discovery and development process.

Artificial intelligence in healthcare global market is classified based on the application, end-user and geography.

Based on the application, the market is segmented into Medical diagnosis, drug discovery, precision medicines, clinical trials, Healthcare Documentation management and others consisting of AI guided robotic surgical procedures and AI-enhanced medical device and pharmaceutical manufacturing processes.

The AI-powered Healthcare documentation management solutions segment accounted for the largest revenue in 2020 and is expected to grow at an exponential CAGR from 2020 to 2027. AI-enhanced Drug Discovery solutions segment is the fastest emerging segment, growing at an exponential CAGR from 2020 to 2027.

The artificial intelligence in healthcare global end-users market is grouped into Hospitals and Diagnostic Laboratories, Pharmaceutical companies, Research institutes and other end-users consisting of health insurance companies, medical device and pharmaceutical manufacturers and patients or individuals in the home-care settings.

Among these end users, Hospitals and Diagnostic Laboratories segment accounted for the largest revenue in 2020 and is expected to grow at an exponential CAGR during the forecasted period. Pharmaceutical companies segment is the fastest-growing segment, growing at an exponential CAGR from 2020 to 2027.

The artificial intelligence in healthcare global market by geography is segmented into North America, Europe, Asia-Pacific and the Rest of the world (RoW). North American region dominated the global artificial intelligence in healthcare market in 2020 and is expected to grow at an exponential CAGR from 2020 to 2027. The Asia-Pacific region is the fastest-growing region, growing at an exponential CAGR from 2020 to 2027.

The artificial intelligence in healthcare market is consolidated with the top five players occupying majority of the market share and the remaining minority share of the market being occupied by other players. Key Topics Covered:

1 Executive Summary

2 Introduction

3 Market Analysis3.1 Introduction3.2 Market Segmentation3.3 Factors Influencing Market3.3.1 Drivers and Opportunities3.3.1.1 Aiabetting the Transition from Volume Based to Value Based Healthcare3.3.1.2 Acceleration and Increasing Efficiency of Drug Discovery and Clinical Trials3.3.1.3 Escalation of Artificial Intelligence as a Medical Device3.3.1.4 Advancement of Precision Medicines3.3.1.5 Acquisitions, Investments and Collaborations to Open An Array of Opportunities for the Market to Flourish3.3.1.6 Increasing Prevalence of Chronic, Communicable Diseases and Escalating Geriatric Population3.3.2 Restraints and Threats3.3.2.1 Data Privacy Issues3.3.2.2 Reliability Issues and Black Box Reasoning Challenges3.3.2.3 Ethical Issues and Increasing Concerns Over Human Workforce Replacement3.3.2.4 Requirement of Huge Investment for the Deployment of AI Solutions3.3.2.5 Lack of Interoperability Between AI Vendors3.4 Regulatory Affairs3.4.1 International Organization for Standardization3.4.2 Astm International Standards3.4.3 U.S.3.4.4 Canada3.4.5 Europe3.4.6 Japan3.4.7 China3.4.8 India3.5 Porter's Five Force Analysis3.6 Clinical Trials3.7 Funding Scenario3.8 Regional Analysis of AI Start-Ups3.9 Artificial Intelligence in Healthcare FDA Approval Analysis3.10 AI Leveraging Key Deal Analysis3.11 AI Enhanced Healthcare Products Pipeline3.12 Patent Trends3.13 Market Share Analysis by Major Players3.13.1 Artificial Intelligence in Healthcare Global Market Share Analysis3.14 Artificial Intelligence in Healthcare Company Comparison Table by Application, Sub-Category, Product/Technology and End-User

4 Artificial Intelligence in Healthcare Global Market, by Application4.1 Introduction4.2 Medical Diagnosis4.3 Drug Discovery4.4 Clinical Trials4.5 Precision Medicine4.6 Healthcare Documentation Management4.7 Other Application

5 Artificial Intelligence in Healthcare Global Market, by End-User5.1 Introduction5.2 Hospitals and Diagnostic Laboratories5.3 Pharmaceutical Companies5.4 Research Institutes5.5 Other End-Users

6 Regional Analysis

7 Competitive Landscape7.1 Introduction7.2 Partnerships7.3 Product Launch7.4 Collaboration7.5 Up-Gradation7.6 Adoption7.7 Product Approval7.8 Acquisition7.9 Others

8 Major Companies8.1 Alphabet Inc. (Google Deepmind, Verily Lifesciences)8.2 General Electric Company8.3 Intel Corporation8.4 International Business Machines Corporation (IBM Watson)8.5 Koninklijke Philips N.V.8.6 Medtronic Public Limited Company8.7 Microsoft Corporation8.8 Nuance Communications Inc.8.9 Nvidia Corporation8.10 Welltok Inc.

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Global Healthcare Artificial Intelligence Report 2020-2027: Market is Expected to Reach $35,323.5 Million - Escalation of AI as a Medical Device -...

Yisong YueCredit: Caltech

On Wednesday, January 13, at 5 p.m. Pacific Time,Yisong Yue, professor of computing and mathematical sciences in the Division of Engineering and Applied Science at Caltech, continues the 20202021 Watson Lecture season by exploring Artificial Intelligence: How it Works and What it Means for the Future.

Over the past decade, artificial intelligence (AI) and the massive amounts of data powering such systems have dramatically changed our world. And as both the technology and the way in which scientists and engineers handle it becomes more refined, the impact of AI in society will become more profound. In this lecture, Yue will explore the key principles powering the current revolution in AI, consider how cutting-edge AI techniques are transforming how research is done across science and engineering at Caltech, and examine what all of this means for the future of material design, robotics, and big data seismology, among other areas of investigation.

Yue will show how, where human intuition breaks down, AI can guide scientists in finding data-driven solutions to complex problems.

Yue, who joined the Caltech faculty as an assistant professor in 2014 and became a full professor in 2020, was previously a research scientist at Disney Research. Before that, he was a postdoctoral researcher in the machine learning department and the iLab at Carnegie Mellon University. He received his PhD from Cornell University and his BS from the University of Illinois at Urbana-Champaign.

Yues research interests lie primarily in the theory and application of statistical machine learning. He is interested in developing novel methods for both interactive and structured machine learning. In the past, his research has been applied to information retrieval, analyzing implicit human feedback, clinical therapy, data-driven animation, behavior analysis, sports analytics, experiment design for science, and policy learning in robotics, among other areas of inquiry.

This event is free and open to the public.Advance registrationis required. The lecture will begin at 5 p.m. and run approximately 45 minutes, followed by a live audience Q&A session with Yue. After the live webinar, the lecture (without Q&A) will be available for on-demand viewing onCaltechs YouTube channel.

Since 1922, The Earnest C. Watson Lectures have brought Caltechs most innovative scientific research to the public. The series is named for Earnest C. Watson, a professor of physics at Caltech from 1919 until 1959. Spotlighting a small selection of the pioneering research Caltechs professors are currently conducting, the Watson Lectures are geared toward a general audience as part of the Institutes ongoing commitment to benefiting the local community through education and outreach. Through a gift from the estate of Richard C. Biedebach, the lecture series has expanded to also highlight one assistant professors research each season.

Watson Lecturesare part of the Caltech Signature Lecture Series, presented by Caltech Public Programming, which offers a deep dive into the groundbreaking research and scientific breakthroughs at Caltech and JPL.

?? Register for the Zoom webinar

For more information, visithttps://events.caltech.edu/calendar/watson-lecture-2021-01.

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Caltech Professor to Explore Artificial Intelligence: How it Works and What it Means for the Future in Upcoming Event - Pasadena Now

The Centre for Data Ethics and Innovation has published its review into bias in algorithmic decision-making; how to use algorithms to promote fairness, not undermine it. We wrote recently about the report's observations on good governance of AI. Here, we look at the report's recommendations around transparency of artificial intelligence and algorithmic decision-making used in the public sector (we use AI here as shorthand).

The need for transparency

The public sector makes decisions which can have significant impacts on private citizens, for example related to individual liberty or entitlement to essential public services. The report notes that there is increasing recognition of the opportunities offered through the use of data and AI in decision-making. Whether those decisions are made using AI or not, transparency continues to be important to ensure that:

However, the report identifies, in our view, three particular difficulties when trying to apply transparency to public sector use of AI.

First, the risks are different. As the report explains at length there is a risk of bias when using AI. For example, where groups of people within a subgroup is small, data used to make generalisations can result in disproportionately high error rates amongst minority groups. In many applications of predictive technologies, false positives may have limited impact on the individual. However in particularly sensitive areas, false negatives and positives both carry significant consequences, and biases may mean certain people are more likely to experience these negative effects. The risk of using AI can be particularly great for decisions made by public bodies given the significant impacts they can have on individuals and groups.

Second, the CDEI's interviews found that it is difficult to map how widespread algorithmic decision-making is in local government. Without transparency requirements it is more difficult to see when AI is used in the public sector which risks suggested intended opacity (see our previous article on widespread use by local councils of algorithmic decision-making here), how the risks are managed, or to understand how decisions are made.

Third, there are already several transparency requirements on the public sector (think publications of public sector internal decision-making guidance, or equality impact assessments) but public bodies may find it unclear how some of these should be applied in the context of AI (data protection is a notable exception given guidance by the Information Commissioner's Office).

What is transparency?

What transparency means depends on the context. Transparency doesnt necessarily mean publishing algorithms in their entirety. That is unlikely to improve understanding or trust in how they are used. And the report recognises that some citizens may make, rightly or wrongly, decisions based on what they believe the published algorithms means.

The report sets out useful requirements to bear in mind when considering what type of transparency is desirable:

Recommendation - transparency obligation

In order to give clarity to what is meant by transparency, and to improve it, the report recommends:

Government should place a mandatory transparency obligation on all public sector organisations using algorithms that have a significant influence [by affecting the outcome in a meaningful way] on significant decisions [i.e. that have a direct impact, most likely one that has an adverse legal impact or significantly affects] affecting individuals. Government should conduct a project to scope this obligation more precisely, and to pilot an approach to implement it, but it should require the proactive publication of information on how the decision to use an algorithm was made, the type of algorithm, how it is used in the overall decision-making process, and steps taken to ensure fair treatment of individuals.

Some exceptions will be required, such as where transparency risks compromising outcomes, intellectual property, or for security & defence.

Further clarifications to the obligation, such as the meaning of "significant decisions" will also be required. As a starting point, though, the report anticipates a mandatory transparency publication to include:

The report expects that identifying the right level of information on the AI is the most novel aspect. CDEI expect that other examples of transparency may be a useful reference, including the Government of Canadas Algorithmic Impact Assessment, a questionnaire designed to help organisations assess and mitigate the risks associated with deploying an automated decision system (and which we referred to in a recent post about global perspectives on regulating for algorithmic accountability).

A public register?

Falling short of an official recommendation, the CDEI also notes that the House of Lords Science and Technology Select Committee and the Law Society have both recently recommended that parts of the public sector should maintain a register of algorithms in development or use (these echo calls from others for such a register as part of a discussion on the UK's National Data Strategy). However, the report notes the complexity in achieving such a register and therefore concludes that "the starting point here is to set an overall transparency obligation, and for the government to decide on the best way to coordinate this as it considers implementation" with a potential register to be piloted in a specific part of the public sector.

Government is increasingly automating itself with the use of data and new technology tools, including AI. Evidence shows that the human rights of the poorest and most vulnerable are especially at risk in such contexts. A major issue with the development of new technologies by the UK government is a lack of transparency. The UN Special Rapporteur on Extreme Poverty and Human Rights, Philip Alston.

https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/939109/CDEI_review_into_bias_in_algorithmic_decision-making.pdf

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Artificial intelligence and transparency in the public sector - Lexology