Daily Archives: January 9, 2021

The Brexit Deal Is Done: What Does it Mean for Recognition in Cross-Border Restructurings and Insolvencies? – Sidley Austin LLP

Posted: January 9, 2021 at 2:50 pm

Recognition in the transition periodWhile the benefits of English R&I processes continue to exist post-Brexit, it is important to understand the complexities that arise from key pieces of EU legislation no longer being applicable to the UK. Until December 31, 2020, the commencement of insolvency proceedings1 in EU Member States (except Denmark), in sofar as it applies to the UK, was regulated primarily by Regulation No 2015-848 on Insolvency Proceedings (Recast Regulation). Pursuant to the Recast Regulation, where the centre of main interests (COMI) of a debtor is located in an EU Member State (except Denmark), insolvency proceedings opened in that Member State are automatically recognized throughout the EU as Main Proceedings. Further, if a debtors COMI is located in an EU Member State, it can open secondary insolvency proceedings in another EU Member State if the debtor has an establishment there, as Secondary Proceedings.

The UK ceased to be a member of the EU from January 31, 2020 (Exit Day), and entered into the EU-UK Withdrawal Agreement, which came into force on February 1, 2020, and was ratified and incorporated into UK domestic law via the European Union (Withdrawal Agreement) Act 2020 (EWA). The EWA provided for a period of time (the transition period) during which EU legislation continued to apply to the UK, which ended on December 31, 2020. During the transition period, the Recast Regulation continued to apply as between the EU and the UK in the same way as prior to Exit Day, resulting in the UK continuing to recognize insolvency proceedings commenced in other EU Member States while receiving reciprocal recognition of UK insolvency proceedings, in each case, where Main Proceedings were commenced on or before December 31, 2020 (Article 67(3), EWA).

Recognition after the end of the transition periodThe Insolvency (Amendment) (EU Exit) Regulations 2019 (as amended by the Insolvency (Amendment) (EU Exit) Regulations 2020) sought to provide a general alignment of treatment of EU Member States and the UK in a hard Brexit scenario. Effectively, UK courts are given jurisdiction to open insolvency proceedings after the end of the transition period where either the debtors (i) COMI is in the UK, or (ii) COMI is in another EU Member State and there is an establishment in the UK. Further, such jurisdiction tests sit alongside pre-existing UK jurisdiction tests (e.g. sufficient connection for the winding-up of foreign companies), which are no longer restricted by the Recast Regulation and could result in the UK courts taking a more expansive jurisdiction in insolvency proceedings.

Significantly, the TCA does not reference any transitional provisions that would afford automatic recognition of UK insolvency proceedings in EU Member States (and vice versa), nor does it extend the protections contained in the EWA. Therefore, while the TCA is considered a deal for certain industries and sectors, we are actually facing a no-deal Brexit scenario raising further complexities and recognition issues with respect to R&I processes of pan-European corporations that are led out of, respectively, the UK and an EU Member State.

Recognition by EU Member States of UK R&I proceedings post-Brexit (outward)

1.UNCITRAL Model Law on Cross-Border Insolvency has been implemented into UK domestic legislation through the Cross Border Insolvency Regulations 2006 and provides for recognition of insolvency proceedings and cooperation between participating jurisdictions. While the U.S. and Singapore are notable jurisdictions that have enacted the Model Law with success, its impact on implementing restructurings post-Brexit throughout Europe may be more limited given that the only EU Member States to implement the Model Law to-date are Poland, Slovenia, Greece, and Romania.

2.Local laws: For EU Member States that have not enacted the Model Law, UK officeholders will need to rely on relevant local laws to gain recognition and enforcement of UK orders in the relevant Member State. For example, Germany has provisions allowing for recognition of foreign insolvency proceedings. Further, recognition in EU Member States may be bolstered by considerations of mutual recognition with the UK.

Recognition by EU Member States of UK judgments and orders post-Brexit (including those handed down in relation to English schemes of arrangement and the new restructuring plans) (outward)

3.Lugano Convention 2007 (Lugano): provides a framework for the recognition and enforcement of judgments (including those handed down in relation to English schemes of arrangement and the new restructuring plan). The general position is that a defendant domiciled in an EU Member State shall be sued in the courts of that EU Member State, and judgments given in one Member State shall be registered and enforced in another Member State. However, this regime has been revoked following the end of the transition period and jurisdiction will be determined, at least for the time being, by applying existing local law of the relevant EU Member State, save where a relevant international convention or agreement applies. On April 8, 2020, the UK deposited a request for re-accession to Lugano as an individual member, which requires the unanimous consent of all contracting parties. However, the EU and Denmark have yet to give their explicit consent. Further, the UK government would prefer to build on the principles of Lugano via a new bilateral agreement with the EU. Whether such a new bilateral agreement can be agreed and, if so, what it will contain remains to be seen.

4.Hague Convention on Choice of Court Agreements: where parties have stipulated an exclusive choice of court agreement in their contracts, the Hague Convention requires that the parties decision and choice of court is respected. It further provides that any judgment rendered by the chosen court will be recognized and enforced in other signatory states. On September 28, 2020, the UK deposited a new request for re-accession to the Hague Convention to ensure the continuity of the Hague Convention following the end of the transition period.

5.Rome I Regulation (Rome I): determines the law governing contractual and non-contractual obligations and applies in all EU member states (except Denmark). Following the end of the transition period, Rome I will be converted into UK domestic law allowing parties to choose the law applicable to the relevant agreements and providing for recognition across EU Member States (except Denmark). In the context of English law governed debt that is compromised under an English scheme of arrangement, Rome I will allow the relevant scheme order to be recognised throughout the EU.

Recognition by the UK of EU R&I proceedings post-Brexit (inward)

6.Section 426 of the Insolvency Act 1986 provides a unique tool whereby recognition is sought in the UK of an insolvency proceeding taking place in a designated jurisdiction, thereby giving English courts permission to assist foreign courts. However, this statutory power is available only upon request by a foreign court and is limited to certain designated jurisdictions (e.g., Commonwealth countries and Ireland).

7.Cross Border Insolvency Regulations 2006 (CBIR): In order for foreign proceedings to be recognised in the UK under the CBIR, the debtor must have a place of business or residence or assets situated in the UK, or the Court must otherwise consider recognition appropriate. The CBIR apply without the need for reciprocity (which means, for example, that the UK will recognise eligible foreign insolvency proceedings even if the relevant foreign country has not itself enacted the Model Law).

8.Common law principles of insolvency assistance: Key principles derived from case law2, provide that, ideally, there should be a unitary insolvency proceeding in the court of the debtors domicile that receives worldwide recognition and that should apply universally to the debtors assets (known as universalism).

Although there are alternative legal mechanisms available to pursue recognition of R&I proceedings, these need to be considered on a case by case basis for restructurings post-Brexit. Eventually, precedent will establish a clearer route, and there is always the possibility of a subsequent deal between the EU and the UK.

Practical consequences

The Brexit deal is likely to have a greater impact on European R&I proceedings involving the UK, particularly given that reciprocal automatic recognition will no longer apply with the loss of access to key EU legislation. Consequently, although the practical consequences will vary depending on the specific circumstances and jurisdictions involved, there may be a possible need for parallel proceedings (particularly for recognition) that will entail additional time and costs. Other possible practical consequences worth noting include these:

1.English-headquartered groups may decide to shift the COMI of European guarantors to the UK ahead of implementing an English R&I process, to ensure they take advantage of the comprehensive toolkit made available under English procedures that are well regarded on the global stage.

2.EU-headquartered groups may similarly decide to shift the COMI of UK guarantors to their home EU Member State and continue to rely on EU legislation and local R&I regimes and tools. However, note that a number of EU Member States have failed to provide the same level of certainty that English courts can offer. For example, European R&I processes have not been as successful as the UK in compromising guarantees that have been granted by group entities that are domiciled in other EU Member States.

3.The rule in Gibbs3 is likely to take on more significance following a hard Brexit. Gibbs provides that only an English R&I process would be effective to compromise or discharge English law governed debt. Under the Recast Regulation, the rule in Gibbs is effectively overriden in respect of European compromise proceedings, but that will no longer be the case, and we may start to see the English courts revert to the rule in Gibbs. Consequently, companies may be required to weigh the benefits of using the English judicial system against the greater harmonisation amongst the remaining EU Member States.

4.A number of European jurisdictions are introducing new restructuring tools akin to the English scheme of arrangement or new restructuring plan. For example, the Netherlands have already introduced their own scheme, and Germany is in the process of introducing its own scheme as an alternative to the English scheme of arrangement.

Despite the loss of access to key EU legislation (predominantly, the Recast Regulation and the Judgments Regulation) that have provided a uniform legal regime for European R&I processes, the popularity of English R&I processes is unlikely to be severely affected post-Brexit. We expect that the English courts will continue to be favored as the choice of forum for many R&I proceedings. UK restructuring tools provide great flexibility to implement a wide range of restructuring options (from short standstills to full holistic restructurings), quick access to the English courts (particularly in the context of implementing an English scheme of arrangement or the new restructuring plan, each of which can be completed in a matter of weeks), and tried and tested procedures providing confidence and certainty for all stakeholders.

1 -Note that judgments (and, as such, UK schemes of arrangement) were generally considered to be dealt with by the Recast Regulation.2 -Rubin v Eurofinance [2012] UKSC 463 -Antony Gibbs & Sons v La Socit Industrielle et Commerciale des Mtaux (1890) LR 25 QBD 399

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The Brexit Deal Is Done: What Does it Mean for Recognition in Cross-Border Restructurings and Insolvencies? - Sidley Austin LLP

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John Bercow anti-Brexit rant BACKFIRES as he faces backlash ‘Who cares what he thinks!’ – Daily Express

Posted: at 2:50 pm

Brexit colossal foreign policy blunder says Bercow

Former House of Commons Speaker John Bercow has been attacked following his comments on Brexit. While speaking to LBC's Nick Ferrari, Mr Bercow argued that Brexit was the UK's biggest mistake post-war. He also argued that while Boris Johnson had secured a Brexit deal, it was bare bones.

Mr Ferrari said: "It has been only days since Britain fully Brexited and has a trade deal.

"Fortunately, goods are still flowing both ways at Dover. We are going to be okay aren't we?"

Mr Bercow admitted he was hopeful but went into detail of his scepticisms.

He said: "I hope so but I do think it is very early days and I am not yet convinced.

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"You would think me a very strange character indeed if I suddenly turned tail and said I think Brexit is a marvellous idea.

"My own personal view is that Brexit is the most colossal foreign policy blunder of the post-war period."

Mr Ferrari interrupted to say that Boris Johnson has pulled Brexit off and managed to get a deal.

Mr Bercow replied: "Yes, Boris Johnson did get a deal and getting a deal is much better than having an ejection from the Union.

"I readily acknowledge that but it is a pretty thin skeletal deal."

Many frustrated viewers and listeners took to Twitter to voice their frustration at the former Speaker's comments.

One user wrote: "Who cares what this nobody thinks."

Another added: "Have you heard this horrible man Bercow on LBC today?

"What a pompous person he is. Our great Parliament is well rid of him."

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While another wrote: "Not as colossal as putting you as Speaker of the Commons.

"That was a monumental crackpot decision."

Another social media user added: "What is this ridiculous man doing back giving his opinion on things that don't concern him.

"I thought we got rid of him."

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John Bercow anti-Brexit rant BACKFIRES as he faces backlash 'Who cares what he thinks!' - Daily Express

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Penzance mayor tells of racist abuse over removal of Brexit flags – The Guardian

Posted: at 2:50 pm

The mayor of a Cornish town who was targeted with racist abuse after a dispute over the flying of union flags to celebrate Brexit has spoken of her fear and anger.

Police have installed a panic alarm at the home of Nicole Broadhurst after she received thousands of messages, many abusive or racist, criticising her for saying the flags had to be taken down from Penzance seafront.

Broadhurst, who is black, also revealed that she and her husband were badly shaken after a group arrived at her remote home in a van at night, shone headlights into their sitting room and shouted abuse.

She has been advised by police to suspend her mayoral Facebook page and staff at Penzance town council are passing offensive messages straight to the police rather than showing them to her.

Broadhurst, 50, said: Its quite frightening to think they can do this to me and make me feel scared. Its a vocal minority who want people to change how they run their homes and towns.

When they came up to the house shouting and flashing their lights that was a bit of a shake-up for me and my husband. Now when a car pulls up, were both like, whats that? Were living differently to the day before it happened.

Flags are not normally flown on Penzance promenade in the winter because they can be damaged by high winds. But over new year 18 union flags appeared on poles owned by Cornwall council to mark the end of the Brexit transition.

Broadhurst made it clear they had not been authorised and they were taken down. A petition was launched against the move and the issue was picked up by local and national media, leading to thousands of abusive messages.

The mayor said: The messages were unpleasant, personal, misogynistic and horrible. They included things like: Dont you dare show your face in this town, youre an incomer, go back to where you came from.

There were lots of comments such as: Look at her, she should go back to where she came from. Broadhurst said she was born in south London, moved to Cornwall in 2005 and had been a town councillor for almost four years.

She said: I think I became a conduit for the anger that is out there, which is puzzling. The people who are angry won, they won Brexit. They got what they wanted, why are they still cross?

Ive been told by a few supporters keep your head down but I wont. People keep their head down because they have been slapped down by a vocal minority. I will be asking the council not to appease these people. If we have a policy there is no way we should change it just because a small vocal minority wants to change something.

She added: Its surreal that I have a personal panic alarm installed. The police are phoning to make sure Im OK. She said she had been told the police were investigating possible crimes under the Malicious Communications Act 1988.

It is not known who put the flags up. One man, speaking on condition of anonymity, told the Cornwall Live website he had done it to help the UK celebrate independence from the EU.

He said: They were not put up to create further divide in our community, they were put up to try to unite us as a nation. I am a proud Cornishman and I am proud to be British.

A spokesperson for Devon and Cornwall police said: Police in Penzance are investigating a report that racist comments had been posted online about a member of the community.

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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 -…

Posted: at 2:50 pm

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.

For more information about this report visit https://www.researchandmarkets.com/r/dxs2ch

Research and Markets also offers Custom Research services providing focused, comprehensive and tailored research.

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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 -...

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Does Artificial Intelligence Have Psychedelic Dreams and Hallucinations? – Analytics Insight

Posted: at 2:50 pm

It is safe to say that the closest thing next to human intelligence and abilities is artificial intelligence. Powered by its tools in machine learning, deep learning and neural network, there are so many things that existing artificial intelligence models are capable of. However do they dream or have psychedelic hallucinations like humans? Can the generative feature of deep neural networks experience dream like surrealism?

Neural networks are type of machine learning, focused on building trainable systems for pattern recognition and predictive modeling. Here the network is made up of layersthe higher the layer, the more precise the interpretation. Input data feed goes through all the layers, as the output of one layer is fed into the next layer. Just like neuron is the basic unit of the human brain, in a neural network, it is perceptron which forms the essential building block. A perceptron in a neural network accomplishes simple signal processing, and these are then connected into a large mesh network.

Generative Adversarial Network (GAN) is a type of neural network that was first introduced in 2014 by Ian Goodfellow. Its objective is to produce fake images that are as realistic as possible. GANs havedisrupted the development of fake images: deepfakes. The deep in deepfake is drawn from deep learning. To create deepfakes, neural networks are trained on multiple datasets. These dataset can be textual, audio-visual depending on the type of content we want to generate. With enough training, the neural networks will be able to create numerical representations the new content like a deepfake image. Next all we have to do is rewire the neural networks to map the image on to the target. Deepfake can also be created using autoencoders, which is a type of unsupervised neural network. In fact, in most of the deepfakes, autoencoders is the primary type of neural network used in their creation.

In 2015, a mysterious photo appeared onRedditshowing a monstrous mutant. This photo was later revealed to be a result of Google artificial neural network. Many pointed out that this inhumanly and scary appearing photo had striking resemblance to what one sees on psychedelic substances such as mushrooms or LSD.Basically, Google engineers decided that instead of asking the software to generate a specific image, they would simply feed it an arbitrary image and then ask it what it saw.

As per an abstract on Popular Science, Google used the artificial neural netowrk to amplify patterns it saw in pictures. Each artificial neural layer works on a different level of abstraction, meaning some picked up edges based on tiny levels of contrast, while others found shapes and colors. They ran this process to accentuate color and form, and then told the network to go buck wild, and keep accentuating anything it recognizes. In the lower levels of network, the results were similar to Van Gogh paintings: images with curving brush strokes, or images with Photoshop filters. After running these images through the higher levels, which recognize full images, like dogs, over and over, leaves transformed into birds and insects and mountain ranges transformed into pagodas and other disturbing hallucinating images.

Few years ago, Googles AI company DeepMindwas working on a new technology, which allows robots to dream in order to improve their rate of learning.

In a new article published in the scientific journalNeuroscience of Consciousness, researchers demonstrate how classic psychedelic drugs such as DMT, LSD, and psilocybin selectively change the function of serotonin receptors in the nervous system. And for this they gave virtual versions of the substances to neural network algorithms to see what happens.

Scientists from Imperial College London and the University of Geneva managed to recreate DMT hallucinations by tinkering around with powerful image-generating neural nets so that their usually-photorealistic outputs became distorted blurs. Surprisingly, the results were a close match to how people have described their DMT trips. As per Michael Schartner, a member of the International Brain Laboratory at Champalimaud Centre for the Unknown in Lisbon, The process of generating natural images with deep neural networks can be perturbed in visually similar ways and may offer mechanistic insights into its biological counterpart in addition to offering a tool to illustrate verbal reports of psychedelic experiences.

The objective behind this was to betteruncover the mechanismsbehind the trippy visions.

One basic difference between human brain and neural network is that our neurons communicate in multi-directional manner unlike feed forward mechanism of Googles neural network. Hence, what we see is a combination of visual data and our brains best interpretation of that data. This is also why our brain tends to fail in case of optical illusion. Further under the influence of drugs, our ability to perceive visual data is impaired, hence we tend to see psychedelic and morphed images.

While we have found answer to Do Androids Dream of Electric Sheep? by Philip K. Dick, an American sci-fi novelist; which is NO!, as artificial intelligence have bizarre dreams, we are yet to uncover answers about our dreams. Once we achieve that, we can program neural models to produce visual output or deepfakes as we expect. Besides, we may also apparently solve the mystery behind black box decisions.

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How artificial intelligence and augmented reality are changing medical proctoring during COVID-19 – FierceHealthcare

Posted: at 2:50 pm

Medical device specialists often monitor the work of surgeons to provide case support during procedures in the operating room. However, during the COVID-19 pandemic, medical proctoring has gone remote.

COVID has dramatically accelerated the need to have a remote tool, when you either don't have access to the hospital, or when there are travel restrictions that make it really difficult, said Jennifer Fried, CEO and co-founder of ExplORer Surgical, a company that provides a software platform for case support during surgery. ExplORer offers the training platform in three formats: in-person, remote or hybrid.

Launched out of the University of Chicago Department of Surgery in 2013, ExplORer recently added augmented reality technology that acts as a virtual laser pointer for medical device specialists to provide on-screen guidance for surgeons similarly to how sports announcers use a Telestrator to mark up plays.

Introduced in November, ExplORers two-way audio/video system is compliant with the Health Insurance Portability and Accountability Act. The AR technology lets medical device reps zoom in or out and use a laser pointer to highlight items on the screen for the doctor. Scrub nurses that prepare the surgical instruments use the customized content in the platform to guide them through the workflow.

RELATED:COVID-19 has caused a surge in telemedicine. Is telesurgery next?

Before COVID-19, physicians would fly around the world to proctor cases. In fact, six to eight surgeons would be in the room to do a procedure. With the pandemic, proctors are logging into the OR remotely, according to Fried.

Medical proctors can draw on a fluoroscopy image, share their screen and warn doctors what to look for during a procedure such as implanting a device in the body.

The ability to do that in a live, real-time way that's also compliant is really a game changer in this environment, Fried told Fierce Healthcare.

Fried noted that an ExplORer customer recently began a large U.S. clinical trial with surgeons in Europe while their clinical specialists were in Australia. The remote proctoring software has helped facilitate this collaboration.

Remote proctoring will likely continue after the pandemic because of its cost savings and efficiency for medical professionals, according to Fried.

What we're seeing with COVID is, I think, people are getting more and more comfortable with remote support, as it has become a necessity, Fried said. I think that trend is going to be here to stay.

RELATED:Healthcare is ramping up AI investments during COVID. But the industry is still on the fence about Google, Amazon. Here's why

The AR features will be particularly valuable for medical proctors to help physicians through complex invasive procedures, noted Art Collins, former chairman and CEO of Medtronic and a senior medical adviser to ExplORer Surgical.

This expert training is critical when surgeons need to switch an implant operation from a large incision and open approach to minimally invasive or a catheter, Fried noted.

Remote proctoring solutions like those that ExplORer offers will incorporate artificial intelligence features such as computer vision to collect data on procedures and feed it automatically into a surgeons workflow.

AI and machine learning will become more impactful as the data set of video feeds increase, as a larger data set empowers better insights, Collins told Fierce Healthcare. As the use of video becomes standard practice in the OR and procedure suites, AI technologies will increasingly be able to interpret video feeds.

In addition, medical proctors will use image-object recognition and machine learning to analyze what is happening in the OR, Fried said. The AI technology could suggest best practices based on knowing that a surgeon took an instrument off the table, as one example.

You could say this step is taking a lot longer and know that because this instrument hasn't been put back onto the table, she explained.

ExplORer has built early iterations of this machine learning technology and plans to launch a commercial case in 2021, Fried said.

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How artificial intelligence and augmented reality are changing medical proctoring during COVID-19 - FierceHealthcare

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Artificial Intelligence: The Winner of the RSNA 2020 – Diagnostic Imaging

Posted: at 2:50 pm

Like the other major events of healthcare in 2020, the Annual Meeting of Radiological Society of North America (RSNA) also had to be held as an online-only event forced by the COVID-19 pandemic. The virtual platform of RSNA was very well designed, and it provided ample opportunities for customers to connect to the right representatives from each company.

The virtual meeting room was a unique tool on the RSNA platform where customers can visit the room just like they would walk into a physical booth without any prior appointments. The company representatives engage with the visitors and transfer the meeting to a private room seamlessly. This feature was quite helpful for those visitors who had not secured prior appointments with the vendors to have discussions and product demonstrations.

The vendors, though, had mixed responses when asked if the virtual meeting were as effective as in-person meetings. While most agreed that there cannot be a substitute for physical meetings, some exhibitors claimed that their live virtual programs had good traffic from the customers and partners. COVID-19 has disrupted the traditional way of conducting trade shows, and it might be precipitating a larger digital transformation of the marketing departments within the vendor companies.

Five large themes can be defined from the RSNA virtual floor this year. Artificial intelligence and enterprise imaging were the predominant themes and formed the framework for the other two themes of workflow efficiency and precision medicine. Moreover, cybersecurity was another emerging theme and it has a potential for huge growth.

Artificial Intelligence in the Post-Pandemic Era

It is quite clear that artificial intelligence (AI) was the No. 1 theme for RSNA this year. Even with just one-third the usual number of exhibitors in the virtual event, almost all players seemed to be pointing out their artificial intelligence capabilities.

Quite understandably the event was dominated by the larger players, and the usual AI showcase had a far fewer number of companies and startups. The key AI theme this year, then, as Platform plays for AI could be biased from that perspective. Platform-based approaches are not really new to radiology AI. Hints were being dropped at RSNA 2019, but this year the AI platform initiatives were the heart of the AI messaging.

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But, what was also curious was the diverse set of strategies evolving, even within the platform play approaches. For example, GE Healthcare had a direct focus on imaging AI with its Edison platform (with Open AI Orchestrator becoming part of the broader offering of health services), whereas Philips Healthcare focused more on precision diagnosis with AI solutions forming a part of that broader strategy (IntelliSpace AI Workflow Suite, IntelliSpace Precision Medicine). Siemens Healthineers has an even broader approach, where their Digital Marketplace goes beyond imaging to support the broader healthcare community (and a somewhat diluted focus on the radiology AI marketplace type approach). We also saw more players developing and/or refining their approach including Sectra [Amplifier], Fujifilm [REiLI], Canon, among others.

But, to not be biased with the virtual nature of this event, we also looked at what happened through 2020, for imaging AI. Several startups leveraged their capabilities to offer solutions for COVID-19 screening and diagnosis, some even securing emergency authorizations (e.g. CuraCloud). Most offered their solutions for free to hospitals, as well, doing their bit to support our COVID-19 warriors. Interesting enough, some of the mature, established start-ups did quite well during this phase of uncertainty Aidoc, for example, announced that it had tripled its revenues in the first three quarters of 2020, and private conversations with some other AI vendors gave us the same impression. Indeed, similar to broader digital health trends in healthcare, the adoption of AI in imaging has improved for several use cases in 2020, thanks to the negative effects of this pandemic.

An emerging trend was the interest in imaging AI companies from non-imaging vendors. Qure.ai announced a partnership with AstraZeneca (AZ) during RSNA week. AstraZeneca's interest lies in the early identification of lung cancer cases by undertaking lung imaging in emerging markets, such as Latin America, Africa, the Middle East, and Asia. In addition, Qure.ai's solutions are built to address the specific needs of the imaging departments in those regions. This is likely to improve the uptake of AI in emerging markets since players, such as AZ, are driving a use case that resonates well with the local market conditions. To really enable the uptake in emerging markets, Qure.AI has also addressed other concerns. Their qTrack smartphone app, allows film-based X-rays to be converted to digital images using only the users smartphone camera and allows for cloud-based algorithms to assess signs of tuberculosis, as well as serves to record patient data. Innovations such as these, and the qBox solution that we covered last year, are key to ensuring AI reaches the masses even in emerging markets.

Enterprise Imaging- Enabler of Efficiency and Productivity

Implementation of enterprise imaging solutions is a time-consuming process. With most hospitals working on skeletal staff due to COVID-19 and the subsequent financial distress that it brought upon them, implementing enterprise imaging during the pandemic was an impossible task. However, this presented an opportunity for both the vendors and the hospitals to consider innovative deployment models. Providers developed an affinity for cloud-based solutions as it does not require investing as much time and effort by the hospital staff as compared to a traditional on-premise implementation. This year witnessed a flurry of activities in cloud-based imaging with various vendors launching solutions that truly leverage the cloud-computing capabilities to realize tangible benefits in the clinical environment.

Earlier in the year, Change Healthcare launched its cloud-native Enterprise Imaging Network that enables aggregation and sharing of imaging data in a secure environment. Hyland launched its Software-as-a-Service (SaaS) solution for enterprise imaging during the event that is intended to relieve the hospitals of their responsibility for application and hardware maintenance. SaaS also enables hospitals to pay as per the usage with components being added only when the hospital demands. The trend towards SaaS in enterprise imaging augurs well for the hospitals who are currently reeling under severe financial stress, as it does not require huge upfront capital spending. Fujifilm highlighted its Synapse Cloud Services for hosting its Enterprise Imaging portfolio in a cost-effective and scalable environment targeting the teleradiology providers, critical access hospitals, and imaging centers.

For those concerned about the bandwidth and privacy issues that come with the cloud solution, GE Healthcares Edison HealthLink might be the right solution. Edison HealthLink is a new edge computing technology that permits clinicians to process the clinical data and act on it even before it reaches the cloud. TrueFidelity image reconstruction, CT Smart subscription, and eight other applications are already available on Edison HealthLink. Hyland introduced edge rendering of its zero footprint NilRead viewer that runs a local instance in low internet bandwidth conditions.

With enormous growth in the number of AI applications in imaging, the responsibility of integrating them into the workflow to ensure that they work seamlessly, has been taken up by the enterprise imaging vendors. The latest offering from Agfa Healthcare, RUBEE for AI is aimed at helping the hospitals in choosing the right AI solution for their needs. RUBEE aims to save the time for providers by offering them a curated set of intelligent applications that can be seamlessly integrated into their workflow in quick time.

Enterprise imaging involves networking multiple elements of imaging from different departments and centers and, as such, poses tremendous challenges in integrating the solutions from various vendors. The expertise of vendors like Altamont Software becomes extremely important with enterprise imaging strategy in perspective. Altamont Passport is a product that incorporates routing, pre-fetch, modality worklist, DICOM SR integration solutions to ensure a smooth workflow at an enterprise level. The Altamont Connectivity Platform provides the users with the necessary tools to integrate any image into their EMR or any enterprise system.

While challenges in enterprise imaging continue to emerge new solutions that address these are also being introduced, indicating the broad-based participation of the imaging industry.

COVID-19 has brought back the focus on efficiency and financial sustainability. With this crisis in the background, enterprise imaging will continue to evolve continuously over the next few years to develop into a unified medical record for all types of images in the enterprise. AI will be the toolkit for many efficiencies and productivity improvement initiatives. The developments in these two key domains will be a major driver for the growth of the imaging industry in the next decade.

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Artificial Intelligence: The Winner of the RSNA 2020 - Diagnostic Imaging

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What does Integration of Artificial Intelligence and Advanced Analytics mean in Business? – Analytics Insight

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What does Integration of Artificial Intelligence and Advanced Analytics mean in Business?

Disruptive technologies like artificial intelligence (AI) and advanced analytics have had a transformational impact on the finance industry. They are also changing the way enterprises interact with their clients and run their organizations. The emergence and rapid growth of these technologies helped companies enhance their processes and operations.

While data analytics refers to drawing insights from raw data, advanced analytics help collate previously untapped data sources, especially the unstructured data and data from the intelligent edge, to garner analytical insights. Meanwhile, artificial intelligence replicates behaviors that are generally associated with human intelligence. These include learning, reasoning, problem-solving, planning, perception, and manipulation. Some latest iterations of AI, like generative AI, can also create creative artwork, music, and more. Though these technologies sound diverse, their synergy would bring tremendous innovation across several industries. When powered by AI, advanced analytics algorithms can offer additional performance over other analytics techniques.

World Economic Forum states that the COVID-19 crisis provided a chance for advanced analytics and AI-based techniques to augment decision-making among business leaders too.

In a study conducted by Forrester Consulting on behalf of Intel, 98% of respondents believe that analytics is crucial to driving business priorities. Yet, fewer than 40% of workloads are leveraging advanced analytics or artificial intelligence. For instance, according to Deloitte Insights, only 70% of all financial services firms use machine learning to predict cash flow events, fine-tune credit scores, and detect fraud.

Advanced analytics and artificial intelligence are emerging favorites in the finance sector as they help firms authenticate customers, improve customer experience, and reduce the cost of maintaining acceptable levels of fraud risk, particularly in digital channels.As finance firms race inch to disruption, the velocity of fraud attacks and threats also increases. The amalgamation of these technologies helps mitigate such threats before there is any severe damage, thus increasing compliance. This is achieved by assessing risks, identifying potential suspicious activities, preventing fraudulent transactions, and more. Since AI powered analytical algorithms are adept at pattern recognition and processing large quantities of data, it is key to improving fraud detection rates. For customers, they can help authenticate any financial services they may be using and issue alert the customer if something is wrong.

This fraud detection capability is also helpful for brand marketers to distinguish successful campaigns and avoid wasteful spending. Boston Consulting Group has observed that consumer packaged goods (CPG) companies can boost more than 10% of their revenue growth through enhanced predictive demand forecasting, relevant local assortments, personalized consumer services, and experiences, optimized marketing and promotion ROI, and faster innovation cycles; all via the said technologies.

While factors like data silos, fear of missing out on the race to digital transformation and agility have influenced companies to rely on data-driven insights, they must leverage advanced analytics and artificial intelligence, to stay relevant in the market. In its September 2017 article, titledHow Big Consumer Companies Can Fight Back, Boston Consulting Group also mentions that these technologies top industry players can use them to transform their data into valuable insights. In other words, it can augment an enterprises ability to execute data-intensive workloads and, at the same time, keep the HPC environment adaptable, responsive, and cost-effective.

However, there are many difficulties faced by companies when adopting them too. As per a research survey by Ericsson IndustryLab, 91% of organizations surveyed reported facing problems in each of three categories of challenges studied, including technology, organizational, and company culture and people. It is true that artificial intelligence and advanced analytics tools allowed navigation and the re-imagining of all aspects of business operations, and the COVID-19 pandemic expedited their adoption. However, despite beingarguably the most powerful general-purpose technologies, companies must recognize potential, use cases, and strategize the right action plans to accelerate their artificial intelligence and advanced analytics undertakings.

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Artificial Intelligence ABCs: What Is It and What Does it Do? – JD Supra

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Artificial intelligence is one of the hottest buzzwords in legal technology today, but many people still dont fully understand what it is and how it can impact their day-to-day legal work.

According to Brookings Institution, artificial intelligence generally refers to machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment, and intention. In other words, artificial intelligence is technology capable of making decisions that generally require a human level of expertise. It helps people anticipate problems or deal with issues as they come up. (For example, heres how artificial intelligence greatly improves contract review.)

Recently, we sat down with Onits Vice President of Product Management, technology expert and patent holder Eric Robertson to cover the ins and outs of artificial intelligence in more detail. In this first installment of our new blog series, well discuss what it is and its three main hallmarks.

At the core of artificial intelligence and machine learning are algorithms, or sequences of instructions that solve specific problems. In machine learning, the learning algorithms create the rules for the software, instead of computer programmers inputting them, as is the case with more traditional forms of technology. Artificial intelligence can learn from new data without additional step-by-step instructions.

This independence is crucial to our ability to use computers for new, more complex tasks that exceed the manual programming limitations things like photo recognition apps for the visually impaired or translating pictures into speech. Even things we now take for granted, like Alexa and Siri, are prime examples of artificial intelligence technology that once seemed impossible. We already encounter in our day-to-day lives in numerous ways and that influence will continue to grow.

The excitement about this quickly evolving technology is understandable, mainly due to its impacts on data availability, computing power and innovation. The billions of devices connected to the internet generate large amounts of data and lower the cost of mass data storage. Machine learning can use all this data to train learning algorithms and accelerate the development of new rules for performing increasingly complex tasks. Furthermore, we can now process enormous amounts of data around machine learning. All of this is driving innovation, which has recently become a rallying cry among savvy legal departments worldwide.

Once you understand the basics of artificial intelligence, its also helpful to be familiar with the different types of learning that make it up.

The first is supervised learning, where a learning algorithm is given labeled data in order to generate a desired output. For example, if the software is given a picture of dogs labeled dogs, the algorithm will identify rules to classify pictures of dogs in the future.

The second is unsupervised learning, where the data input is unlabeled and the algorithm is asked to identify patterns on its own. A typical instance of unsupervised learning is when the algorithm behind an eCommerce site identifies similar items often bought by a consumer.

Finally, theres the scenario where the algorithm interacts with a dynamic environment that provides both positive feedback (rewards) and negative feedback. An example of this would be a self-driving car where, if the driver stays within the lane, the software will receive points in order to reinforce that learning and reminders to stay in that lane.

Even after understanding the basic elements and learning models of artificial intelligence, the question often arises as to what the real essence of artificial intelligence is. The Brookings Institution boils the answer down to three main qualities:

In the next installment of our blog series, well discuss the benefits AI is already bringing to legal departments. We hope youll join us.

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Artificial intelligence and transparency in the public sector – Lexology

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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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