Category Archives: Ai

Disinformation has been used in warfare and military strategy over time. But it is undeniably being intensified by the use of smart technologies and social media. This is because these communication technologies provide a relatively low-cost, low-barrier way to disseminate information basically anywhere.

The million-dollar question then is: Can this technologically produced problem of scale and reach also be solved using technology?

Indeed, the continuous development of new technological solutions, such as artificial intelligence (AI), may provide part of the solution.

Technology companies and social media enterprises are working on the automatic detection of fake news through natural language processing, machine learning and network analysis. The idea is that an algorithm will identify information as fake news, and rank it lower to decrease the probability of users encountering it.

From a psychological perspective, repeated exposure to the same piece of information makes it likelier for someone to believe it. When AI detects disinformation and reduces the frequency of its circulation, this can break the cycle of reinforced information consumption patterns.

However, AI detection still remains unreliable. First, current detection is based on the assessment of text (content) and its social network to determine its credibility. Despite determining the origin of the sources and the dissemination pattern of fake news, the fundamental problem lies within how AI verifies the actual nature of the content.

Theoretically speaking, if the amount of training data is sufficient, the AI-backed classification model would be able to interpret whether an article contains fake news or not. Yet the reality is that making such distinctions requires prior political, cultural and social knowledge, or common sense, which natural language processing algorithms still lack.

Read more: An AI expert explains why it's hard to give computers something you take for granted: Common sense

In addition, fake news can be highly nuanced when it is deliberately altered to appear as real news but containing false or manipulative information, as a pre-print study shows.

Classification analysis is also heavily influenced by the theme AI often differentiates topics, rather than genuinely the content of the issue to determine its authenticity. For example, articles related to COVID-19 are more likely to be labelled as fake news than other topics.

One solution would be to employ people to work alongside AI to verify the authenticity of information. For instance, in 2018, the Lithuanian defence ministry developed an AI program that flags disinformation within two minutes of its publication and sends those reports to human specialists for further analysis.

A similar approach could be taken in Canada by establishing a national special unit or department to combat disinformation, or supporting think tanks, universities and other third parties to research AI solutions for fake news.

Controlling the spread of fake news may, in some instances, be considered censorship and a threat to freedom of speech and expression. Even a human may have a hard time judging whether information is fake or not. And so perhaps the bigger question is: Who and what determine the definition of fake news? How do we ensure that AI filters will not drag us into the false positive trap, and incorrectly label information as fake because of its associated data?

An AI system for identifying fake news may have sinister applications. Authoritarian governments, for example, may use AI as an excuse to justify the removal of any articles or to prosecute individuals not in favour of the authorities. And so, any deployment of AI and any relevant laws or measurements that emerge from its application will require a transparent system with a third party to monitor it.

Future challenges remain as disinformation especially when associated with foreign intervention is an ongoing issue. An algorithm invented today may not be able to detect future fake news.

For example, deep fakes which are highly realistic and difficult-to-detect digital manipulation of audio or video are likely to play a bigger role in future information warfare. And disinformation spread via messaging apps such as WhatsApp and Signal are becoming more difficult to track and intercept because of end-to-end encryption.

A recent study showed that 50 per cent of the Canadian respondents received fake news through private messaging apps regularly. Regulating this would require striking a balance between privacy, individual security and the clampdown of disinformation.

While it is definitely worth allocating resources to combating disinformation using AI, caution and transparency are necessary given the potential ramifications. New technological solutions, unfortunately, may not be a silver bullet.

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Artificial intelligence may not actually be the solution for stopping the spread of fake news - The Conversation CA

From 2018 a new and unexplored market experienced impressive growth in AI Influencers. By now, you might have already heard about AI influencers but if you have the interest to learn more is in human nature. Or this is the first time you are coming across this peculiarity, and you are anxious to become familiar with AI influencers and digital models. Notwithstanding, you have come to the right place, where you learn everything you want to know about AI influencers and the eventual fate of influencer marketing.

The idea of AI influencers has been around starting around 2016 and sometimes much sooner than that. Nonetheless, it wasnt until 2018 that AI influencers began acquiring footing, and according to most predictions, 2021 will be a significantly greater year for digital influencers. As you can tell from the name, an AI influencer is an influencer based on Artificial Intelligence (AI).

Note that there are also genuine individuals alluded to as AI influencers, and they are generally prominent developers and specialists in the field of artificial intelligence. But this article talks about AI influencers that are not humans.

From the start, it may appear to be abnormal to utilize a robot for influencer marketing but the matter of the fact is that AI influencers will have effectively turned into an intriguing issue, and a few of the worlds greatest brands have utilized them for exceptionally fruitful missions. The interest is mostly driven by the fashion and lifestyle industries. For instance, an AI influencer on Twitter or Facebook isnt as famous or successful yet since it requires a real human association. On Instagram, in any case, one can without much of a stretch distribute an energized picture of an influencer wearing brand clothing with an extraordinary caption.

That being said, over the most recent months, a few of the most noticeable digital models have begun overwhelming YouTube demonstrating that AI influencers can deal with something beyond Instagram promotions. One of the additional intriguing parts of AI influencers is that they get a lot of supporters. There are AI influencers with a large number of adherents, and apparently, these great many devotees know that AI influencers are just utilized for marketing. All in all, there has been a shift from where individuals by and large attempt to keep away from promotions on TV and around the net by utilizing advertisement blockers and such to a period where individuals are effectively searching out showcasing channels via online media. Thus, this shift is assisting brands with arriving at a more extensive client bunch while additionally expanding the chances within influencer marketing.

All in all, are AI influencers are actually the future, or is it a prevailing fashion that will pass? Taking into account that the market for AI influencers is only developing and that their effect on Instagram and YouTube is expanding constantly, one can be are sure that they will be around for a long time. There are also a few significant brands that are intending to work with these influencers in the following year so one can anticipate a great deal of action.

Nonetheless, one cannot forget that these are robots and advanced manifestations and that somehow or another that conflict with the establishment of influencer marketing. The reason influencer marketing turned out to be a particularly colossal industry was that it considered ordinary people to elevate items to other typical people. The relationship was and still is human-like, based on trust and regard.

Up until now, this is the sort of thing that AIs cannot accomplish, and their advertisements will stay impersonal. Furthermore, in light of this, it seems like, even though AI influencers could keep on growing, a total takeover of the business is extremely unlikely. Additionally, AI influencers are more subject to a bigger after than the remainder of the business, and the idea of miniature and nano influencers is not appropriate for an AI-driven market.

In conclusion, another intriguing potential improvement that this could result in has to do with individuals behind the influencers. Right now, all of the large AI influencers are made and constrained by advanced brands working in AI and CGI. Nonetheless, what is preventing brands from making their own advanced models? Could Gucci make their own AI influencer that only addresses them? Furthermore, should not something be said about if Gucci has its own influencers and Prada has another, what might prevent the two brands from exchanging influencers and working together?

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Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

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AI Influencers: What Makes Them So Successful? - Analytics Insight

VUNO will showcase four medical artificial intelligence solutions:VUNO Med-Chest X-Ray, VUNO MedLungCT AI,VUNO Med-DeepBrain andVUNO Med-BoneAge that can be seamlessly integrated with PACS(Picture Archiving and Communication System), EMR(Electronic Medical Record) systems or any medical devices. A virtual reading room linked to the PACS system will be set up in the VUNO booth so that visitorscan get a first-hand look at VUNO Med solutions that integrated with PACS.

VUNO will present the future R&D direction of VUNO at the AI Theater under the topic 'The Past, Present and the Future of VUNO Med for Precision Imaging.' Following that, nine research achievements will be presented at a research presentation session. It includes topics encompassing various medical imaging fields, such as MRI-based AI model in prediction of MCI to dementia, AI-aided diagnosis to diagnose lung nodule malignancy, basal lung metastasis in colon cancer patients.

Furthermore, VUNO will participate in IAIP (Imaging AI in Practice), a virtual demonstration program developed to effectively demonstrate the effects of AI solutions in clinical settings. The company will showcase VUNO MedLungCT AI on this program to demonstrate how it works by using real-world clinical scenarios.

Hyun-Jun Kim, CEO of VUNO said, "Through RSNA 2021 participation, we will introduce various clinical research achievements to global radiology officials using AI technology," Hyun-jun Kim, CEO of VUNO said, adding "we plan to expand our partnerships with overseas partner companies and medical institutions by offering an opportunity for participants to directly experience VUNO Med solutions."

SOURCE VUNO Inc.

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VUNO Boasts of its AI Solutions and Research Results at RSNA 2021 - PRNewswire

In this paper, a comprehensive overview on current applications of AI in oncology-related areas is provided, specifically describing the AI-based devices that have already obtained the official approval to enter into clinical practice. Starting from its birth, AI demonstrated its cross-cutting importance in all scientific branches, showing an impressive growth potential for the future. As highlighted in this study, this growth has interested also oncology and related specialties.

In general, the application of the FDA-approved devices has not been conceived as a substitute of classical analysis/diagnostic workflow, but is intended as an integrative tool, to be used in selected cases, potentially representing the decisive step for improving the management of cancer patients. Currently, in this field, the branches where AI is gaining a larger impact are represented by the diagnostic areas, which count for the vast majority of the approved devices (>80%), and in particular radiology and pathology.

Cancer diagnostics classically represents the necessary point of start for designing appropriate therapeutic approaches and clinical management, and its AI-based refining represents a very important achievement. Furthermore, this indicates that future developments of AI should also consider unexplored but pivotal horizons in this landscape, including drug discovery, therapy administration and follow-up strategies. In our opinion, for determining a decisive improvement in the management of cancer patients, indeed, the growth of AI should follow comprehensive and multidisciplinary patterns. This represents one of the most important opportunities provided by AI, which will allow the correct interactions and integration of oncology-related areas on a specific patient, rendering possible the challenging purposes of personalised medicine.

The specific cancer types that now are experiencing more advantages from AI-based devices in clinical practice are first of all breast cancer, lung cancer and prostate cancer. This should be seen as the direct reflection of their higher incidence compared with other tumour types, but in the future, additional tumour types should be taken into account, including rare tumours that still suffer from the lack of standardised approaches. Since AI is based on the collection and analysis of large datasets of cases, however, the improvement in the treatment of rare neoplasms will likely represent a late achievement. Notably, if together considered, rare tumours are one of the most important category in precision oncology [11]. Thus, in our opinion, ongoing strategies of AI development cannot ignore this tumour group; although the potential benefits seem far away, it is already time to start collecting data on rare neoplasms.

One of the most promising expectancy for AI is the possibility to integrate different and composite data derived from multi-omics approaches to oncologic patients. The promising tools of AI could be the only able to manage the big amount of data from different types of analysis, including information derived from DNA and RNA sequencing. Along this line, the recent release of American College of Medical Genetics standards and guidelines for the interpretation of the sequence variants [12] has fostered a new wave of AI development, with innovative opportunities in precision oncology (https://www.businesswire.com/news/home/20190401005976/en/Fabric-Genomics-Announces-AI-based-ACMG-Classification-Solution-for-Genetic-Testing-with-Hereditary-Panels; last access 09/21/2021). In our opinion, however, the lack of ground-truth information derived from protected health- data repositories still represents a bottleneck in evaluating the accuracy of AI applications for clinical decision-making.

Overall considered, AI is providing a growing impact to all scientific branches, including oncology and its related fields, as highlighted in this study. For designing new development strategies with concrete impacts, the first steps are representing by knowing its historical background and understanding its current achievements. As here highlighted, AI is already entered into the oncologic clinical practice, but continuous and increasing efforts should be warranted to allow AI expressing its entire potential. In our opinion, the creation of multidisciplinary/integrative developmental views, the immediate comprehension of the importance of all neoplasms, including rare tumours and the continuous support for guaranteeing its growth represent in this time the most important challenges for finalising the AI-revolution in oncology.

See more here:

Artificial intelligence in oncology: current applications and future perspectives | British Journal of Cancer - Nature.com

When it comes to writing content, a significant number of businesses are relying on artificial intelligence (AI).

This content writing trend was unveiled by a new large-scale study conducted by Semrush, providers of content, SEO, PPC and social media solutions.

Semrushs State of Content Ops & Outsourcing Report 2021 explored how businesses of various industries and sizes manage their content operations, including content writing. The study involved Semrush conducting more than 20 comprehensive video interviews with reputable marketers.

One of the most surprising findings unveiled by the research is that 12% of respondents admit to using AI writing technology to create content.

44% of the businesses that outsource their content requirements to content writers say their choice of writer is dependent on the quality of service they provide. Most of the respondents (61%) define quality as writers have to cover a topic in-depth, in the most comprehensive way possible.

The report reveals some important insight regarding the creation of content that small businesses can take away. For example, with 12% of businesses using AI for content creation, this type of technology should not be dismissed as a means of taking care of content tasks.

It also shows the importance of outsourcing tasks like content writing, not only to ensure the quality content is produced, but also to free up a small businesss time to concentrate on other areas of the business.

As Agata Krzysztofik, Vice President of Piktochart, commented for the Semrush report: We had an ambitious plan to create all of our content in-house. Why? Because internally, we know our product best. Good storytelling is also part of our DNA. Unfortunately, reality hit us. Its difficult to write that much content if you have a small marketing team.

That said, finding appropriate content writers is not without its challenges. The respondents were asked about what they found difficult in finding writers. 49% of businesses said their issues were centered on finding writers with hands-on experience and knowledge of the subject. 41% reported their primary challenge as the content quality not being high enough.

The report also shows that outsourcing content commitments is not exceptionally costly, with most business spending less than $1k a month on content writing.

Image: Depositphotos

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You'll Probably Be Surprised to Know How Many Companies are Using AI Writing Tools - Small Business Trends

Defining what is, or isnt artificial intelligence can be tricky (or tough). So much so, even the experts get it wrong sometimes. Thats why MIT Technology Reviews Senior AI Editor Karen Hao created a flowchart to explain it all. In this bonus content our host and her team reimagined Haos original reporting, gamifying it into a radio play.

This episode was reported by Karen Hao. It was adapted for audio and produced by Jennifer Strong and Emma Cillekens. The voices you hear are Emma Cillekens, as well as Eric Mongeon and Kyle Thomas Hemingway from our art team. Were edited by Michael Reilly and Niall Firth.

[:15 pre-roll]

[TR ID]

Jennifer: Hi there. Im Jennifer Strong host of In Machines We Trust.

Defining what is, or isnt artificial intelligence can be a little tough. So much so, that even the experts get it wrong sometimes. Thats why Tech Reviews senior AI editor Karen Hao created a flowchart to explain it and together, we turned into this next episode Its silly. Its fun. And we hope it helps.

I also want to tell you about something really special weve been working on for more than a year. Its called The Extortion Economy. Its a short podcast series about the ransomware epidemic produced in collaboration with ProPublica. And Its available now wherever you like to listen.

[Show ID]

Emma Cilikens: Ladies and gentlemen... Welcome to This is AI...

Players will ask questions that get to the bottom of what it is or isnt AI And... Ive brought along an assistant to help out with the answers

Voice assistant: Hello.

Emma Cilikens: Hello, Alexa.

Emma Cilikens: And just so were all on the same page Artificial Intelligence... in its broadest sense refers to machines that can learn, reason, and act for themselves. They can make their own decisions when faced with new situations, much like humans and animals do.

Emma Cilikens: Now this bell... [SOT: ding] ...means correctly identified AI and this buzzer... [SOT: buzzer, crowd sigh] Well... not so much.

Emma Cilikens: Ok. So, lets test your knowledge.. Ready... set player one, go! ..

Eric Mongeon: Can it see...

Voice assistant: Yes.

Eric Mongeon: Can it identify what it sees...

Voice assistant: No ...[SOT: buzzer]

Emma Cilikens: Ok, so thats just a camera

Eric Mongeon: ok ok but what if it can identify what it sees?

[SOT: ding, ding, ding]

Emma Cilikens: Yep - thats computer vision and image processing. Player two!

Kyle Thomas Hemingway: Can it hear...

Voice assistant: Yes

Kyle Thomas Hemingway: Does it respond in a useful, sensible way to what it hears?

Voice assistant: Yes

[SOT: DING DING DING]

Emma Cilikens: So, thats NLPnatural language processing.

The goal of this kind of AI is to help computers make sense of human languages in a way thats useful.

But what if it doesnt respond in a useful, sensible way to what it hears. Could that also be AI?

Kyle Thomas Hemingway: If it's transcribing what you say

[SOT: bell ding, ding, ding]

Emma Cilikens: Yes! Thats also AIits speech recognition, which is similar but working from the spoken word instead of text. New round of questions! Player 1.

Eric Mongeon: Can it read?

Voice assistant: Yes

Eric Mongeon: Is it reading what you type?

Voice assistant: No

Eric Mongeon: Is it reading passages of text?

Voice assistant: Yes

Eric Mongeon: Is it analyzing the text for patterns?

Voice assistant: Yes

[SOT: ding, ding, ding]

Emma Cilikens: Yes, once again thats NLPnatural language processing. Well done!

Kyle Thomas Hemingway: Ill take that same question again - Can it read?

Voice assistant: Yes

Kyle Thomas Hemingway: Is it reading what you type?

Voice assistant:: Yes

Kyle Thomas Hemingway: Does it respond in a sensible, useful way?

Voice assistant: Yes

[SOT: ding, ding, ding]

Emma Cilikens: Thats also NLPnatural language processing. New question please player 1.

Eric Mongeon: Can it reason?

Voice assistant: Yes

Eric Mongeon: Is it looking for patterns in massive amounts of data?

Voice assistant: Yes

Eric Mongeon: Is it using these patterns to make decisions?

Emma Cilikens: Well, if not, that sounds like math.

Eric Mongeon: But if it is using patterns to make decisions?

Voice assistant: Yes

[SOT: ding, ding, ding]

Emma Cilikens: Then thats machine learningwhich is when a machine learns through experience. Ok. Final round!

Kyle Thomas Hemingway: Can it move?

Voice assistant: Yes.

[SOT: ding, ding, ding]

Kyle Thomas Hemingway: By itself, without help?

Voice assistant: Yes.

[SOT: ding, ding, ding]

Kyle Thomas Hemingway: Does it move based on what it sees and hears?

Voice assistant: Yes.

[SOT: ding, ding, ding]

Kyle Thomas Hemingway: Are you sure its not just moving along a pre-programmed path?

Voice assistant: [Alexa] Hmmm. Im not sure.

Emma Cilikens: Very funny but if so, thats just a bot.

[SOT: buzzer, crowd sigh]

Kyle Thomas Hemingway: Ok, lets try that again. Is it moving along a pre-programmed path?

Voice assistant: No.

[SOT: ding, ding, ding]

Emma Cilikens: Ok, so thats a smart robot, meaning one thats using AI to make some of its own decisions.

Great.

And thats the game.

Thanks for playing!

[Music up full]

Jennifer: Well be back - right after this.

[MIDROLL]

[MUSIC]

Jennifer: Many thanks to the talented voices in this episodeincluding our producer, Emma Cillekens, with Eric Mongeon and Kyle Thomas Hemingway. The editors are Michael Reilly and Niall Firth.

Thanks for listening Im Jennifer Strong.

[Post Roll: TR ID]

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Podcast: What is AI? We made this radio play to help. - MIT Technology Review

Subtle Medical's AI technologies acquire and improve high quality images from both accelerated and low dose scans.

MRI exams enable physicians to obtain detailed images of the body and help them answer critical medical questions in the diagnosis and monitoring of disease. In light of an aging population, the need for medical imaging to facilitate diagnosis, treatment decisions and therapy planning has grown. Artificial intelligence is a promising tool to support radiologists in their task to provide accurate and timely diagnosis to their patients, in addition to offering opportunities to speed up imaging exam times and reduce dose. In particular, Subtle Medical's suite of deep learning technologies aid in higher quality image acquisition from both accelerated and low dose scans. Both companies are looking forward to joining forces to drive innovation in medical imaging.

"We are proud to collaborate with Bayer to combine our expertise and drive innovative solutions in light of the ever increasing demands of medical imaging," said Dr Ajit Shankaranarayanan, Chief Product Officer at Subtle Medical. "At Subtle Medical, we are committed to continued innovation and the development of AI tools that create a meaningful impact for both radiologists and patients."

Subtle Medical's clinically validated AI-powered software solutions increase the efficiency of image capture by improving the quality of accelerated and low dose imaging. Subtle's solutions are compatible with all scanner brands, models and field strengths so institutions can see the benefit across their entire scanner fleet. Meet with Subtle Medical at RSNA 2021.

"Bayer is a key partner due to their clinical expertise and proven track record in medical imaging. Such collaborations across the industry will be crucial to unlock the power of AI, ultimately benefiting patients, improving imaging capabilities and patient diagnosis worldwide," said Josh Gurewitz, Chief Commercial Officer at Subtle Medical.

About Subtle MedicalSubtle Medical is a global healthcare technology company with a suite of deep learning solutions, including SubtlePET and SubtleMR, that increase the quality and efficiency of medical imaging. Subtle's deep learning solutions are deployed in top academic hospitals and leading imaging institutions worldwide. It was named CB Insights Top AI 100 and Digital Health 150 company in 2020 and is an Nvidia Inception Award Winner. For more information, please visit subtlemedical.comor email [emailprotected].

Media contact:Anna Menyhart-BorroniHead of Marketing+1 501.766.2662[emailprotected]

About BayerBayer is a global enterprise with core competencies in the life science fields of health care and nutrition. Its products and services are designed to help people and planet thrive by supporting efforts to master the major challenges presented by a growing and aging global population. Bayer is committed to drive sustainable development and generate a positive impact with its businesses. At the same time, the Group aims to increase its earning power and create value through innovation and growth. The Bayer brand stands for trust, reliability and quality throughout the world. In fiscal 2020, the Group employed around 100,000 people and had sales of 41.4 billion euros. R&D expenses before special items amounted to 4.9 billion euros. For more information, go to http://www.bayer.com.

Media contact: Jennifer MayBayer Communications+1 412.656.8192[emailprotected]

SOURCE Subtle Medical

Original post:

Subtle Medical and Bayer Collaborate to Advance Deep Learning Research to Harness the Power of AI in Medical Imaging - PRNewswire

When TechCrunch asks Flowrite if its Grammarly on steroids, CEO and co-founder Aaro Isosaari laughs, saying thats the comment they always get for the AI writing productivity tool theyve been building since late summer 2020 drawing on early access to OpenAIs GPT-3 API, and attracting a wait-list of some 30,000 email-efficiency seeking prosumers keen to get their typing fingers on its beta.

The quest for Inbox zero via lightning speed email composition could be rather easier with this AI-powered sidekick. At least if youre the sort of person who fires off a bunch of fairly formulaic emails each and every day.

What does Flowrite do exactly? It turns a few instructions (yes you do have to type these) into a fully fledged, nice to read email. So where Grammarly helps improve a piece of (existing) writing, by suggesting tweaks to grammar/syntax/style etc., Flowrite helps you write the thing in the first place, so long as the thing is email or some other professional messaging type comms.

Email is what Flowrites AI models have been trained on, per Isosaari. And frustration with how much time he was having to spend composing emails was the inspiration for the startup. So its focus is firmly professional comms rather than broader use cases for AI-generated words, such as copy writing etc (which GPT-3 is also being used for).

In my previous work I knew that this is a problem that I had Id spend several hours every day communicating with different stakeholders on email and other messaging platforms, he says. We also knew that there are a lot more people its not just our problem as co-founders; theres millions of people who could benefit from communicating more effectively and efficiently in their day to day work.

Heres how Flowrite works: The user provides a set of basic (bullet pointed) instructions covering the key points of what they want to say and the AI-powered tool does the rest generating a full email text that conveys the required info in a way that, well, flows.

Automation is thus doing the wordy leg work of filling in courteous greetings/sign-offs and figuring out appropriate phrasing to convey the sought for tone and impression.

Compared to email templates (an existing tech for email productivity), Isosaari says the advantage is the AI-powered tool adapts to context and isnt static.

One obvious but important point is that the user does also of course get the chance to check over and edit/tweak the AIs suggested text before hitting send so the human remains firmly the agent in the loop.

Isosaari gives an example use-case of a sales email where the instructions might boil down to typing something like sounds amazing lets talk more in a call next week, Monday PM in order to get a Flowrite-generated email that includes the essential details plus all the greetings and added formalities the extended email format requires.

(Sidenote: Flowrites initial pitch to TechCrunch was via email but did not apparently involve the use of its tool. At least the email did not include a disclosure that: This email isFlowritten as a later missive from Isosaari (to send the PR as requested) did. Which, perhaps, gives an indication of the sorts of email comms you might want to speed-write (with AI) and those you maybe want to dedicated more of your human brain to composing (or at least look like you wrote it all yourself).)

Weve built an AI powered writing tools that helps professionals of all kinds to write and communicate faster as part of their daily workflow, Isosaari tells TechCrunch. We know that theres millions of people who spend hours every day on emails and messages in a professional context so communicating with different stakeholders, internally and externally, takes a lot of work, daily working hours. And Flowrite helps people to do that faster.

The AI tool could also be a great help to people who find writing difficult for specific reasons such as dyslexia or because English is not their native language, he further suggests.

One obvious limitation is that Flowrite is only able to turn out emails in English. And while GPT-3 does have models for some other common languages, Isosaari suggests the quality of its human-like responses there might not be as good as they are in English hence he says theyll remain focused there for now.

Theyre using GPT-3s language model as the core AI tech but have also, recently, begun to use their own accumulated data to fine tune it, with Isosaari noting: Already weve built a lot of things on top of GPT-3 as opposed to building a wrapper on it.

The startups promise for the email productivity tool is also that the AI will adapt to the users writing style so that faster emails wont also mean curtly out of character emails (which could lead to fresh emails asking if youre okay?).

Isosaari says the tech is not not mining your entire email history to do this but rather only looks at the directly preceding context in an email thread (if there is one).

Flowrite does also currently rely on cloud processing, since its calling GPT-3s tech, but he says they want to move to on-device processing, which would obviously help address any confidentiality concerns, when we ask about that.

For now the tool is browser-based and integrates with web email. Currently it only works for Chrome and Gmail but Isosaari confirms the teams plan is to expand integrations such as for messaging platforms like Slack (but still initially at least, only for the web app version).

While the tech tool is still in a closed beta, the startup has just announced a $4.4 million seed raise.

The seed is led by Project A, along with Moonfire Ventures and angel investors Ilkka Paananen (CEO & Co-founder of Supercell), Sven Ahrens (director of global growth at Spotify), and Johannes Schildt (CEO & Co-Founder of Kry). Existing investors Lifeline Ventures and Seedcamp also joined in the round.

What types of emails and professionals is Flowrite best suited for? On the content side, Isosaari says its typically replies where theres some kind of existing context that you are responding to.

Its able to understand the situation really well and adapt to it in a really natural way, he suggests. And also for outreaches things like pitches and proposals What it doesnt work that well for is if you want to write something that is really, really complex because then in order to do that you would need to have all that information in the instructions. And then obviously if you need to spend a lot of time writing the instruction that could be even close to the final email and theres not much value that Flowrite can provide at that point.

Its also obviously not going to offer great utility if youre firing off really, really short emails since if youre just answering with a couple of words its likely quicker to type that yourself.

In terms of whos likely to use Flowrite, Isosaari says theyve had a broad range of early adopters seeking to tap into the beta. But he describes the main user profile as executives, managers, entrepreneurs who communicate a lot on a daily basis aka, people who need to give a good impression about themselves and communicate very thoughtfully.

On the business model front, Flowrites initial focus is on prosumers/individual users although Isosaari says it may look to expand out from there, perhaps first supporting teams. And he also says he could envisage some kind of SaaS offering for businesses down the line.

Currently, its not charging for the beta but does plan to add pricing early next year.

Once we move out of the beta then well be starting to monetize, he adds, suggesting that a full launch out of beta (so no more waitlist) could happen by mid 2022.

The seed funding will primarily be spent on growing the team, according to Isosaari, especially on the engineering side with the main goal at this early stage being to tool up around AI and core product.

Expanding features is another priority including adding a horizontal way of using the tool across the browser, such as with different email clients.

This report was updated to clarify a quotation by Isosaari as he intended to say Flowrite is not just building a wrapper for GPT-3 but rather that its building on top of it

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Flowrite is an AI writing productivity tool that wants to help you hit inbox zero - TechCrunch

This FDA clearance, received after submitting results from a multi-reader study conducted earlier this year, makes Mammoscreen immediately available to the entire US mammography market. Study findings revealed improvement in readers' performance in screening for lesions when paired with MammoScreen compared to radiologists alone. Additionally, the study showed significant time saving when using MammoScreen.

MammoScreen automatically detects and characterizes suspicious soft tissue lesions and calcifications in mammography and tomosynthesis images while assessing their likelihood of malignancy. The results are summarized by the MammoScreen Score that characterizes suspiciousness of each lesion scored on a scale of 1-10, with 1, being least likely to reveal malignancy and 10 most likely.

"Receiving this second FDA clearance for MammoScreen is a major milestone for Therapixel," said Pierre Fillard, Ph.D., Founder and Chief Scientific Officer of Therapixel. "Thanks to a deep and fruitful collaboration with radiologists, we have, over the last 18 months, turned the 2017 DREAM challenge winning 2D algorithm to a powerful FDA-cleared product for both 2D and 3D mammography. MammoScreen can now assist all radiologists in their day-to-day-work whichever modality they are using."

"MammoScreen now covers both the gold standard 2D mammography and the state-of-the-art 3D tomosynthesis modalities. This provides quick and reliable confirmation of Radiologists' suspicions as they read," said Matthieu Leclerc-Chalvet, Therapixel CEO. "Tomosynthesis is known as time consuming because of the large number of images to review. MammoScreen allows a more optimized and certain assessment by Radiologists and a speedier reassurance of women having breast cancer screening exams, resulting in a more efficient workflow and reduced costs for the healthcare system. Thanks to this new FDA clearance, we expect significant growth in the US market and we look forward to installing MammoScreen in additional radiology departments and institutions across the U.S. so that imaging professionals and women can benefit from its use."

Breast cancer is the second cause of cancer death worldwide. In the United-States, 1 in 8 women will develop breast cancer during their lifetime. Early detection is the key to successful treatment.

About Therapixel

Therapixel is a French company specialized in the design and commercialization of AI-powered medical imaging software, with the goal of delivering pertinent information at the right time to healthcare professionals. Winner of the Digital Mammography DREAM Challenge in 2017, the global competition on breast cancer detection with AI, Therapixel has developed MammoScreen, a software that helps with the interpretation of screening 2D and 3D mammograms. With MammoScreen, even experienced radiologists can improve their performance, feel more confident in their judgment, and reassure women quicker. Find more atwww.mammoscreen.com

SOURCE Therapixel

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Therapixel receives FDA clearance for use of its AI-based breast cancer screening software MammoScreen on Digital Breast Tomosynthesis - PRNewswire

November 28, 2021 Lunit, a medical AI provider, is returning to the 107th Radiological Society of North America (RSNA) this year with its up-to-date, comprehensive AI solutions for chest and breast radiology. Along with its most mature products, the company will showcase the demo version of the brand-new AI solution for digital breast tomosynthesis (DBT) and chest CT in its booth #4545, RSNA South Hall.

Lunits most mature products for radiology include AI solutions for chest x-ray, Lunit INSIGHT CXR and for mammography, Lunit INSIGHT MMG, which have been first introduced at RSNA 2017 and 2018 respectively. The company has been presenting the upgraded versions of the software ever since, now clinically available in 400 medical sites in more than 30 countries. This time the company is expanding Lunit INSIGHT product lines with DBT and chest CT solutions.

According to the company, its AI solution for DBT is called Lunit INSIGHT DBT, which can accelerate reading by handpicking the 3D slice image with suspicious lesions among multiple images. Its solution for CT is Lunit INSIGHT Chest CT, which AI automatically generates the location, type, volume, and axial diameter of the detected nodule.

Chest x-ray and mammography are the first-line examinations where you find suspicious diseases that can cause lung or breast cancer, said Sunggyun Park, Chief Product Officer of Lunit. CT and DBT are more sophisticated examinations where you can further evaluate the specification of nodules or distinguish cancer. At this stage, our AI will be able to assist radiologists to diagnose cancer faster, at a higher accuracy. The products are now under final validation and expected to officially launch next year.

It is meaningful for us to introduce the demo of Lunit INSIGHT DBT and Lunit INSIGHT Chest CT for the first time at this years RSNA, where we can actually demonstrate the solution to our potential users, said Brandon Suh, CEO of Lunit. From x-ray, mammography to Chest CT, DBT and more, we will keep working on developing innovative AI solutions to offer comprehensive medical imaging--to improve radiologist workflow and patient health.

Lunit Booth

Visit booth #4545 South Hall for product demonstration for Lunit INSIGHT CXR, Lunit INSIGHT MMG, Lunit INSIGHT DBTand Lunit INSIGHT Chest CT. Available from Nov. 28 Dec. 2, 2021, 10 a.m. to 5 p.m. daily

Some of the products will be available for demonstration during RSNA, not only at Lunit booth, but also displayed at our global partners booths: FUJIFILM, GE Healthcare, Philips, INFINITT healthcare and SECTRA.

CEO Presentation at AI Theater

Lunits CEO, Brandon Suh, will be on stage at AI Theater to give an industry presentation about the recent and upcoming product developments, business activities on how our AI is clinically applied across global sites.

Title: Perfecting Intelligence, Transforming Medicine: A.I. in Lung and Breast Diagnostics

If you would like to book a media interview with our CEO, contact [emailprotected].

Presenting 13 Abstracts

Studies on the diagnostic value of AI in chest and breast radiology will be presented during the event. Abstracts include the effectiveness of AI in finding breast cancer on DBT analysis, clinical validation of Lunit INSIGHT CXR and Lunit INSIGHT MMG and more.

Search Lunit in RSNA meeting program to read our abstracts.

Book your meeting with Lunit at RSNA 2021 live or virtually

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Lunit to Participate in RSNA 2021, Presenting Its New AI Solutions for Digital Breast Tomosynthesis and Chest CT - Imaging Technology News