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Artificial intelligence technology is being used more and more and in many situations it is used to see how people would look with the passing of time or by removing aesthetic touch-ups

This is the case with a TikTok account, run by the Australian streamers Vandahood Live. They posted a video compilation of what the Kardashians would look like naturally on their profile.

Kim Kardashian, Kylie Jenner, Khloe Kardashian, Kris Jenner and Kourtney Kardashian have been the subjects of that now viral TikTok, showing their faces without the surgeries they have been getting over the last few years.

The real video used is from 'Keeping Up with the Kardashians' where you can see their real faces compared to the one generated by artificial intelligence.

The most obvious Kardashian differences are Kylie's thinner lips or Khloe's much thinner nose. In addition, both have admitted in interviews that they have had these surgeries.

"We used three different artificial intelligence software and two different standard graphics software and a full week to get it done," explained Keith, one of the members of Vandahood Live to PetaPixel.

"We had to take a different approach for each family member, as each experienced different changes over the years."

Deep Face Lab, FaceApp and EbSynth were the programs used to recreate the Kardashians.

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Artificial intelligence reveals what the Kardashians would look like without all their cosmetic surgeries - Marca English

Review time shortened from an average of nine days to less than one day

BOSTON, Oct. 12, 2022 /PRNewswire/ --Blue Cross Blue Shield of Massachusetts("Blue Cross") today announced the completion of a proof-of-concept pilot called "FastPass," an automated prior authorization process from end-to-end, eliminating the need for faxes, phone calls and manual processes for payers and providers. The initiative, piloted at New England Baptist Hospital (NEBH), focused on addressing the major problem areas, including reducing the time from submission to decision, alleviating administrative burden, decreasing clinical review time, and increasing clinician satisfaction.

The ProblemPrior Authorization (also known as "Pre-Certification") is a process through which a clinician seeks advanced approval from a health plan to ensure that a service or treatment is covered, medically necessary, and not duplicated. Prior authorizations exist to manage excess health care costs and mitigate patient risk while also helping ensure consumers receive high-quality care. However, prior authorization can be cumbersome for clinicians.

"We realize that the prior authorization process is widely recognized as the single biggest administrative pain point for hospital staff," said Kathy Gardner, RN, vice president of clinical operations at Blue Cross. "We wanted to figure out a way to retain the value of prior authorizations ensuring our members receive treatments that are medically necessary and clinically effective while eliminating the administrative burden on our clinical partners and allowing members to get the care they need sooner."

How it worksBlue Cross engaged Olive, a leading automation and intelligence company bridging the divide in health care, to help streamline both clinician and payer processes and prior authorization decision-making using artificial intelligence (AI).

The technology automated the process of cross-checking Blue Cross' prior authorization requirements in real-time to identify if a prior authorization was required. If a prior authorization was not required, the provider received instant notification that they could proceed with scheduling the procedure. When prior authorizations were required, FastPass used AI to cross-check the clinical history in the electronic medical record against Blue Cross' medical necessity criteria and automatically generate a recommendation in real time, again giving the clinician the ability to proceed with scheduling the procedure. For the remaining prior authorization submissions that required more complex clinical review, FastPass automatically packaged and made available all the clinical documentation and notes to the clinical review team, significantly streamlining and accelerating the reviews.

The ResultsThe pilot at NEBH focused on hip and knee procedures for 32 orthopedic providers over the course of a four-month period. 88% of prior authorization submissions were processed automatically in real-time. The overall impact on prior authorization approval time went from an average of nine days to an average of less than one day. The associated impact on administrative burden and cost has been significant for Blue Cross.

"The FastPass proof-of-concept is just one step in our journey toward automating prior authorizations across BCBSMA to continue to make the process frictionless for our clinical partners and ultimately our members," said Deb Vona, senior director of business operations at Blue Cross.

About Blue Cross Blue Shield ofMassachusettsBlue Cross Blue Shield ofMassachusetts(http://www.bluecrossma.org) is a community-focused, tax-paying, not-for-profit health plan headquartered inBoston. We are committed to the relentless pursuit of quality, affordable and equitable health carewithan unparalleled consumer experience.Consistent with our promise to always put our members first, we are rated among the nation's best health plans for member satisfaction and quality.Connect with us onFacebook,Twitter,YouTube,andLinkedIn.

About OliveOlive delivers automation and intelligence to bridge the divide in healthcare. By addressing the most burdensome operational issues, Olive is reducing costs and increasing capacity for hospitals, health systems and payers, so the focus can remain on delivering the best, most effective care to patients. To learn more about Olive, visit oliveai.com.

SOURCE Blue Cross Blue Shield of Massachusetts

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BLUE CROSS BLUE SHIELD OF MASSACHUSETTS USES ARTIFICIAL INTELLIGENCE TO SPEED REVIEW TIME, AUTOMATE AUTHORIZATIONS & ELIMINATE ADMINISTRATIVE...

Lantheus Holdings, Inc.

NORTH BILLERICA, Mass., Oct. 17, 2022 (GLOBE NEWSWIRE) -- Lantheus Holdings, Inc. (the Company) (NASDAQ: LNTH), a company committed to improving patient outcomes through diagnostics, radiotherapeutics and artificial intelligence solutions that enable clinicians to Find, Fight and Follow disease, showcased artificial intelligence (AI) data at the 2022 European Association of Nuclear Medicine (EANM) Annual Meeting in Barcelona, Spain.

PYLARIFY AI has the potential to contribute meaningful insights to inform treatment selection and monitoring in prostate cancer. Our presentations at EANM highlight new data on the clinical utility of our artificial intelligence solution to assess response to prostate cancer therapy, said Etienne Montagut, Chief Business Officer, Lantheus. Lantheus continues to be a leader in harnessing the power of AI and machine learning to inform clinical decisions, and support our mission to Find, Fight and Follow disease to deliver better patient outcomes.

A summary of the data presented is included below.

In an oral presentation, the Company reviewed the results from a retrospective analysis using aPROMISE to evaluate PSMA PET/CT scans, pre- and post-androgen deprivation therapy, of men with treatment nave castration sensitive prostate cancer. The results demonstrated that a change in automated PSMA scores in bone and lymph nodes is strongly associated with PSA response. The analysis also indicated that a quantitative automated PSMA-score may assess treatment response in bone, which is not feasible with conventional imaging.1 This presentation was chosen as a top-rated oral presentation within the scientific program at EANM.

In a poster presentation, the Company shared the results from an evaluation of the volumetric expression of PSMA in prostate tumor in PET/CT against MRI PIRAD-Index 4 and 5, in patients who underwent radical prostatectomy. The volumetric expression of PSMA was quantified into an automated PSMA score, which PSMA score was calculated using the PROMISE criteria. The automated PSMA score was observed to be significantly lower in patients with PIRAD score-4 (Median=21.40; 95% CI 10.76 - 40.65), compared to that observed in PIRAD score-5 (Median=37.00; 95% CI 24.68 - 56.05), p=0.014.2

In a second poster presentation, the Company highlighted the results from a study evaluating a novel methodology for adaptive lesion segmentation in PSMA PET/CT that employs a threshold based on a decreasing percentage of maximum Standard Uptake Value (SUVmax), with the percentage dependent on SUVmax and blood-pool uptake of PSMA PET/CT imaging. The study concluded that the adaptive threshold can be applied to improve reproducibility and robustness when quantifying tumor burden in PSMA PET/CT images. The proposed adaptive thresholding for automatic lesion segmentation demonstrated significantly more accurate segmentations than the conventional method, achieving an improved precision for all lesion types and a similar recall, as compared to the conventional method.3

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About PYLARIFY AIPYLARIFY AI employs deep learning algorithms that allow healthcare professionals and researchers to perform standardized quantitative assessment of PSMA PET/CT images in prostate cancer. Through rigorous analytical and clinical studies, PYLARIFY AI has demonstrated improved consistency, accuracy and efficiency in quantitative assessment of PSMA PET/CT. An FDA-cleared medical device software, PYLARIFY AI is commercially available in the United States.

PYLARIFY AI Indications for UsePYLARIFY AI is intended to be used by healthcare professionals and researchers for acceptance, transfer, storage, image display, manipulation, quantification and reporting of digital medical images. The system is intended to be used with images acquired using nuclear medicine imaging using PSMA PET/CT. The device provides general picture Archiving and Communications System (PACS) tools as well as a clinical application for oncology including marking of regions of interest and quantitative analysis.

PYLARIFY AI Warnings and PrecautionsThe user must ensure that the patients name, ID, and study date displayed in the patient section correspond to the patient case. The user must ensure the review of the image quality and quantification analysis results before signing the report. User must review the images and quantification results in the report to ensure that the information saved and exported is correct. The quantification analysis results provided by PYLARIFY AI are intended to be used as complementary information together with other patient information. The user shall not rely solely on the information provided by PYLARIFY AI for diagnostic or treatment decisions. Quantitative indexes (aPSMA Score) are only appropriate for PSMA PET/CT images. User should not select hotspots for studies with images that do not fulfill the Quality Control requirements. In such cases, user can create and sign a report indicating that the review cannot be done due to image quality deficiencies.

AboutLantheus With more than 60 years of experience in delivering life-changing science, Lantheus is committed to improving patient outcomes through diagnostics, radiotherapeutics and artificial intelligence solutions that enable clinicians to Find, Fight and Follow disease. Lantheus is headquartered in Massachusetts and has offices in New Jersey, Canada and Sweden. For more information, visit http://www.lantheus.com.

Safe Harbor for Forward-Looking and Cautionary StatementsThis press release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995, as amended, that are subject to risks and uncertainties and are made pursuant to the safe harbor provisions of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. Forward-looking statements may be identified by their use of terms such as can, continue, may, potential and other similar terms. Such forward-looking statements are based upon current plans, estimates and expectations that are subject to risks and uncertainties that could cause actual results to materially differ from those described in the forward-looking statements. The inclusion of forward-looking statements should not be regarded as a representation that such plans, estimates and expectations will be achieved. Readers are cautioned not to place undue reliance on the forward-looking statements contained herein, which speak only as of the date hereof. The Company undertakes no obligation to publicly update any forward-looking statement, whether as a result of new information, future developments or otherwise, except as may be required by law. Risks and uncertainties that could cause our actual results to materially differ from those described in the forward-looking statements include (i) our ability to successfully launch PYLARIFY AI as a commercial product; (ii) the market receptivity to PYLARIFY AI as a new digital application for quantitative assessment of PSMA PET/CT images in prostate cancer; (iii) the intellectual property protection of PYLARIFY AI; (iv) interruptions or performance problems associated with our digital application, including a service outage; (v) a network or data security incident that allows unauthorized access to our network or data or our customers data; and (vi) the risks and uncertainties discussed in our filings with the Securities and Exchange Commission (including those described in the Risk Factors section in our Annual Reports on Form 10-K and our Quarterly Reports on Form 10-Q).

1Anand A, et.al. PROMISE-criteria inspired quantitative response in PSMA PET to androgen deprivation in patients with treatment nave castration sensitive prostate cancer. 35th Annual Congress of the European Association of Nuclear Medicine Scientific Program, OP-060, p. 34 (Eur J Nucl Med Mol Imaging (2022) 49 (Suppl 1): S495).2Wang W. et.al. Evaluation of PROMISE criteria inspired intraprostatic PSMA-score against PIRAD-Index in patients undergoing radical prostatectomy. Eur J Nucl Med Mol Imaging (2022) 49 (Suppl 1): S489.3Brynolfsson J, et.al. A Novel Adaptive Approach to Automatic Segmentation of PSMA-positive Lesions in Positron Emission Tomography (PET) of Prostate Cancer. Eur J Nucl Med Mol Imaging (2022) 49 (Suppl 1): S596.

Contacts:Mark KinarneyVice President, Investor Relations978-671-8842ir@lantheus.com

Melissa Downs Senior Director, Corporate Communications646-975-2533media@lantheus.com

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Lantheus Presentations at the European Association of Nuclear Medicine Annual Meeting Showcased Artificial Intelligence Data - Yahoo Finance

AI takes on predictive power; humans retain judgement.

There has been no shortage of attention given to the potential of artificial intelligence, along with related concerns about bias, data viability, costs, and employee resistance. But we may be missing the most important point when it comes to AIs ultimate impact, a leading AI proponent argues. That is, were starting to outsource a large share of human decision-making to machines, which may have unforeseen implications beyond simply making cheaper predictions.

Its time to start looking at AI not from a technologists perspective, but from an economists perspective, states Ajay Agrawal, professor at the University of Toronto, and co-author of Power and Prediction: The Disruptive Economics of Artificial Intelligence. Agrawal recently shared his views on the coming AI wave in a talk hosted at the University of British Columbias Green College. AI is moving into its next phase moving up the decision-making food chain. This is where AI is moving from sidelines to a more central role in the economy, he says.

Overall, there has been disappointment with AI, as it does not appear to be delivering the miracles initially promised, he adds, noting that many things seem much less impacted than what we thought. Productivity growth even still continues to decline. At the same time, Agrawal continues, AI is still a work in progress, and were just beginning to see it unfold.

Agrawal maintains that its time to take an economists view of AI. A computer scientist or an engineer will talk about AI in terms of advances in neural networks. But if you ask an economist what's going on with AI, they will characterize it as a drop in the cost of prediction. As AI gets better and better, it effectively makes prediction cheaper and cheaper. This is significant because we use prediction everywhere. Prediction is embedded in all kinds of things where you might not think of prediction for example, autonomous driving.

Decision-making, which is the source of financial and political power in the economy, has two components: prediction and judgment, Agrawal says. These two functions are being decoupled in AI systems humans are retaining judgement, but turning prediction over to AI. We are constantly making some form of a probability assessment and a judgment assessment whether we realize it or not, he says. The rise of AI is shifting one of those ingredients prediction from humans to machines. Were outsourcing the prediction part to the machine.

To date, AI has focused on point solutions transcribing text, detecting errors in production lines, and so forth. We've picked all the low-hanging fruit of all the point solutions where you just get a prediction, the prediction leads to a simple action, Agrawal says. Like a tool linked to a camera that predicts if a tooth on a digger in a mining operation if the tooth is broken. That's a point solution, a prediction that leads to a specific action. It doesn't impact anything else in the operation.

AI begins to realize greater value when you start building a fully autonomous system, where one prediction one decision impacts all many other decisions, Agrawal points out. From an economics perspective, we're into the realm of game theory, where if we change a decision how does that impact all other decisions?

Moving the predictive aspect of decisions to machines can be an eye-opening experience as it rolls out. AI opens the door to a flourishing of new decisions, Agrawal says. Many of these decisions are new because we previously hid them via rules, insurance and over-engineering, he says. We did such a good job hiding them that weve long since forgotten they were ever there. AI is unearthing these long-hidden, latent decisions.

This is more than an exercise in creativity it means power. Decision-making confers power; changes in decision-making can lead to changes in power, he says. Centralizing or decentralizing decision-making will consolidate or distribute power.

This means transformation throughout the economy, Agrawal states. AI is arguably the first tool in human history that learns as you use it. The more you use it, the smarter it gets because every time you use it.

Continued here:
Economists View Of Artificial Intelligence: Beyond Cheaper Prediction Power - Forbes

U.S. government investment in artificial intelligence has grown significantly in the last few years, as evidenced by the additional funding for AI research in President Bidens 2023 fiscal budget.

With more than $2 billion allocated to the National Institute of Standards and Technology and the Department of Energy for AI research and development, it is clear there is a growing enthusiasm for the technology in government.

Driving the push to implement AI is the urgent need to address federal employee burnout. A recent study found that almost two-thirds of government employees are experiencing burnout, a much higher rate than seen in the private sector. Furthermore, almost half of respondents are considering leaving their government jobs within the next year due to increased burnout and stress.

One immediate solution to help this potential crisis is the responsible implementation of artificial intelligence. AI can mitigate the impact of burnout by removing repetitive and time-consuming tasks and streamlining processes, reducing the overall burden and repetitive nature of government work. However, effective AI investments demand more than just funding and technology.

Agencies must balance efforts to scale investment in AI while responding to the unique needs and challenges of the many diverse teams that make up the federal workforce.

Currently, there is a lack of cohesive guidance leading government efforts around AI. While organizations such as NIST have released basic AI framework for RMF, organizations without any AI experience may struggle to build the necessary foundation for a mature, agile AI posture. To lay the groundwork for a long-term AI strategy while generating short-term gains to support the federal workforce quickly, agencies must consider three guideline components of AI.

Dedicated funding for AI is only one component of an effective AI strategy. Before implementing new technologies, agencies must start with their existing processes beginning with their level of data maturity.

If an agency does not have enough historical data to analyze, or the data they do have is not organized, implementing AI can create extra work on the front end for federal workers who could find themselves sorting through inaccurate or incomplete data processed by AI. For example, in response to this challenge, the Department of Defense stood up the Chief Digital and Artificial Intelligence Office (CDAO) to lead the deployment of AI across DoD, including the Departments strategy and policy on data.

Once agencies realize a baseline of data maturity, they can pilot basic AI applications such as automating basic tasks, empowering agencies to gather high-quality data and provide analysis and insight around that data, providing the information needed to create a scalable AI roadmap that can integrate with other IT modernization technologies.

But having a roadmap alone is not enough to ensure that AI-driven technologies are useful for the federal workforce.

To implement AI that truly supports federal workers, agencies need to understand the main pain points and challenges facing federal employees. For most private enterprises looking to implement new technology, user experience surveys would be a core part of the pilot program to ensure an analytics-driven understanding of the technologys successes and gaps.

However, although employee input is a crucial part of the AI planning process, government surveys are often expensive and can take months or years to consolidate into actionable data.

One way to combat this difficulty is utilizing existing AI to inform AI investments. For example, instead of sending out a survey where the results may take months to receive, an AI dashboard may provide a real-time view of overall work showing what areas need more support or automate a simple response survey where employees can provide input.

Using relatively basic AI to evaluate implementation allows agencies to gain insight into the needs of the workforce more sustainably and effectively than surveys, showing IT leaders where to implement AI for the most impact.

Once agencies have an AI baseline and understand worker needs, the last step to implementing employee-focused AI is creating a robust AI-empowered employee experience program.

There are many ways that AI can help agencies with experience management, from automating timesheets to streamlining business decisions. When AI is designed with these improvements in mind, AIs tangible benefits support both broader organizational goals and the humans working to achieve them.

Scaling AI beyond pilot programs remains a challenge. One of the primary responsibilities of the CDAO is to develop processes for AI-enabled capabilities to be developed and fielded at scale across the defense space.

CDAO addressed this issue by selectively scaling only proven AI solutions for enterprise and joint use cases. Prioritizing proven solutions ensures that the AI they are implementing runs smoothly, is easy to use and most importantly is familiar to the workforce. As AI solutions become more sophisticated, agencies can continue to expand until they have a fully scalable AI network designed by and for humans.

Contrary to much of the conversation around AI, people are the most essential component of successful artificial intelligence programs. For implementation to be successful, agencies need a human-centered AI mindset. Following these three guidelines to create human-centered AI creates space for the federal workforce to be exponentially more creative, productive and ultimately more effective in furthering agency missions, equipping leaders to elevate the full potential of their teams.

Dr. Allen Badeau is the chief technology officer for Empower AI, as well as the director of the Empower AI Center for Rapid Engagement and Agile Technology Exchange (CREATE) Lab.

This article is an Op-Ed and the opinions expressed are those of the author. If you would like to respond, or have an editorial of your own you would like to submit, please email Federal Times Senior Managing Editor Cary OReilly.

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Artificial intelligence in government is about people, not programming - Federal Times