Computer vision systems can identify dental deterioration using various object identification and semantic segmentation techniques

FREMONT, CA: AI-powered dental imaging software can assist in swiftly and efficiently making sense of the data. Machine learning algorithms also outperformed dentists in diagnosing tooth decay and predicting whether a tooth should be removed, kept, or restored. Before you worry that a robot will replace the kind human who looks after your teeth, know that ML and computer vision systems are being used to assist your dentist in providing the best possible treatment.

Detection of dental deterioration

Enlisting the assistance of other (computer vision) eyes can increase dentists' capacity to diagnose and treat difficulties in the same. And sometimes, that extra assistance is more valuable than you might think. Computer vision systems can identify dental deterioration using various object identification and semantic segmentation techniques. One method is to train CNNs on large sets of photos, including labeled carious lesions.

Oral cancer screening

While losing a tooth is upsetting, it pales in comparison to the consequences of oral cancer. Furthermore, diagnosing the early signs of oral cancer is not difficult. Visible oral lesions known as "oral potentially malignant disorders" (OPMDs) are a significant indicator of cancer and can be found during routine oral exams by a general dentist. The issue is that this type of inspection is not performed frequently enough during dental visits. If only there were simple, low-cost methods for automating the detection of cancerous or potentially malignant tumors.

Dental caries detection and diagnosis

Early identification of dental caries, like oral cancer, is crucial to preventing irreversible injury. Cavities that are addressed early minimize treatment costs, restoration time, and the chance of tooth loss dramatically. However, computer-aided detection and diagnosis systems (CAD) are gradually becoming a common feature of dental clinics. These technologies can detect oral pathology by reading dental X-rays and cone-beam computed tomography (CBCT) pictures. Furthermore, computer vision-powered systems can assess lesion depth and use this information to detect and classify lesions.

Endodontics

Endodontics is something you've probably heard of if you've ever had a root canal. Fortunately, artificial intelligence (AI) offers applications that can assist dentists in detecting and treating these feared illnesses even more effectively. Endodontists often examine, measure, and evaluate the status of the tooth beneath the gums using radiographic imaging. Deep learning algorithms can then detect, locate, and classify various elements of tooth root anatomy and potential diseases. It is beneficial for locating specific tooth features and identifying particular types of fissures and lesions in or around the tooth.

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Major Applications of Artificial Intelligence in Dentistry - Healthcare Tech Outlook

The use of artificial intelligence (AI) for war has been a promise of science fiction and politicians for years, but new research from the Georgia Institute of Technology claims to show the value that AI can automate only a limited subset of human judgment. "All of the hard problems in AI really are judgment and data problems, and the interesting thing about that is when you start thinking about war, the hard problems are strategy and uncertainty, or what is well known as the fog of war," said Jon Lindsay, an associate professor in the School of Cybersecurity & Privacy and the Sam Nunn School of International Affairs. "You need human sense-making and to make moral, ethical, and intellectual decisions in an incredibly confusing, fraught, scary situation." AI decision-making is based on four key components: data about a situation, interpretation of those data (or prediction), determining the best way to act in line with goals and values (or judgment), and action. Machine learning advancements have made predictions easier, which makes data and judgment even more valuable. Although AI can automate everything from commerce to transit, judgment is where humans must intervene, Lindsay and University of Toronto Professor Avi Goldfarb wrote in the paper, "Prediction and Judgment: Why Artificial Intelligence Increases the Importance of Humans in War," published in International Security.

Many policymakers assume human soldiers could be replaced with automated systems, ideally making militaries less dependent on human labor and more effective on the battlefield. This is called the substitution theory of AI, but Lindsay and Goldfarb state that AI should not be seen as a substitute, but rather as a complement to existing human strategy.

"Machines are good at prediction, but they depend on data and judgment, and the most difficult problems in war are information and strategy," he said. "The conditions that make AI work in commerce are the conditions that are hardest to meet in a military environment because of its unpredictability."

An example Lindsay and Goldfarb highlight is the Rio Tinto mining company, which uses self-driving trucks to transport materials, reducing costs and risks to human drivers. There are abundant, predictable, and unbiased data traffic patterns and maps that require little human intervention unless there are road closures or obstacles.

War, however, usually lacks abundant unbiased data, and judgments about objectives and values are inherently controversial, but that doesn't mean it's impossible. The researchers argue AI would be best employed in bureaucratically stabilized environments on a task-by-task basis.

"All the excitement and the fear are about killer robots and lethal vehicles, but the worst case for military AI in practice is going to be the classically militaristic problems where you're really dependent on creativity and interpretation. But what we should be looking at is personnel systems, administration, logistics, and repairs," Lindsay said.

There are also consequences to using AI for both the military and its adversaries, according to the researchers. If humans are the central element to deciding when to use AI in warfare, then military leadership structure and hierarchies could change based on the person in charge of designing and cleaning data systems and making policy decisions. This also means adversaries will aim to compromise both data and judgment since they would largely affect the trajectory of the war. Competing against AI may push adversaries to manipulate or disrupt data to make sound judgment even harder. In effect, human intervention will be even more necessary.

Yet this is just the start of the argument and innovations.

"If AI is automating prediction, that's making judgment and data really important," Lindsay said. "We've already automated a lot of military action with mechanized forces and precision weapons, then we automated data collection with intelligence satellites and sensors, and now we're automating prediction with AI. So, when are we going to automate judgment, or are there components of judgment cannot be automated?"

Until then, though, tactical and strategic decision-making by humans continues to be the most important aspect of warfare. (ANI)

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An artificial intelligence-based strategy or judgement cannot be trusted by the military, according to researc - Times Now

Guest post from Jingchen Zhao, professor of law at Nottingham Law School

Zhao: AI brings many positive changes

Artificial intelligence (AI) is taking the business world by storm with its capability to collect, filter and react to data rapidly and in many different ways.

Applying AI technology to law firms involves the use of computers, algorithms and big data to assist, support, collaborate or even duplicate lawyers behaviours and decisions so that law firms can function competently, successfully and with foresight in their business environment.

The interconnected world enhanced by technologies such as AI brings many positive changes for the ways in which law firms communicate with their customers, clients and business partners, offering the advantage of providing more an efficient and effective service without compromising quality.

Although AI has not yet been developed to a level where AI-empowered legal advice could fully replace human legal practitioners, the adoption of AI has the potential to reduce transaction costs and improve the accessibility of legal advice through the use of automated assistants, digital hubs or software to offer AI-powered legal services for vulnerable clients.

In collaboration with the Hungarian digital law firm SimpLEGAL, InvestCEE LegalTech Consultancy issued AI in Legal Services A Practical Guide in December 2021, suggesting that AI offers new opportunities for digitalising legal services.

One of the most common ways of using AI in legal practice is to delegate certain tasks, especially where decisions need to be reached on the basis of a large quantity of data and legal practitioners are not capable of providing a swift response.

This kind of delegation can ease the tension between plausible hypotheses and the formal analysis of professional judgements by lawyers, allow the systematic study of issues in order to help legal practitioners make better decisions, and mitigate human limitations in terms of understanding complex data and making well-informed choices between the options available.

In addition to assistance with processing large quantities of data, efficient algorithms have empowered AI to make decisions at a near-instantaneous speed.

AI technologies are able to categorise solutions based on different criteria and priorities, assess the merits of each solution, and recommend a set of selected options for legal practitioners, who are then able to evaluate these solutions more efficiently and in a focused and informed manner.

This evaluation process can be made even more effective as algorithms can be configured to calculate and inform the confidence level of the selected options and assess the merits and disadvantages of each one.

In-house legal departments require more guidance in relation to the basic terminology used in the legal AI domain. When applying AI in a firm, it is also important to understand how this might change the firms risk profile, since AI can also be a disruptive technology, and accountable AI practice needs to be reinforced by regulatory insight to enable its sustainable development.

However, as yet no consensus has been reached on the most appropriate regulatory framework to achieve these goals.

The European Commission is taking a lead in terms of regulating AI globally, proposing a risk-based regulatory framework that involves determining the scale or scope of risks related to a concrete situation and a recognised threat.

This framework is also likely to be useful in unpacking the potential role and challenges of AI in promoting more accountable law firms and legal professionals, considering the benefits that accountable and sustainable AI could bring to law firms to protect their clients, particularly vulnerable ones.

By facilitating the use of AI services, the commissions regulatory framework should help law firms to identify and meet the needs of clients who may have difficulty using legal services, or who may be at risk of acting against their own best interests.

An appropriate regulatory framework to promote sustainable AI by monitoring and mitigating the associated risks in legal practice is a pre-condition for using AI more comprehensively in the legal domain.

Instead of being a free-standing regulatory intervention, I believe that an ideal approach will be to construct a regulatory agenda and a control strategy to be combined with other control strategies across different social, economic and cultural contexts and tasks.

The design of this framework should encourage the participation of stakeholders with different expertise such as computer scientists, representatives from industrial organisations, active shareholders, specialist committees and counsel, and consultants or partners with expert technological skill sets, as well as international agencies.

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Using artificial intelligence in the legal profession - Legal Futures

Throughout history, technology has never advanced as quickly as it is right now. It's becoming harder than ever for investors to track the sheer number of innovative tech companies in the public markets, each offering its own unique vision for the future.

But perhaps no technology is more transformative than artificial intelligence (AI), which is already being deployed to complete highly complex tasks in a fraction of the time that humans can. According to one estimate, up to 70% of companies worldwide will have integrated some form of AI into their businesses by 2030, adding $13 trillion in additional output to the global economy.

There will be no shortage of opportunities in the sector over the next decade, but these two stocks might be a great place to start given they're trading at hefty discounts to their all-time highs amid the broader tech sell-off.

C3ai (AI 5.18%) is a first-of-its-kind enterprise AI company. Its stock is volatile because the company isn't profitable yet, and its revenue growth has underperformed expectations since it was listed on the public markets in December 2020. But that's often part and parcel of breaking ground in a brand-new industry.

C3.ai is a good place to start for investors who want exposure to the artificial intelligence sector because it builds both ready-made and customizable AI applications for 11 different industries. For most of its customers, C3.ai is the one-stop source for their AI needs, and it's possible they wouldn't otherwise have access to the technology.

The oil and gas industry is C3.ai's largest contributor to revenue, making up 54% of its $252 million in fiscal 2022, which ended on April 30. The company's technology helps oil behemoths like Shelltrack thousands of pieces of equipment to predict potentially catastrophic failures, saving time, money, and negative environmental impacts. C3.ai has an entire suite of applications just for the fossil fuel sector, which also helps those companies manage carbon emissions to run cleaner businesses.

But C3.ai has also drawn recognition from the largest tech companies in the world. It has partnerships with both Microsoftand Alphabet'sGoogle to collaboratively develop AI applications to better serve their customers using cloud computing technology.

C3.ai's stock price is down 88% from its all-time high, so it carries inherent risks. The company lost $192 million in fiscal 2022 (which ended April 30), but importantly, it has $959 million in cash, equivalents, and short-term investments on its balance sheet -- which means it can run at that loss rate for the next five years before it needs more money. C3.ai has a high gross profit margin of 81%, so once it achieves scale, it can cut back its operating costs to generate positive earnings. But the key question is how long it will take to get there, if at all. With new businesses in new industries, it's always an unknown.

But C3.ai estimates its AI software opportunity could be worth $596 billion by 2025. Since the company's market value is only $2 billion now, it might be worth a small bet for investors with some risk appetite.

Upstart Holdings (UPST 9.54%) offers a great example of how artificial intelligence is being used to improve decades-old processes. Its AI-powered algorithm is designed to replace Fair Isaac's FICO credit scoring system, which is the traditional means of assessing a potential borrower's creditworthiness. Upstart can analyze as many as 1,600 data points about an applicant to deliver a loan decision almost instantly 74% of the time, a feat that might take human assessors days or even weeks to determine.

Fifty-seven banks and credit unions have signed on to use Upstart's algorithm, and one bank, in particular, has abandoned FICO scores altogether in its favor. This is key because Upstart isn't a lender; it originates loans for its bank partners in exchange for a fee. But the company was forced to deviate from this strategy slightly in the recent first quarter of 2022 amid turbulent credit market conditions. Upstart absorbed $345 million worth of new loans using its own balance sheet, which spooked investors.

This added to the $252 million worth of loans it already held mostly for research and development purposes. Management says the increase is only temporary, and it's important to note the $345 million jump represented just 7% of the total $4.5 billion in originations during the quarter.

It's partly a symptom of Upstart's rapid growth, which is bolstered by its entry into the automotive loan origination space. Since launching its car sales and origination software in 2021 called Upstart Auto Retail, 35 car makers have adopted the platform across 525 dealerships. That's up 224% from 162 dealers in Q1 last year.

Upstart generated $849 million in revenue during 2021, a whopping 264% year-over-year jump. It thinks revenue could top $1.25 billion this year, and while that's a slowdown in growth, consumers are contending with higher interest rates and tougher economic conditions, which could dampen demand for credit.

But the company continues to expand into what it estimates is a $6 trillion addressable opportunity. With its stock price down 90% from its all-time high, it might be a great chance to make a long-term bet on what could be the future of credit assessments.

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2 Artificial Intelligence Growth Stocks to Buy on the Dip - The Motley Fool

To many they are arts next big thing digital images of jellyfish pulsing and blurring in a dark pink sea, or dozens of butterflies fusing together into a single organism.

The Argentine artist Sofia Crespo, who created the works with the help of artificial intelligence, is part of the generative art movement, where humans create rules for computers which then use algorithms to generate new forms, ideas and patterns.

The field has begun to attract huge interest among art collectors and even bigger price tags at auction.

US artist and programmer Robbie Barrat a prodigy still only 22 years old sold a work called Nude Portrait#7Frame#64 at Sothebys in March for 630,000 ($821,000).

That came almost four years after French collective Obvious sold a work at Christies titled Edmond de Belamy largely based on Barrats code for $432,500.

Collector Jason Bailey told AFP that generative art was like a ballet between humans and machines.But the nascent scene could already be on the verge of a major shake-up, as tech companies begin to release AI tools that can whip up photo-realistic images in seconds.

Artists in Germany and the United States blazed a trail in computer-generated art during the 1960s.

The V&A museum in London keeps a collection going back more than half a century, one of the key works being a 1968 piece by German artist Georg Nees called Plastik 1.

Nees used a random number generator to create a geometric design for his sculpture.

Nowadays, digital artists work with supercomputers and systems known as Generative Adversarial Networks (GANs) to create images far more complex than anything Nees could have dreamed of.

GANs are sets of competing AIs - one generates an image from the instructions it is given, the other acts as a gatekeeper, judging whether the output is accurate.

If it finds fault, it sends the image back for tweaks and the first AI gets back to work for a second try to beat the gamekeeper.But artists like Crespo and Barrat insist that the artist is still central to the process, even if their working methods are not traditional.

When Im working this way, Im not creating an image. Im creating a system that can create images, Barrat told AFP.

Crespo said she thought her AI machine would be a true collaborator, but in reality it is incredibly tough to get even a single line of code to generate satisfactory results.

She said it was more like babysitting the machine. Tech companies are now hoping to bring a slice of this rarefied action to regular consumers.

Google and Open AI are both touting the merits of new tools they say bring photorealism and creativity without the need for coding skills.

They have replaced GANs with more user-friendly AI models called transformers that are adept at converting everyday speech into images.

Google Imagens webpage is filled with absurdist images generated by instructions such as: A small cactus wearing a straw hat and neon sunglasses in the Sahara desert.

Open AI boasts that its Dalle-2 tool can offer any scenario in any artistic style from the Flemish masters to Andy Warhol.

Although the arrival of AI has led to fears of humans being replaced by machines in fields from customer care to journalism, artists see the developments more as an opportunity than a threat.

Crespo has tried out Dalle-2 and said it was a new level in terms of image generation in general though she prefers her GANs. I very often dont need a model that is very accurate to generate my work, as I like very much when things look indeterminate and not easily recognizable, she said.

Camille Lenglois of Pariss Pompidou Centre Europes largest collection of contemporary art also played down any idea that artists were about to be replaced by machines.

She told AFP that machines did not yet have the critical and innovative capacity, adding: The ability to generate realistic images does not make one an artist.

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Is Artificial Intelligence the future of art? : - The Tico Times

By using automation, how is artificial intelligence benefiting the Fintech industry in 2022?

Right present, the fintech business is undergoing a considerable transformation. Customers are benefiting from the disruption by having more accessible access to credit, which has made payments and transactions more accessible than ever before. All of this is feasible because of technological advancements such as open banking and the rise of AI and Machine Learning.

Young India is credit-hungry, and its per capita expenditure has been steadily increasing. Customers used to have to go to the bank location, physically produce the appropriate paperwork, and wait at least 15 days for credit or a loan until recently. Banks used to take a long time to process documents, conduct KYC through human visits, assess credit risk, and finally authorize loans. Banks and lenders, on the other hand, may now lend money in a matter of hours rather than days. This has made the entire loan cycle shorter and more accessible to the average person.

The Fintech sector has undergone a complete transformation because of digitalization, open APIs, and machine learning integration. Lenders may process loan applications, conduct e-KYCs, and credit assessments, assess creditworthiness, and process loan amounts in only a few minutes. This has opened up a lot of options for people looking for financing. Every month, millions of customers apply for a loan, but only 10 to 15% of them are successful in completing the application procedure, and only 2 to 5% of those who apply are approved.

Both pre-processing and post-processing steps are affected by loan dropout. Filling up the application, receiving an offer, presenting KYC papers, providing account statements, income tax returns, and so on are all examples of pre-processing phases. Credit evaluation, credit determination, and loan distribution are the steps of post-processing. Several causes contribute to loan dropout at various stages: the client does not complete the application or is unable to supply the required papers, does not meet the risk score requirements, is price sensitive, and so on.

The loss of consumers along the loan application journey has grown costly for digital lending organizations as customer acquisition expenses have risen, resulting in a significant loan drop at every stage. This is where AI-driven intelligent automation technologies are assisting financial institutions in not only automating the entire process but also drastically lowering their costs and even helping customers in making educated decisions during their loan application journey.

Furthermore, it is a time-consuming procedure for lending organizations to complete all of their research while relying on the expertise of credit risk managers, credit policymakers, legal resources, and an entire team to analyze customer paperwork and still fail. Given the large number of applicants in this digital era, its hard to explore all the papers, assess the risk, determine credit worthiness, and make the best judgments possible while minimizing risk.

To address this problematic issue, AI and machine learning-based intelligent automation systems have been created and implemented to handle massive amounts of data, categorize anomalies, evaluate payment behaviour and patterns, assess credit worthiness, and automate risk choices. AI is enabling credit risk managers to gain a scientific understanding of each customers identity and risk behaviour, as well as give credit risk causation. AI is assisting lenders in predicting client loan dropout probabilities, which may aid in screening out qualified candidates, allowing the funnel to be optimized and ultimately lead to quality consumers being targeted and the entire application process being improved.

Following the completion of loan applications, the overarching AI model aids in predicting which customers are most likely to have their loan approved and establishing a pattern for suitable applications. This enables lenders to identify high-quality clients ahead of time and focus their efforts entirely on helping them boost conversion rates and reduce loan default rates.

Client acquisition costs are also significantly reduced as a result of this. The lender might also employ AI-powered intelligent automation to predict which customers are most likely to abandon their digital loan application at critical phases like avail offer, KYC, and document submission. The potent mix of AI and automation produces a one-of-a-kind customer service approach that also helps to prevent loan default. Using this data, the lender may now optimize targeted consumer advertising and call centre activity.

Adopting digital technology such as artificial intelligence in loan application administration can help banks reorganize the customer journey, increase efficiency, and free up people to provide value-added services.

Edited by Prakriti Arora

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By Using Automation, How Is Artificial Intelligence Benefiting The Fintech Industry In 2022? - Inventiva

Self-driving cars are nifty. Electric vehicles are cool. And when you think of self-driving electric cars, its hard not to think of Tesla. That said, not everyone associates them with safety. And with how the AIs algorithmic thinking is looking, they may have good reason.

On Thursday, the National Highway Traffic Safety Administration, an agency under the guidance of Transportation Secretary Pete Buttigieg, said it would be expanding a probe and look into830,000 Tesla carsacross all four current model lines, 11% more vehicles than they were previously examining.

Initially the probe started last year in response to Tesla vehicles mysteriously plowing into the scene of an existing accident where first responders were already present.

On Thursday, NHTSA said it had discovered in 16 separate instances when this occurred that Autopilot aborted vehicle control less than one second prior to the first impact, suggesting the driver was not prepared to assume full control over the vehicle.

CEO Elon Musk hasoften claimedthat accidents cannot be the fault of the company, as data it extracted invariably showed Autopilot was not active in the moment of the collision.

At least 26 crashes and 11 deaths appear to involve Teslas autopilot feature. While it is true that drivers should have their hands at 10 and 2 with their eyes on the road, youve gotta admit that there have been some representations of the autopilot feature as a replacement for human inputs. A last-minute shift from AI to UI is exactly the type of childish loopholing masquerading as brilliance youd expect from a guy with an Elden Ring build this bad.

Look, I know Ive made that gag in a prior article where I dunked on Musk for being goofy, BUT TWO MEDIUM SHIELDS?

For fear of being labeled a one-trick Tesla with weak windows this is exactly what youd expect from a guy who was already on trial for killing someone with a car.

Whats next? A special re-issue of O.J. Simpsons If I Did It with an additional chapter from Elon on how hed use tweets to manipulate stock prices?

Cartoonish evil gets satirical responses. In the meantime, it may be worth it to consider electric car alternatives that arent Teslas. And pay attention to the road, damn it.

Elon Musks Regulatory Woes Mount As U.S. Moves Closer To Recalling Teslas Self-Driving Software [Fortune]

Chris Williams became a social media manager and assistant editor for Above the Law in June 2021. Prior to joining the staff, he moonlighted as a minor Memelord in the Facebook groupLaw School Memes for Edgy T14s. He endured Missouri long enough to graduate from Washington University in St. Louis School of Law. He is a former boatbuilder who cannot swim,a published author on critical race theory, philosophy, and humor, and has a love for cycling that occasionally annoys his peers. You can reach him by email atcwilliams@abovethelaw.comand by tweet at@WritesForRent.

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Where Does Legal Accountability Rest Between Tesla's Artificial Intelligence and Human Error? - Above the Law

image:Anant Madabhushi view more

Credit: CWRU

CLEVELANDCollaboration between pharmaceutical companies and the Center for Computational Imaging and Personalized Diagnostics (CCIPD) at Case Western Reserve University has led to the development of artificial intelligence (AI) tools to benefit patients with non-small cell lung cancer (NSCLC) based on an analysis of routine tissue biopsy images, according to new research.

This year, more than 236,000 adults in the United States will be diagnosed with lung cancerabout 82% of them with non-small cell lung cancer, according to the American Society of Clinical Oncology.

Researchers at the CCIPD used AI to identify biomarkers from biopsy images for patients with NSCLC, as well as gynecologic cancers, that help predict the response to immunotherapy and clinical outcomes, including survival.

We have shown that the spatial interplay of features relating to the cancer nuclei and tumor-infiltrating lymphocytes drives a signal that allows us to identify which patients are going to respond to immunotherapy and which ones will not, said Anant Madabhushi, CCIPD director and Donnell Institute Professor of Biomedical Engineering at Case Western Reserve.

The study was published this month in the journal Science Advances.

Immunotherapy is expensive, and studies show that only 20-30% of patients respond to the treatment, according to National Institutes of Health and other sources. These findings validate that the AI technologies developed by the CCIPD can help clinicians determine how best to treat patients with NSCLC and gynecologic cancers, including cervical, endometrial and ovarian cancer, Madabhushi said.

The study, drawn from a retrospective analysis of data, also revealed new biomarker information regarding a protein called PD-L1 that helps prevent immune cells from attacking non-harmful cells in the body.

Patients with high PD-L1 often receive immunotherapy as part of their treatment for NSCLC, while patients with low PD-L1 are often not offered immunotherapy, or its coupled with chemotherapy.

Our work has identified a subset of patients with low PD-L1 who respond very well to immunotherapy and may not require immunotherapy plus chemotherapy, Madabhushi said. This could potentially help these patients avoid the toxicity associated with chemotherapy while also having a favorable response to immunotherapy.

The multi-site, multi-institutional study examined three common immunotherapy drugs (called checkpoint inhibitor agents) that target PD-L1: atezolizumab, nivolumab and pembrolizumab. The AI tools consistently predicted the response and clinical outcomes for all three immunotherapies.

The study is part of broader research conducted at CCIPD to develop and apply novel AI and machine-learning approaches to diagnose and predict the therapy response for various diseases and cancers, including breast, prostate, head and neck, brain, colorectal, gynecologic and skin.

The study coincides with Case Western Reserve recently signing a license agreement with Picture Health to commercialize AI tools to benefit patients with NSCLC and other cancers.

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Case Western Reserve University is one of the country's leading private research institutions. Located in Cleveland, we offer a unique combination of forward-thinking educational opportunities in an inspiring cultural setting. Our leading-edge faculty engage in teaching and research in a collaborative, hands-on environment. Our nationally recognized programs include arts and sciences, dental medicine, engineering, law, management, medicine, nursing and social work. About 5,800 undergraduate and 6,300 graduate students comprise our student body. Visitcase.eduto see how Case Western Reserve thinks beyond the possible.

Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors

1-Jun-2022

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Artificial intelligence tool predicts response to immunotherapy in lung and gynecologic cancer patients - EurekAlert