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Good afternoon! I’m Annika and I’ll be there American Society of Clinical Oncology The annual meeting will be held in Chicago in the coming days.

So will the thousands of cancer researchers and clinicians who want to see encouraging new science and data to advance cancer treatment, health equity, and patient care.

More than 5,000 research abstracts will be presented or published at ASCO, which begins Friday and runs through Tuesday. There will be data from existing drugs from pharmaceutical companies, experimental treatments in early to mid-stage testing from biotech companies, and even artificial intelligence tools.

I’m going to focus on some of the bigger brands in the industry. Here’s some data I’m looking at:

• Merck and modern Three years of data from the second phase of the study will be provided experimental vaccineIn combination with Keytruda therapy, it is used to treat patients with more severe versions of the deadliest skin cancer.
• Pfizerof Antibody-drug conjugates, or ADC: The company will present data on some of the drugs it acquired through its $43 billion acquisition of Seagen last year. These include an experimental ADC called sigvotatug vedotin, used to treat a type of lung cancer, and another approved treatment, Adcetris, used to treat a common blood cancer.
• Johnson & Johnson will provide interim and late-stage data on its targeted antibody drugs in more convenient formats, rebrevanta patient with a type of lung cancer.
• Johnson & Johnson also released early data Radiopharmaceuticals, showing signs of effectiveness in prostate cancer patients. But four trial participants died.
• Merck and partner Kelen Biologics release positive data from a Phase 3 clinical trial Antibody-drug conjugates in a type of breast cancer.

I plan to write up some data and a summary or two after the conference, so stay tuned for my report. If you see me at ASCO this weekend, please don’t hesitate to say hi!

Please feel free to send Annika any tips, suggestions, story ideas and data: annikakim.constantino@nbcuni.com.

Software provider Epic Systems is trying to make it easier for medical systems to tune out artificial intelligence noise.

The company last week released a free new toolset called the AI ​​Trust and Assurance Suite that health systems can use to evaluate the performance of AI models integrated with electronic health records (EHRs).

An EHR is an electronic version of a patient’s medical history. Epic is perhaps the best-known vendor in the field, as it holds the medical records of more than 300 million people.

Epic has released the suite as an open source tool on the popular developer platform GitHub. This means anyone in the world can use it. Corey Miller, Epic’s vice president of research and development, said the suite can help health systems evaluate the fairness, fairness and performance of artificial intelligence models and how it affects patient outcomes.

For example, a health system could use the kit to test several different models and determine which model works best for that community’s local patient population. The toolset also allows for “fairness auditing,” which can look for bias across race, gender and age in a group of patients, Miller said.

The suite is designed specifically for evaluating artificial intelligence in health care, and Miller said it will work regardless of whether a health system uses Epic’s EHR. He added that the tools were not built specifically for Epic’s artificial intelligence models.

“We saw this as an opportunity to create something that doesn’t exist today,” Miller told CNBC.

The launch of the Epic suite comes as the healthcare industry has been considering how to build guardrails and best practices around artificial intelligence. Several organizations, including the Alliance for AI in Health, Microsoft’s Trusted and Accountable AI Network, and Partners in AI for Health, were created with these goals in mind, but there are no hard and fast rules for how to use the technology.

In the meantime, Miller said he believes Epic’s suite will make it easier for health systems to understand how AI models perform in local populations, especially in rural community hospitals that may not have access to data scientists.

The kit has been in development since late last year, he said. It evaluates models against existing standards set by “healthcare systems, medical IT software developers, third-party experts, and governments,” according to a GitHub post. Miller said Epic plans to update the tool as best practices evolve.

Epic’s suite currently works with binary classification models or typical predictive models, and Miller said the next step will be generative AI models.

“We want to make this a tool that can really study the entire field of artificial intelligence,” Miller said.

Please feel free to send Ashley any tips, suggestions, story ideas, and data: ashley.capoot@nbcuni.com.