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Good morning! 

Welcome to Healthcare AI News, your weekly dose of the latest developments and headlines in the world of Healthcare AI.

In this issue, we explore:

✅ Headlines: A blueprint for AI in Psychotherapy

✅ Industry: Clinicians don't believe AI is ready for healthcare usage

✅ Feature: Predicting the Future to Prevent Diseases with AI

✅ Tech:  5 C-suite bridges every IT leader must build

✅ Deal Flow: Lilly to acquire DICE Therapeutics for $2.4 billion

🚨 Announcement: We are thrilled to share that moving forward, we will be featuring industry leaders as guest contributors, providing their valuable insights on AI in healthcare. This exciting initiative begins with the current edition! Take a look at the feature.

Let's dive in.

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HEADLINE ROUNDUP

• Johns Hopkins grants aim to support AI solutions for older patients (Read More)

• Emerging tech like AI, is poised to make healthcare more accurate, accessible, and sustainable (Read More)

• This AI startup wants to be the next Nvidia by building brain cell-powered computers (Read More)

• A blueprint for using AI in Psychotherapy (Read More)

• How healthcare can pioneer industry-specific AI for privacy and optimization (Read More)

• Tele-robot with a ‘sense of touch’ gives clinicians the ability to ‘feel’ patients remotely (Read More)

• Could switching off a neural “death response” slow aging? (Read More)

💡 Keep reading to catch up on Industry, Tech & Deal flow

TOGETHER WITH FEMHEALTH INSIGHTS

June 2023 marks the 30th anniversary of the NIH Revitalization Act of 1993, mandating the inclusion of women in clinical trials. This pivotal moment marked the beginning of a long journey toward addressing the substantial lack of female-specific clinical data in research. It also ushered in a new era of healthcare focused on improving women’s health by developing sex-specific diagnostic and treatment paradigms.

Join FemHealth Insights for a 1-day virtual event to celebrate this pivotal milestone, discuss why continued advancement in research and innovation is crucial, and learn about the important role the government can play in continuing to improve female health and wellness.

Use Promo code "AINEWS" for free registration.

INDUSTRY NEWS

• Senators concerned Amazon health platform harvesting consumer health data from patients (Read More)

• Over half of clinicians don't believe AI is ready for healthcare usage (Read More)

• Why patients might choose Walmart Health over other retail clinics (Read More)

• ChatGPT provides evidence-based answers to public health questions (Read More)

• Unequal treatment: How health plans are confronting racial and ethnic disparities (Read More)

• Physicians call for more regulation of health insurers using AI (Read More)

• Artificial intelligence is a ‘moment of revolution’ Sen. Schumer says in urging swift action on regulations (Read More)

• AI-Based “Denoising” in medical imaging may require further evaluation (Read More)

THE FEATURE

Predicting the Future to Prevent Disease: Study Finds AI Algorithms Accurately Predict 5-Year Risk of Breast Cancer

Guest Authored by: Dr. Brittany Barreto
Women’s Health Innovation & FemTech Expert, Geneticist, Investor, Speaker

If breast cancer is identified in stage I, the patient has a 98 to 100 percent chance of survival. Those are the same odds as not getting pregnant while taking oral contraceptives as prescribed. And yet, approximately 42,500 lives are lost each year in the United States to breast cancer. Could AI help prevent these deaths by predicting the 5-year risk of breast cancer?

A recent study published in Radiology shows that five AI-based algorithms are capable of predicting the 5-year risk of breast cancer based on the mammogram and clinical factors of the patient. This is the first time AI has predicted the long-term risk of cancer and could be a critical tool in reducing breast cancer deaths. The predictions were statistically significantly more accurate than the Breast Cancer Surveillance Consortium (BCSC) risk model, which is the tool relied on by healthcare professionals to calculate a female patient’s risk of breast cancer within the next five years.

Researchers performed a retrospective case-cohort study of 13,628 patients who were negative for breast cancer in 2016 and of which 193 were positively diagnosed by 2021. They found that the AI algorithms had significantly higher discrimination of breast cancer risk with an AUC of 0.09 for interval cancer risk and 0.06 for overall 5-year risk. This means that AI can predict the future risk of cancer for up to 5 years when no cancer is clinically detected in mammography. The results showed that AI predicted up to 28% of cancers versus 21% with BCSC. These are substantial and clinically meaningful improvements in prediction.

AI is identifying breast tissue features that we may not yet understand or be able to see and use to predict future cancer development. AI sees things we do not do on the mammogram and is finding complex patterns and relationships that we have not yet discovered.

The authors conclude that radiologists should combine their standard risk assessment tools, such as BCSC, with predictive AI to improve risk prediction for patients. These predictions will change the way that the patient is monitored. A high-risk score for interval cancer may lead to a recommendation for a second reading of mammograms, supplementary screening like MRI, or short-interval follow-ups.

We can learn a lot from these algorithms, like what features they are identifying that indicate a risk of cancer. Could this change how we image the breast? The researchers did not have enough data to statistically validate this finding, but the data suggested prediction accuracy fluctuate based on the manufacturer of the mammography machine, Hologic vs. GE Healthcare.

Predictive AI algorithms could be extremely beneficial for treatment planning as well. We may be able to determine if a certain type of chemotherapy or combination with surgery would be the best treatment plan for a specific patient. The suggested treatment would be based on patient data that physicians may not have thought was relevant and features in medical images we may not have seen with the naked eye. This is an excellent example of how AI can optimize healthcare screening, diagnosis, and treatment in a way that will improve patient outcomes.

Unfortunately, the eradication of late-stage breast cancer depends on screening and access to healthcare. Only 67% of women over 40 in the US had a mammogram in the last two years, with Asian and Hispanic women having the lowest rates. One of the biggest barriers to screening is health insurance, with only 42% of uninsured women having had a mammogram within the last two years.

Predictive AI should also be used to locate and advocate for the women that miss their mammograms. Can predictive AI identify who is most likely to miss their mammogram and why? Can it tell us which programs would be the most beneficial for specific communities, such as providing free transportation versus free childcare at the clinic? A task force for these individuals could allow for more effective programs with consistently higher rates of screening and, thereby, significantly fewer cases of breast cancer in a community.

TECH NEWS

• 5 C-suite bridges every IT leader must build (Read More)

• Why compliance in the cloud is a multi-faceted problem (Read More)

• People, skills, budget: What health system CIOs need from IT departments (Read More)

• Is AI the future? 12 DIY projects that show AI's potential (Read More)

• Databricks marketplace expands with lakehouse-native apps, AI model sharing (Read More)

• Emerging architectures for LLM applications (Read More)

• Transforming project management: The emergence of AI-powered solutions (Read More)

DEAL FLOW

• Lilly to acquire DICE Therapeutics for $2.4 billion to advance innovation in Immunology (Read More)

• Healthcare startup Aledade valued at $3.5 billion after latest funding (Read More)

• Caraway secures $16.75M series A funding to fundamentally change how Gen Z experiences Healthcare (Read More)

• Healthcare billing and claims automation startup Outbound AI raises $16M (Read More)

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