As hospitals across the nation adopt more and more AI technology, there are some common hazards of which they should be wary. During a conference presentation on Tuesday, healthcare AI expert Suchi Saria laid out seven AI pitfalls hospitals should look out for — some of these include being duped by Big Tech companies’ marketing strategies and focusing too much on administrative use cases.

U.K. doctor David Watkins has been a public critic of Babylon Health and its AI technology for years, during which has received anonymous emails from Babylon employees who shared his concerns but feared to speak out publicly. After Babylon collapsed, a former employee publicly declared that the AI engine was indeed faulty. As a longtime skeptic of Babylon’s claims surrounding its technology, Watkins said that Ali Parsa, the company’s CEO, shouldn’t be treated any differently to Elizabeth Holmes.

Over the past year, Meditech has deepened its relationship with Google Cloud by exploring ways to embed Google’s generative AI into its EHR. Some of the use cases the EHR vendor is exploring include an enhanced search and summarization tool that would present clinicians with a longitudinal view of their patient and a tool that would auto-generate clinical documentation for the hospital course summary.

The President’s Council of Advisors on Science and Technology (PCAST) issued a report that laid out four recommendations to improve patient safety across the nation. Some of these included creating federal leadership positions focused on advancing patient safety and hastening research on systems of safe care.

Pitango Venture Capital, one of the largest venture capital firms in Israel, announced the first closing of a new $175 million healthcare technology fund. With the new fund, the firm plans to build a portfolio of approximately 15 new companies over a period of three to four years.

A new report predicted that generative AI tools will increasingly streamline many aspects of a clinician’s day in the next five to 10 years — and that this is particularly true for tools that can automate diagnoses and respond to patients’ questions.

Regard — a startup selling an AI co-pilot that helps clinicians diagnose medical conditions — announced a new partnership to add to its technology’s capabilities. The company is teaming up with OpenAI to release new chatbot functionalities built on OpenAI’s large language model GPT-4.

Innovaccer announced a new suite of healthcare products. It comprises four different solutions — one for answering healthcare executives’ questions about their business metrics, one for automating care planning and documentation, one for generating clinical visit summaries, and one for streamlining workflows at contact centers.

The healthcare AI space will be an exciting one to watch over the next couple years, as investment dollars flow to startups and providers launch more AI pilots. Two of the most interesting trends to watch will be the use cases that healthcare organizations prioritize when deploying generative AI models, as well as M&A activity within the healthcare AI field, a CB Insights analyst declared during a recent webinar.

Executives from Geisinger and UNC Health discussed the most impactful ways they have deployed predictive AI across their health systems during a recent virtual panel. At Geisinger, these predictive algorithms are reducing avoidable emergency department admissions, and at UNC, they are helping to identify sepsis before it becomes severe.

NYU researchers conducted a study this year in which nearly 400 people were asked to identify whether responses to patient questions were produced by human providers or ChatGPT. Participants had a limited ability to tell the source of the responses apart, so the study authors concluded that the use of LLMs like ChatGPT could be an effective way to streamline healthcare providers’ communication with patients.

Sonio recently gained $14 million in Series A funding. The Paris-based startup’s AI platform automates ultrasound reporting so that healthcare providers can conduct ultrasound exams with greater accuracy, speed and efficiency.

There are three key areas of the healthcare sector where generative AI is booming the most — in terms of both venture capital funding and the development of innovative technology — according to Anjalika Komatireddy, an analyst at CB Insights. During a recent webinar, she discussed the generative AI activity occurring in these three areas: care delivery and navigation, digital therapeutics and drug discovery.

Researchers at UPMC and the University of Pittsburgh created and deployed a machine learning algorithm that flags patients who are at high risk for postsurgical complications. The research team recently published research validating the tool in JAMA Network Open — the study found that the health system’s model outperforms the industry standard.

Butterfly Network recently launched an AI tool that can provide medical practitioners with an accurate B-line count from just a six-second lung ultrasound clip. By automatically counting B-lines in a lung image, the tool helps doctors quickly identify and assess a patient’s lung issues at the point of care.

UNC Health recently launched a pilot for its in-house generative AI app called Ava, which stands for “AI virtual assistant.” The app allows staff members to ask questions about the different digital health tools available to them across the health system, and it gives them concise answers about how to use the tools effectively. The goal is to prevent staff from searching through vast training libraries.

Suki recently announced one of its biggest partnerships to date: it is integrating its voice assistant into Epic’s EHR. The startup’s assistant can be thought of as like Apple’s Siri or Amazon’s Alexa — but for doctors.

There is a huge opportunity for PBMs and health systems to use AI to help address the challenges surrounding the affordability and accessibility of medicines, from navigating formularies to resolving prior authorization. More automation in pharmacies and with ordering workflows is one piece of that puzzle.

By investing in the value chain of IVF with the right AI solutions, investors can solve the access problem growing in the IVF space, help more people build their families, and achieve significant ROI.

Just after exiting stealth with $62.5 million in Series A and B financing, Sirona Medical plans to leverage Nines’ AI technologies to provide a comprehensive digital solution for radiologists.

Today, we see too many doctors working with their blinders on, either too busy to learn what is happening to independent healthcare or unaware that AI can reinvigorate their practice.

Enhanced mental health care that integrates human and artificial intelligence is in its infancy. While there are significant challenges to address, the potential benefits are enormous for all stakeholders: patients, therapists, providers and payers. 

Which is more important when working with healthcare AI detection: that it never misses something on a patient scan, or that it never identifies something that isn’t there?

Without the combination of RPM and AI, it would be exceedingly challenging for a care team to identify a chronic disease patient’s increase in risk and accurately anticipate potential complications. 

Federated learning is the right solution for healthcare AI. It allows connecting data from different data silos while not requiring any movement of patient data. No huge files to transfer and store. And, most importantly, no risk to patient privacy.

We’re only just beginning to learn how to harness the power of AI in ways that cultivate stronger healthcare systems — and ensure that the value of connection akin to the human condition is not lost but rather nurtured in its use among healthcare providers.

When we talk about artificial intelligence, the truth is most physicians aren’t using it.What we need now is for developers and tech executives to stop designing theoretical solutions, and instead place physicians, patients, and specific clinical needs at the center of their solutions.

Ultimately, we should be excited about AI and its adoption in biopharma, but we should also stay sensible and focus our efforts where they’ll have the greatest impact.

Opportunities are set to appear at the intersection of technology, consumerism, and markets for those willing to move past their traditional limitations

By helping patients find affordable healthcare, AI will promote stronger financial health for providers and patients alike, and improve the quality of care as patients gain the financial support to engage in more preventative healthcare.