Literature Review

Can a chatbot be a medical surrogate? The use of large language models in medical ethics decision-makingNEJM-AI; Isha Harshe, B.S., B.A.; Kenneth W. Goodman, Ph.D.; Gauri Agarwal, M.D.; 6/25 The use of AI in health care has raised numerous ethical challenges. Issues concerning data privacy, accountability, bias perpetuation, and the identification of appropriate uses and users have prompted scholars and scientists to tackle these challenges. Our findings indicate that while AI systems may assist in identifying considerations and guidelines for ethical decision-making, they do not consistently demonstrate the flexibility of thought that humans exhibit when addressing novel ethical cases. AI can support ethical decision-making, but it is not currently capable of showing autonomous ethical reasoning for consultation regarding patient care.

Giving full consideration to medical technology during health system mergers and acquisitions Healthcare Business Today; by Nancy Wright; 6/17/25 ... There’s a unique balance to strike in the early stages of an acquisition. On one hand, quality care must be a priority as clinicians continue to treat patients, but of course, the strategic vision should focus on the long-term path that the health system is on. In light of the population around them, their goals as a provider, and their current assets and offers, the new health system needs to take a long, hard look and ask, “What is it we want to become?” A big part of the answer to that question lies in accurately assessing the existing system—highlighting the current staff, technology, equipment, and financial performance—and how it can be leveraged strategically to move towards an ideal future. ...

Joint Commission to create guidelines on AI’s use in healthcare Becker's Clinical Leadership; by Eria Carbajal; 6/11/25 The Joint Commission has partnered with the Coalition for Health AI to establish and implement evidence-based guidance on the use of artificial intelligence in healthcare operations. The first set of guidelines and best practices is slated to be released in the fall, according to a June 10 news release shared with Becker’s. The organizations are also developing an AI certification program, which will be available after the initial guidance is published. The guidance will include a “suite of AI playbooks” and tools to support health systems with safe and ethical adoption of AI. The guidance is expected to be used by more than 80% of U.S. healthcare organizations, according to the release. 

5 real world lessons from using AI to transform legacy software Forbes - Innovation; by Muamer Cisija; 6/10/25 AI is not the future—it is already shaping our present. According to a recent Forbes survey, AI and machine learning represent the top tech spending area in 2025, capturing 42% of IT budgets. Moreover, an NTT survey conducted by WSJ Intelligence shows that 89% of global CEOs consider AI crucial for future profitability. ... Drawing from real-world use cases I've seen across sectors, here are five critical lessons for applying AI to transform legacy software and unlock growth.

Medical robots to the rescue: New technologies to help our healthNIH News in Health; 6/2/25What do you think of when you hear the word robot? Is it a human-like assistant with a friendly face, or a large and menacing foe? Generally, a robot is a machine that that’s been built to perform specific tasks. [This article highlights:]

Empathetic AI policy example: A framework for the human impact on AI Solutions Review; by Tim King; 5/30/25 ... Empathetic AI is our organizational pledge to place people at the center of our AI strategy. It means prioritizing the dignity of work, the stability of our workforce, and the fair treatment of all individuals impacted by automated systems. It means actively supporting those whose roles may be transformed or displaced and investing in their future through retraining, redeployment, and transparent communication. Our core principles are as follows:

Empathy meets efficiency: Voice AI in law, healthcare and debt collection Forbes; by Kumar Abhirup; 5/29/25 When a grieving daughter calls a probate firm at midnight or a worried parent rings a pediatric clinic after hours, the first voice they meet decides whether they stay or stray. As organizations adopt AI, especially voice and conversational AI, that voice is increasingly synthetic. A recent survey from the American Bar Association found that the share of firms using AI tools rose from 11% in 2023 to 30% in early 2025. Healthcare has sprinted even faster: The American Medical Association found 66% of physicians now use some form of AI, up from 37% two years earlier. Collections agencies are also hopping aboard this trend ... 

Trend Report: Artificial Intelligence In Health CareHealth Affairs; 5/25/25For our first Insider trend report, we explore the ways that artificial intelligence could impact the health care landscape. The report highlights:

6 ways to successfully implement clinical communication tools Forbes; by Judit Sharon; 5/19/25 In the high-stakes environment of healthcare, communication breakdowns can have life-or-death consequences. Yet too often, hospitals and healthcare systems assume that simply deploying new clinical communication and collaboration (CC&C) tools will solve the problem. The reality is more complex: Successful implementation depends just as much on operational alignment, cultural change and thoughtful execution as it does on the technology itself. To turn communication tools into real transformation, here are six ways to drive successful CC&C adoption.

How to put together your AI Dream Team Forbes; by Megan Poinski; 5/15/25 What are front-runners [with AI] doing differently? Accenture found they have five imperatives in common. They’re prioritizing AI innovation and growth over cost reduction—with C-suite leaders agreeing on the reasons behind it and how to measure the outcomes. They’re reinventing talent and ways of working, hiring employees who can do more with AI and upskilling existing staff, and giving them more opportunities to learn on the job by experimenting with it. They’re building an AI-enabled digital core, ensuring that their core systems and data are all designed with current and future AI use in mind. They’re using AI responsibly, focusing on governance for its use and risk assessments for both security issues and impacts on the workforce. And they’re also continuously reinventing their strategies and goals, creating a change-management framework that can shift with the way tools are employed, actual results, and changes in technology capabilities and the larger business environment.  

Doctors told him he was going to die. Then A.I. saved his life.New York Times; by Kate Morgan; 3/20/25Scientists are using machine learning to find new treatments among thousands of old medicines... The handful of success stories so far have led researchers to ask the question: How many other cures are hiding in plain sight?... The first time Dr. Fajgenbaum [University of Pennsylvania] repurposed a drug, it was in an attempt to save his own life... “Essentially we ran a query that said, ‘Show us every proposed treatment there has ever been in the history of medicine for nausea,’” said Matt Might, a professor at University of Alabama at Birmingham...Publisher's note: While slightly dated, this is an interesting application of AI in medicine and provoked me to consider "what are other ways AI could be used in hospice?" Accessing this article may require a subscription.

AI tool uses face photos to estimate biological age and predict cancer outcomes Mass General Brigham - Technology & Innovation - Research; by Hugo Alerts, Ray Mak, Dennis Bontempi, Osbert Zalay, Danielle S. Bitterman, Fridolin Haugg, Jack M. Qian, Hannah Roberts, Subha Perni, Vasco Prudente, Suraj Pai, Christian Guthier, Tracy Balboni, Laura Warren, Monica Krishan, and Benjamin H. Kann; 5/8/25 Mass General Brigham findings suggest FaceAge tool could provide objective data to help inform treatment decisions in cancer care and other chronic diseases. Eyes may be the window to the soul, but a person’s biological age could be reflected in their facial characteristics. Investigators from Mass General Brigham developed a deep learning algorithm called FaceAge that uses a photo of a person’s face to predict biological age and survival outcomes for patients with cancer. They found that patients with cancer, on average, had a higher FaceAge than those without and appeared about five years older than their chronological age. Older FaceAge predictions were associated with worse overall survival outcomes across multiple cancer types. They also found that FaceAge outperformed clinicians in predicting short-term life expectancies of patients receiving palliative radiotherapy. Their results are published in The Lancet Digital Health.Editor's note: This summary article is published by the main source, Mass General Brigham. Various articles are being written about this new use of AI.

Hospice AIA Hospice & Palliative Care Today compilation; 5/5/25We've seen several recent articles discussing the benefits of additional AI integration into hospice services.