2023年5月31日

首先,正如预期,炒作机生成AI现在活着,在医疗保健。从环境的人工智能应用程序用例集中在总结医疗笔记和其他文档从临床/行政环境,感觉就像每个人都有某种形式的chatGPT提供。史诗和细微差别的上下文中HIMSS中心阶段,他们当然有一些令人印象深刻的演示。但我再说一遍,这是演示(幻灯片演示?)和陪审团仍然是他们如何执行在现实世界中坦率地说,他们甚至准备好了去住吗?讨论了一些用例包括嵌入GPT4史诗的医患信息和SlicerDicer分析工具。工具解决临床医生是至关重要的,以解决行政负载倦怠,但想知道负责任的AI协议前已落实到位的滚出来。我们正在做大量的研究在这一领域,好奇多深的承诺负责任的人工智能是在实践中。OpenAI已经取得了一些有问题的隐私曝光,它回避了问题的实质是一个适当的资产是否考虑使用在医疗环境中。这些模型的性能指标随时间和错误率将重要的看——地区,我们接到的演示HIMSS小见解。而开放艾未未的兴趣是在这个空间军备竞赛,我的钱在有意向的供应商更周到地介绍产品。 Some may even choose not to enter the market until a later time or will perhaps work with smaller large language models (LLMs) that pay much more attention to the data they curate, privacy/security and data governance as well controls for managing misinformation/disinformation that the models produce. Ricoh Document Scanner was a nifty example of how the use of optical scanning of faxes with a generative AI textual summary service on top could play a role in smaller medical practices. Note: I’m not using the term “hallucination” because AI models are not sentient beings and cannot hallucinate. The anthropomorphizing of AI needs to stop. They lack agentic capacity and we need to not buy into the hype if we are going to engage with the technology in a fruitful, ethical manner. In the lead up to HIMSS I wrote a bit about some of these concerns on our blog. Health equity: marketing slogan or real, material ‘thing’? Earlier in the month, ViVE made a big splash around their “techquity” coalition . Given that a significant portion of my career has revolved around fairness and ethical development in technology, I’ve learned firsthand that one always needs to be skeptical of these pronouncements of tech solving broader social problems through slogans and PR campaigns. It is one thing to make pronouncements at conferences; it’s almost a sure thing that we’ll return a year later with little accomplished unless there are real incentives to change business practices. The issue of technological solutionism has been discussed and critiqued for over a decade now and should inform how we go about addressing health equity in society and its relationship with technology. I spoke to a number of vendors who seemed to agree that most of the talk about health equity in the health IT space seems to be more marketing and aspirational, rather than involving clearly defined programs and technologies applied with demonstrated results. I tend to agree; I think more care needs to be given to how we strategize around social determinants of health and the health policies/politics that can contribute to a healthier population. That said, there are some areas where we’ve seen the appropriate application of AI around social determinants and health equity issues that hold promise for making an impact. Often the key challenges for a tech company is finding a business model that can sustain the company long enough to create lasting change. Doing good is hard to do in practice given the incentives that drive company engagement with these issues. Jvion , acquired last year and now pretty solidly integrated into Lightbeam Health Solution’s offerings, will be one to watch. During the first year of COVID, Jvion did some impressive vulnerability mapping of neighborhoods who were at higher risk of COVID due to social determinants. This is important for any population health approach and will continue to add value in the post-COVID context. I hope to have some follow on calls with companies such as Health Catalyst with whom I met and who were excited about some data they have on real impact in this space. This is what we need more of – real world data on actual impact on health equity and social determinants-related outcomes. 106 Crooked Trail