Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs), offer powerful capabilities for interpreting the complex data landscape in healthcare. In this paper, we present a comprehensive overview of the capabilities, requirements and applications of GenAI for deriving clinical insights and improving clinical efficiency. We first provide some background on the forms and sources of patient data, namely real-time Remote Patient Monitoring (RPM) streams and traditional Electronic Health Records (EHRs). The sheer volume and heterogeneity of this combined data present significant challenges to clinicians and contribute to information overload. In addition, we explore the potential of LLM-powered applications for improving clinical efficiency. These applications can enhance navigation of longitudinal patient data and provide actionable clinical decision support through natural language dialogue. We discuss the opportunities this presents for streamlining clinician workflows and personalizing care, alongside critical challenges such as data integration complexity, ensuring data quality and RPM data reliability, maintaining patient privacy, validating AI outputs for clinical safety, mitigating bias, and ensuring clinical acceptance. We believe this work represents the first summarization of GenAI techniques for managing clinician data overload due to combined RPM / EHR data complexities.

Medicaid is a federal-state program that provides healthcare to over 80 million low-income Americans, including pregnant women, children, and individuals with disabilities. Up against a host of problems, including rising healthcare costs, disparity in access, and the management of chronic conditions among at-risk groups, Medicaid is one of the biggest healthcare payers in the U.S. Just as Medicare does, the use of Artificial Intelligence (AI) offers a major opportunity to change the delivery of care and operational efficiency in Medicaid [1] [16]. While there has been extensive conversation about AI in Medicare, the unique population and requirements of Medicaid require customized AI applications [1]. Chronic disease management, improving admin tasks, and a reduction in costs are amongst the ways AI tools can help, especially by focusing on social determinants of health (SDOH) that are important for Medicaid populations. The study will assess the ability of AI-enabled systems to reinforce Medicaid in handling its particular challenges while facilitating fair and quality care for its entire population of beneficiaries [8] [9].

The rapid evolution of Artificial Intelligence (AI) has significantly transformed healthcare, particularly in the domain of Remote Patient Monitoring (RPM). This chapter explores the integration of AI in RPM, highlighting real-life applications, system architectures, and the benefits it brings to patient care and healthcare systems. Through a comprehensive analysis of current technologies, methodologies, and case studies, I present a detailed overview of how AI enhances monitoring accuracy, predictive analytics, and personalized treatment plans.

What is edge AI and what are its applications? Edge AI has been the cornerstone of many transformations in imaging systems used across industries such as agriculture, medical, retail, industrial, smart city, etc. It makes use of artificial intelligence to help automate certain tasks to improve the efficiency and performance of machines. But what is edge AI? What is the difference between AI and edge AI? Does edge AI come with certain benefits?

Remote patient monitoring is now a key application in medical spaces where cameras and AI are revolutionizing the delivery of care. This article will thus discuss how the two technologies work together to make life easier for patients and caregivers. The adoption of artificial intelligence is on the rise across all sectors. Though current AI cannot compete with the cognitive ability of the human brain, it has already started to dominate when it comes to performing mundane as well as intelligent tasks – and the medical field is not an exception to this. It has been captivating to see new and emerging applications and use cases where AI works in harmony with other technologies to enhance human experiences.

As hospital consolidation continues nationwide, rural areas are beginning to take a new shape – and it is not a pretty picture. According to a recent study from Health Affairs, newly acquired rural hospitals are eliminating surgical care services and mental health treatment access, despite a sharp rise in depression, suicide and addiction in the hard-hit rural communities. Even more stunning, these newly acquired hospitals are more likely to eliminate maternity and neonatal care than those that remain independent. Coupled with a worsening nursing shortage, this is a huge problem for rural American families. Even before the pandemic, maternal and infant outcomes in the U.S. were shockingly poor.

Americans will generate more clinical grade biological data like daily vital signs in the next 5 years than has previously been recorded in the past 20 years. The data will be more accurate since it won't be one snapshot in time, but many snapshots in someone's daily life. While most clinical grade vital signs are collected and recorded in a healthcare setting like a clinic, hospital, or ER, there are a number of factors changing that quickly. The combination of AI based software & medical devices that have cleared the FDA, payor reimbursement, clinical adoption, and patient adoption are all coming together to bring RPM mainstream. This is impactful for a number of reasons.

It's no secret that big tech companies like Amazon (AMZN), Microsoft (MSFT), and Alphabet (GOOG), the parent company of Google, are investing in digital healthcare. The market opportunity is pretty enticing when you consider that the U.S. alone spent $3.65 trillion on healthcare just last year. Google made the latest headline-grabbing move when it announced that it would buy wearables-maker Fitbit (FIT) in a deal valued at $2.1 billion. Analysts have noted that the acquisition is part of the company's overall strategy to build an ambient intelligent system where Google is omnipresent. Another motive behind the purchase – pending regulatory approvals – is that Fitbit gives Google access to a treasure trove of healthcare data that it can feed to its London-based AI lab DeepMind or its life sciences subsidiary Verily, which is already collaborating on at least one AI healthcare device for remote patient monitoring.

It's no secret that big tech companies like Amazon (AMZN), Microsoft (MSFT), and Alphabet (GOOG), the parent company of Google, are investing in digital healthcare. The market opportunity is pretty enticing when you consider that the U.S. alone spent $3.65 trillion on healthcare just last year. Google made the latest headline-grabbing move when it announced that it would buy wearables maker Fitbit (FIT) in a deal valued at $2.1 billion. Analysts have noted that the acquisition is part of the company's overall strategy to build an ambient intelligent system where Google is omnipresent. Another motive behind the purchase – pending regulatory approvals – is that Fitbit gives Google access to a treasure trove of healthcare data that it can feed to its London-based AI lab DeepMind or its life sciences subsidiary Verily, which is already collaborating on at least one AI healthcare device for remote patient monitoring.

Digital technologies are constantly evolving and finding new applications in healthcare, even while the industry is struggling with adoption and'digital transformation'. Each year new applications emerge, but the underlying technologies driving them remain the same. For 2019, we asked companies around the world one basic question: "Please indicate the key technology which you believe will have the most profound impact on the healthcare industry during 2019?" Of course, these respondents are distributed across widely different sectors – pharmaceuticals and biotechnology, medical devices, medical imaging equipment, in-vitro diagnostics, remote patient monitoring, healthcare IT and digital health solution providers – but excluding care delivery settings such as hospitals and other facilities. This means that these technologies are being viewed through a different lens, depending on which sector the respondent belongs to.