--This research presents a multi-robot system for inpatient care, designed using swarm intelligence principles and incorporating wearable health sensors, RF-based communication, and AI-driven decision support. Within a simulated hospital environment, the system adopts a leader-follower swarm configuration to perform patient monitoring, medicine delivery, and emergency assistance. Due to ethical constraints, live patient trials were not conducted; instead, validation was carried out through controlled self-testing with wearable sensors. The Leader Robot acquires key physiological parameters, including temperature, SpO, heart rate, and fall detection, and coordinates other robots when required. The Assistant Robot patrols corridors for medicine delivery, while a robotic arm provides direct drug administration. The swarm-inspired leader-follower strategy enhanced communication reliability and ensured continuous monitoring, including automated email alerts to healthcare staff. The system hardware was implemented using Arduino, Raspberry Pi, NRF24L01 RF modules, and a HuskyLens AI camera. Experimental evaluation showed an overall sensor accuracy above 94%, a 92% task-level success rate, and a 96% communication reliability rate, demonstrating system robustness. Furthermore, the AI-enabled decision support was able to provide early warnings of abnormal health conditions, highlighting the potential of the system as a cost-effective solution for hospital automation and patient safety. These pressures place a considerable burden on healthcare providers and often compromise the speed and quality of patient care.. Such challenges cause a delay in getting the medicines, slower assistance in emergencies, and more stress on the health workers. Robotic systems may help with dedicated duties and complement medical personnel, which allows faster and more reliable patient care. Social insects often inspire swarm robotics and can enable many small robots to work together to perform complex tasks and complete objectives. Swarm robots can help throughout the healthcare sector, ranging from drug delivery to hospital cleaning and patient monitoring.

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.

Robin Farmanfarmaian is a Silicon Valley-based entrepreneur working in technology and artificial intelligence. She has been involved with more than 20 early-stage biotech and healthcare startups, including ones working on medical devices and digital health. With more than 180 speaking engagements in 15 countries, she has educated audiences on many aspects of technology intersecting healthcare, including artificial intelligence and the shift in healthcare delivery to the patient's home. She has written four books, including The Patient as CEO: How Technology Empowers the Healthcare Consumer and, most recently, How AI Can Democratize Healthcare: The Rise in Digital Care. Healthcare IT News spoke with Farmanfarmaian to discuss where AI is impacting remote patient monitoring today, and how AI can democratize healthcare.

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.

AI and machine learning have gained a lot of popularity and acceptance in recent years. With the onset of the Covid-19 pandemic, the situation changed even more. During the crisis, we witnessed a rapid digital transformation and the adoption of disruptive technology across different industries. Healthcare was one of the potential sectors that gained many benefits from deploying disruptive technologies. AI, machine learning, and deep learning have become an imperative part of the sector.

Artificial intelligence (AI) in medicine has a long history (1). AI has been an active subfield of computer science for more than 60 years, while medicine is even a much older field, which can trace back to thousands of years. Researchers from both AI and medicine communities have been interacting to create novel solutions for better patient care and enabling more efficient healthcare systems (2,3). Collaborations between both communities were either technology-driven or problem-driven. In technology-driven research, innovations are mainly the development and validation of new AI algorithms for selected clinical problems where the algorithms are generic and not necessary to be optimal in solving real-world problems.

No matter what industry you're in, Artificial Intelligence (AI) is all the rage. In pop culture alone it's the central theme of HBO's Westworld, where humanoid AI robots pretend to be people, or even the most recent season of Silicon Valley where a major character was an AI-powered robot named Fiona. AI is also the central, recurring theme at every conference. Even at giant tradeshows like the Consumer Electronics Show (CES) where, this year, we saw autonomous vehicles, voice-enabled bot driving assistants within cars, L'Oreal's thumbnail-sized UV sensor patch, and hundreds of other AI-enabled "smart" products. At South by Southwest (SXSW), it seemed every other session was about AI.

Two emerging technologies are likely to help drag healthcare into the modern age--blockchain and artificial intelligence. And my third area of focus continues to be interoperability, enabling seamless health data exchange. We will see some significant progress in 2018 on adoption of blockchain and AI, and hopefully at least some baby steps on achieving nationwide interoperability. Interoperability Healthcare must make a shift to Interoperability 2.0--that's because health data are essentially useless if systems aren't integrated. A platform for interoperability is essential to thrive in a transformed health system.

Two emerging technologies are likely to help drag healthcare into the modern age--blockchain and artificial intelligence. And my third area of focus continues to be interoperability, enabling seamless health data exchange. We will see some significant progress in 2018 on adoption of blockchain and AI, and hopefully at least some baby steps on achieving nationwide interoperability. Interoperability Healthcare must make a shift to Interoperability 2.0--that's because health data are essentially useless if systems aren't integrated. A platform for interoperability is essential to thrive in a transformed health system.