Chronic diseases, including diabetes, hypertension, asthma, HIV-AIDS, epilepsy, and tuberculosis, necessitate rigorous adherence to medication to avert disease progression, manage symptoms, and decrease mortality rates. Adherence is frequently undermined by factors including patient behavior, caregiver support, elevated medical costs, and insufficient healthcare infrastructure. We propose AdCare-VLM, a specialized LLaVA-based multimodal large vision language model (LVLM) by introducing a unified visual latent space with pre-alignment to facilitate visual question answering (VQA) concerning medication adherence through patient videos. We employ a private dataset comprising 806 custom-annotated tuberculosis (TB) medication monitoring videos, which have been labeled by clinical experts, to fine-tune the model for adherence pattern detection. We present LLM-TB-VQA, a detailed medical adherence VQA dataset that encompasses positive, negative, and ambiguous adherence cases. Our method identifies correlations between visual features, such as the clear visibility of the patient's face, medication, water intake, and the act of ingestion, and their associated medical concepts in captions. This facilitates the integration of aligned visual-linguistic representations and improves multimodal interactions. Experimental results indicate that our method surpasses parameter-efficient fine-tuning (PEFT) enabled VLM models, such as LLaVA-V1.5 and Chat-UniVi, with absolute improvements ranging from 3.1% to 3.54% across pre-trained, regular, and low-rank adaptation (LoRA) configurations. Comprehensive ablation studies and attention map visualizations substantiate our approach, enhancing interpretability.

Judges are important policymakers but are less accountable to the public than legislators. One way judges strengthen the legitimacy of their policy choices given low accountability is by providing written justifications based on shared principles, which are then published as judicial opinions. John Rawls argued that "[The U.S. Supreme Court's] role is not merely defensive but to give due and continuing effect to public reason by serving as its institutional exemplar." Presumably, this legitimizing function is best served when the general population can understand the written justifications. In practice, however, judicial opinions tend to be extremely long and written in complicated technical language that is inaccessible except to trained lawyers.

In the not-too-distant future, many of us may routinely use 3D headsets to interact in the metaverse with virtual iterations of companies, friends, and life-like company assistants. These may include Lily from AT&T, Flo from Progressive, Jake from State Farm, and the Swami from CarShield. We'll also be interacting with new friends like Nestlé's Cookie Coach, Ruth, the World Health Organization's Digital Health worker Florence, and many others. Creating digital characters for virtual reality apps and in ecommerce is a fast-rising new segment of IT. San Francisco-based Soul Machines, a company that is rooted in both the animation and artificial intelligence (AI) sectors, is jumping at the opportunity to create animated digital avatars to bolster interactions in the metaverse.

Each year, over 2.5 million people, most of them in developed countries, die from pneumonia [1]. Since many studies have proved pneumonia is successfully treatable when timely and correctly diagnosed, many of diagnosis aids have been developed, with AI-based methods achieving high accuracies [2]. However, currently, the usage of AI in pneumonia detection is limited, in particular, due to challenges in generalizing a locally achieved result. In this report, we propose a roadmap for creating and integrating a system that attempts to solve this challenge. We also address various technical, legal, ethical, and logistical issues, with a blueprint of possible solutions.

Health care has come a long way and with the integration of technologies including artificial intelligence, machine learning¹ and natural language processing, clinicians derive insights from data with better outcomes. At the same time, the adoption of technology in healthcare has faced challenges with health care providers unable to harness the power of technology to address patient problems. Human-centered design is one framework gaining traction in the health care world, with physicians using frameworks to understand patient problems and address them. The human-centered model has the interest of patients and enables collaboration and communication. Simply put, human-centered design² brings together all stakeholders including patients, clinicians and technology to offer seamless experiences across the board.

In 2003, the SARS outbreak took the world by surprise. "For me, the SARS outbreak was an eye-opening event," says Dr. Kamran Khan, infectious disease physician, professor of medicine and public health at the University of Toronto, and founder and CEO of BlueDot. "I recognized that we'd never seen anything like it before, but there would be more outbreaks like this again in the future." Khan spent the next 10 years studying infectious disease spread, looking for a way to better detect and respond to threats like SARS and the ones that followed. By 2013, machine learning technology had advanced to the point where he was able to put his vision of a digital global warning system into action -- and BlueDot was born.

With most US states now reporting sustained increases in new coronavirus cases, fear about the pandemic's resurgence is on the rise. That is placing renewed pressure on the key elements in this healthcare battle, including early detection, containment, triage and diagnosis, and vaccine development. Artificial intelligence (AI) has the potential to bring an arsenal of important weapons to this fight. But the reviews are mixed on how effectively healthcare has used AI in the past. Many experts hope that the current crisis will change that.

The coronavirus outbreak happened in our cutting edge, exceptionally connected, information-dense world. However, dissemination of accurate, up-to-date data about the spread of the ailment stays a challenge. Conventional media (radio, TV and print channels) have contracting audiences and the best require memberships for access. A few local and provincial authorities have made text-based notifications available, yet these are accessible just to the individuals who register and aren't accessible in each territory. More youthful audiences incline toward social media over traditional channels but many social media channels have been challenged by fake news and privacy breaches and aren't in every case completely reliable.

Global health organizations' bandwidth and effectiveness have been put to the test as the novel coronavirus continues to wreak havoc all over the world. It has resulted in difficulties in keeping up with their mandate to try and help citizens be more informed about the current medical situation. Now, more than ever, it is important to give the public timely, truthful, and comprehensible information. These organizations have been questioned as to whether or not they have been doing a good job in giving out proper information and if they are even equipped to handle this type of crisis. However, with the help of conversational artificial intelligence (AI), everyone is hoping that they could improve their responsiveness using the technology.

As we discussed in previous No Jitter posts, COVID-19 has sent healthcare, local government, and other customer service operations into a tailspin, with physical contact centers shutting down just as they need to deal with a barrage of questions around the disease. In response, contact center providers are scrambling to help these customers spin up home-based operations and deal with unanticipated call volumes. In a recent interview with No Jitter, Omar Tawakol, VP and GM of Cisco Contact Center, shared how Cisco is helping businesses get up and running quickly with its cloud platform, Webex Contact Center; support remote agents; and deflect calls onto AI capabilities to ease the load on live agents. Over the last couple of weeks, Cisco has helped one customer shift 10,000 agents into a work-from-home scenario, and another bring on 1,000 agents to answer COVID-19 questions coming in from around the country, he cited as just two of many examples. All told, Cisco provides contact center technology to over 36,000 customers (on-prem plus cloud) and hosts 3.6 million agents globally, many of are overwhelmed by the global pandemic, Tawakol said.