Clinical decision support systems (CDSS) are used to improve clinical and service outcomes, yet evidence from low- and middle-income countries (LMICs) is dispersed. This protocol outlines methods to quantify the impact of CDSS on patient and healthcare delivery outcomes in LMICs. We will include comparative quantitative designs (randomized trials, controlled before-after, interrupted time series, comparative cohorts) evaluating CDSS in World Bank-defined LMICs. Standalone qualitative studies are excluded; mixed-methods studies are eligible only if they report comparative quantitative outcomes, for which we will extract the quantitative component. Searches (from inception to 30 September 2024) will cover MEDLINE, Embase, CINAHL, CENTRAL, Web of Science, Global Health, Scopus, IEEE Xplore, LILACS, African Index Medicus, and IndMED, plus grey sources. Screening and extraction will be performed in duplicate. Risk of bias will be assessed with RoB 2 (randomized trials) and ROBINS-I (non-randomized). Random-effects meta-analysis will be performed where outcomes are conceptually or statistically comparable; otherwise, a structured narrative synthesis will be presented. Heterogeneity will be explored using relative and absolute metrics and a priori subgroups or meta-regression (condition area, care level, CDSS type, readiness proxies, study design).

Addressing the imminent shortfall of 10 million health workers by 2030, predominantly in Low- and Middle-Income Countries (LMICs), this paper introduces an innovative approach that harnesses the power of Large Language Models (LLMs) integrated with machine translation models. This solution is engineered to meet the unique needs of Community Health Workers (CHWs), overcoming language barriers, cultural sensitivities, and the limited availability of medical dialog datasets. I have crafted a model that not only boasts superior translation capabilities but also undergoes rigorous fine-tuning on open-source datasets to ensure medical accuracy and is equipped with comprehensive safety features to counteract the risks of misinformation. Featuring a modular design, this approach is specifically structured for swift adaptation across various linguistic and cultural contexts, utilizing open-source components to significantly reduce healthcare operational costs. This strategic innovation markedly improves the accessibility and quality of healthcare services by providing CHWs with contextually appropriate medical knowledge and diagnostic tools. This paper highlights the transformative impact of this context-aware LLM, underscoring its crucial role in addressing the global healthcare workforce deficit and propelling forward healthcare outcomes in LMICs.

Growing populations around the world are experiencing (and contributing to) a shortage of healthcare workers, and the gap is expected to widen in the future. "The world will be short of 12.9 million healthcare workers by 2035; today, that figure stands at 7.2 million," as per a recent report by the World Health Organization (WHO). Globally, billions of people will suffer serious health consequences if the findings in today's WHO report are not addressed. Experts believe that integrating technology into healthcare and digitalizing the system can address future challenges. The healthcare sector has been embracing artificial intelligence (AI) by using it to assist doctors, hospitals, pharmaceutical companies, and others in overcoming practical challenges.

The year 2022 has been an eventful year in the digital health segment globally. Thanks to the coronavirus pandemic the much-needed digital transformation for the healthcare sector has opened many doors. Several industry experts told Financial Express.com that the growth continued its momentum in 2022 and it will enhance further in 2023. The rapid penetration of smartphones and the internet, coupled with supportive government policies, has propelled the growth of the digital healthcare market in India. In India, the central government is also focusing more on digital health ventures to increase access to quality healthcare.

The healthcare sector, particularly tertiary-care hospitals, face an ever-increasing amount of pressure due to evolving demands aided by the growing population and unforeseen pandemics. Mounting healthcare needs directly impact patients' overall experience; including prolonged waiting periods, delayed appointments, mired level of services, and hindered ability to provide proper care. With the unprecedented global health crisis we have faced in recent years, the international healthcare system has been pushed to reform and transform. In this light, artificial intelligence (AI) and emerging technology have become increasingly prevalent, propelling efforts to improve patient care, solutions, and overall healthcare outcomes. Furthermore, the wider acceptance, and even promotion of smart technology, amongst clinicians, as a tool for informed clinical decisions has helped streamline operations, improve outcomes, and improve patient and staff satisfaction.

"Al will not replace doctors but instead will augment them, enabling physicians to practice better medicine with greater accuracy and increased efficiency." Artificial intelligence (AI) and machine learning (ML) have received widespread interest in recent years due to their potential to set new paradigms in healthcare delivery. It is being said that machine learning will transform many aspects of healthcare delivery, and radiology & pathology are among the specialties set to be among the first to take advantage of this technology. Medical imaging professionals in the coming years will be able to use a rapidly expanding AI-enabled diagnostic toolkit for detecting, classifying, segmenting, and extracting quantitative imaging features. It will eventually lead to accurate medical data interpretation, enhanced diagnostic processes, and improved clinical outcomes.

New Delhi: Metaverse, the new buzzword amongst healthcare, a collective virtual shared space is no more a mystery box. This new emerging technology which is more prominent in the cryptocurrency market and gaming segment is now slowly proliferating in the healthcare domain. Some of the big hospitals are already adapting the digital virtual space of'metaverse'. ETHealthWorld explores what does this new technology'really' mean for healthcare? How will this technology make transformational changes, break the physical rules of the real world and redefine the future of the health domain.

National, 24 February 2022: The pandemic has demonstrated that health care organizations can become more resilient, agile and innovative if they shift to digitally enabled business models with data at the core, according to an EY report, "Getting future ready", released at BioAsia 2022 - Asia's largest Life-sciences and Health-Tech convention. The report highlights how healthcare organizations need to create the right data environment to support a more human-centered approach. Hitesh Sharma, National Tax Leader -Life Sciences, EY India said, "Healthcare delivery is moving outside the four walls of the traditional health system. Health care providers who take the lead in engaging with users across the health care value chain will be best placed to integrate the physical and the virtual world. Healthcare delivery in India, predominantly recognised for sick care delivery, will have to innovate in the coming decade to build next-gen capabilities, and unlock the power of data to enhance the overall healthcare experience."

Big data, artificial intelligence (AI), internet of things (IoT) and deep learning (DL) are revolutionizing modern healthcare post pandemic. After having made remarkable improvements in finance, retail and marketing, big data, artificial intelligence, internet of things (IoT) and deep learning are now transforming healthcare. The volume of data involved in healthcare studies and analysis makes it a perfect use-case for these ground breaking technologies. Healthcare industry handles an immense load of data that is piling up every day. Sooner or later, we will need big data tools to transform healthcare information into relevant insights that can help the development of health services.

Artificial Intelligence (AI) systems are getting smarter every passing moment. Some complex tasks like driving and understanding natural language are already being allotted to AI, but can we move a step further? In this article, we look at prospects of using AI for medical diagnosis. Artificial Intelligence can be used to diagnose cancer, triage critical findings in medical imaging, flag acute abnormalities, provide radiologists with help in prioritizing life threatening cases, diagnose cardiac arrhythmias, predict stroke outcomes, and help with the management of chronic diseases. Artificial Intelligence (AI) has been around for quite some time now.