In the post-pandemic world, the onus seems to be on artificial intelligence (AI) to carry the healthcare sector forward. Maja Dragovic finds out how the attitudes towards AI in the sector have changed over the last 12 months. The focus on AI has definitely shifted since the pandemic, with data being seen as a tool to improve the health and care of a population in a safe, trusted and transparent way. The government's recent data strategy for health and care has set the direction for the use of data in a post-pandemic healthcare system with AI playing a significant role, especially in screening services where the technology can help scan numerous hospital images for irregularities. For Chris Scarisbrick, sales director at Sectra, an accelerator of AI use in screening was the development of the National Lung Screening pilot.

The future of healthcare is being driven by digital transformation and innovation. Healthcare is evolving into a new era where almost everything is connected through digital technologies to improve the way that healthcare is delivered. Most things that need to happen before the patient walks into a clinic or hospital – from appointment scheduling to check in and digital intake forms – can be done using technology. This was true before the pandemic, but now almost every interaction within a healthcare delivery organization (HDO) starts with a call, click, or chat. As hospitals and healthcare systems continue to re-evaluate their patient engagement strategy, they need to intentionally design a hybrid experience.

The adoption of AI in health care is being driven by an exponential growth of health data, the broad availability of computational power, and foundational advances in machine learning techniques. AI has already demonstrated the potential to create value by reducing costs, expanding access, and improving quality. But in order for AI to realize its transformative potential at scale, its proponents need business models optimized to best capture that value. AI changes the rules of business and, as ever, there are some unique considerations in health care. In order to understand these, we studied AI across 15 sets of use cases. These span five domains of health care (patient engagement, care delivery, population health, R&D, and administration) and cover three types of functions (measure, decide, and execute).

Artificial intelligence (AI) has disrupted numerous industries and prompted the addition of the suffix "-tech" to many of them: insurtech, fintech, agritech. Healthcare, in particular, has flourished because of AI, even before the pandemic, as machine intelligence makes scanning large populations for diseases feasible and drives a proactive approach to healthcare -- keeping people healthy instead of waiting for them to get sick. As the name suggests, "population health" focuses on cohorts over individuals, but there is more to it than that. For researchers in healthcare, population health relies on keeping track of the incidence of diseases in a variety of groups of people. For example, they might compare Covid-19 outbreaks among individuals of different demographics who reside in a range of ZIP codes.

Enterprise analytics company Clarify Health has secured $115 million in series C funding to scale its self-service healthcare analytics cloud and business software. Clarify Health combines longitudinal data for more than 300 million "unique patient lives" from government and commercial claims, electronic health records (EHRs) and prescriptions, according to the company. These data can help healthcare professionals manage population health and commercialize pharmaceutical and biotechnology products. "By linking CMS claims data with commercial claims, EHR, prescription and socioeconomic data, our models are trained on large cohorts and a more complete picture of each patient's longitudinal healthcare journey," Clarify Health CEO Jean Drouin, M.D., told Fierce Healthcare. The San Francisco-based company was launched in 2015 and has raised $178 million to date, according to Crunchbase.

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Another prominent example in this regard came from DeepMind's publication of the possible protein structures associated with the COVID-19 virus (SARS-CoV-2) using their AlphaFold system. For example, our process of vetting results in the Global Burden of Disease Study [2] included the visual inspection of thousands of plots showing data together with model estimates. Our experience developing methods for computer certification of verbal autopsy has bolstered our belief that using an explainable approach, even with a reduction in accuracy, can be superior. Qualified practitioners are in short supply. There is increasing awareness that health … enhancing the ability to see and navigate in a procedure. Going beyond the conventional long-haul process, AI techniques are increasingly being applied to accelerate the fundamental processes of early-stage candidate selection and mechanism discovery. This could be the biggest impact of AI tools as it can potentially transform the quality of life for billions of people around the world. These technologies are also being used in the following ways: Preventing crime: AI and machine learning help authorities track and manage the huge amount of data generated by public surveillance devices, and analyze that data in real time for anomalies and threats.

The rediscovery of the potential of artificial intelligence (AI) to improve healthcare delivery and patient outcomes has led to an increasing application of AI techniques such as deep learning, computer vision, natural language processing, and robotics in the healthcare domain. Many governments and health authorities have prioritized the application of AI in the delivery of healthcare. Also, technological giants and leading universities have established teams dedicated to the application of AI in medicine. These trends will mean an expanded role for AI in the provision of healthcare. Yet, there is an incomplete understanding of what AI is and its potential for use in healthcare.

The role of artificial intelligence in healthcare continues to evolve as does the definition of what it is and is not. This state of flux has contributed to slower than desired adoption, unmet expectations, and gaps between deployment and value realization. If clinical artificial intelligence more specifically is to transform patient care, it must deliver insights that are unique, individualized, can be tied to community and align with existing workflows. Join Jvion, a market leader in clinical AI, along with leadership from Microsoft, for a one-hour webinar that will provide clarity and guidance to aid in addressing patient risk and improving population health across communities.