How will AI continue to shape healthcare in 2022? 9 Predictions to see

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Digital health executives share their predictions about how artificial intelligence (AI) will continue to shape the healthcare industry in 2022.

Diana Nole, Vice President and CEO of Healthcare at Nuance Communications, Inc.

AI will find more game-changing use cases: According to Optum’s fourth annual survey on AI in healthcare, 96% of respondents said AI has a major role to play in increasing equity in health and 94% said they have a duty to ensure that AI is used responsibly. in healthcare. As healthcare organizations and researchers set strong standards for the secure sharing of healthcare data, new collaborative AI projects are being set up to drive more informed care decisions.

Brian Foy, Product Manager at Q-Centrix

Artificial intelligence (AI) and natural language processing (NLP) have come to stay, not to replace clinical experts, but as an augmentative tool that makes doctors more efficient. Hospitals will play a crucial role in fostering innovation in this area by partnering with companies committed to developing these automation tools and making greater use of clinical data.

How will AI continue to shape healthcare in 2022?  Executives share their predictions

Ari Kamlani, AI Solutions Architect / Senior Data Scientist Beyond the limits

Healthcare has changed dramatically in recent decades with innovations in brain scanning technology, disease diagnosis, cancer treatment and more. In 2022, some of the most recent and impressive innovations on the horizon of the healthcare industry are emerging from the field of technology, especially artificial intelligence. This pattern is similar to what we have seen in other regulated and risk-averse industries, such as the fintech industry.

Over the next few years, responsible AI solutions will help physicians make decisions in a reliable, fair, and secure way, drastically reducing the time they spend deciphering data. This not only guides doctors towards improving patient outcomes, but also allows them to spend more quality time with their patients.

Mark Day, Vice President of R&D a iRhythm

Bias in AI: Over the next year, AI companies will continue to improve their data collection methods and develop processes that avoid bias in algorithm training and, in turn, performance in the intended population. Specifically, improving the design of clinical trials will encourage more heterogeneous and representative patient populations, leading to algorithms that reduce bias.
In terms of technique, methods will be developed to provide a larger view of the “black box” of AI algorithm decisions, which will guide the understanding of whether these decisions represent a bias based on factors such as race, gender and age.

Mark Olson, CEO of RecoveryOne

AI will be used to provide even more personalized interventions. MSK augmented and virtual reality will use familiar devices such as smartphones and laptops, rather than proprietary hardware and sensors, which cause friction in the consumer experience.

Gaurav Kaushik, Ph.D., co-founder and president of Science IO

Natural language processing is at a turning point, but it has yet to mature in the healthcare industry. Healthcare-specific NLP will reach the age of majority in the coming years as the technological advances we have made in media and finance will translate into healthcare, be more widely available, and act as an accelerator for development. of patient-centered solutions across the industry.

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Dr. Trishan Panch, co-founder of Wellframe

Healthcare providers and insurers will use AI in a variety of contexts in 2022. They will see the best results in a hybrid context, where they can leverage human capacity to generate hypotheses and collaborate by combining it with AI capacity. to analyze large volumes of data to optimize for specific and well-defined criteria. Healthcare systems should not view AI as a medical device, but rather as an information resource. To properly apply AI to clinical workflows, healthcare systems will need to hire AI specialists or physicians to maintain their quality and safety.

Calum Yacoubian, MD, Associate Director, Health Strategy, Linguamatics, an IQVIA company

We have reached a turning point for the adoption of tools such as natural language processing (NLP) that help organizations manage large, unstructured data sets. One factor driving demand for these technologies is the pending deadline for the patient access rule that requires the interoperability of full-text medical records. Payers especially recognize that they will soon be inundated with unstructured data that needs to be managed to effectively inform patient care, achieve value-based care goals, and drive predictive algorithms that improve outcomes. The growing presence of large technology and cloud vendors in the text analysis space has also increased awareness of these tools, which are increasingly available for easy and convenient consumption through cloud-based approaches rather than large-scale software deployments. Finally, the pandemic has affected healthcare in many ways, including increasing acceptance and demand for cloud-based technologies, which have allowed users to access and manage data remotely. The pandemic has also highlighted the need to look beyond structured data and use tools such as NLP to understand and address the social determinants of health factors that lead to unequal outcomes among populations.

Improving the patient experience by optimizing telehealth through COVID-19 and beyond

Art Papier, MD, CEO of VisualDx

By 2022, the huge hype surrounding artificial intelligence in medicine will begin to wane and digital thinking leaders will begin to have more realistic expectations (at least that’s my hope), realizing that clinical decision-making it needs augmented intelligence, not artificial intelligence. The focus will be on collaborative processes, lifelong learning, and information tools that guide and teach. The shortage of professional labor will require better tools, especially visual decision support systems that educate as they are used.

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