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TECHNOLOGY

Generative AI In Healthcare:
Technology Poised For Impact

By Eleni Stamoulakatou

Originally published on LinkedIn on January 20, 2024 | Generative AI In Healthcare: Technology Poised For Impact

Revised on May 7, 2024 at 6p.m. GR | 11a.m. ET

Generative AI In Healthcare
Healthcare is one of the most critical industries due to its importance in handling public health. For this reason, ensuring that healthcare companies gear towards adopting and embedding emerging and disruptive technologies is not an option but an imperative. The transformation of healthcare can lead to a multi-faceted win as it can help drive new financial benefits, facilitate effective decision making, but most importantly, help guard human health by accelerating the research process and setting the floor for vertical innovation. The Covid-19 pandemic highlighted the value of AI-assisted drug discovery and development, but also went to expose some notable vulnerabilities in various areas such as clinical trials, manufacturing, supply chain management, and more.
 
Generative AI offers a wide range of applications applicable to the healthcare industry, spanning from scientific and medical content generation to manufacturing. Currently, some of the top healthcare providers are using generative AI for patient data processing and extraction, automation and streamlining of administrative tasks (e.g., appointment scheduling) and workflow optimization.
 
However, there is a multitude of ways that generative AI can help healthcare with, such as drug discovery and development, remote patient assistance, medical imaging, clinical diagnosis and personalized treatment, fraud detection and predictive modeling.
 
Generative AI In Healthcare Market Size
Generative AI in the healthcare market globally is projected to surpass around USD 21.74 billion by 2032, growing at a CAGR of 35.14% over the forecast period 2023 to 2032. Available statistics highlight the increasing adoption of generative AI in the healthcare industry, but also offer valuable insights around its potential to revolutionize medicine in its entirety.

Source: Precedence Research

North America is leading a march as the most powerful force in the healthcare market during the forecast period. The key reason behind this expected growth lies in the increasing adoption of the healthcare institution, as hospitals, clinics, and diagnostic centers are already applying it in various domains such as radiology, pathology, and cardiology, aiming to enhance diagnostic accuracy and optimize treatment planning. The key driver is the need for sophisticated high-tech tools that can help augment healthcare professionals' capabilities and streamline standard healthcare processes.

Source: Precedence Research

Generative AI Use Cases and Business Application

Source: curated by author using collected data upon research

June 2024

Below are some popular generative AI applications and examples of how they can help transform various areas in the industry:

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  • Wavenet - is an artificial neural network designed to generate raw audio one text-to-speech tool to improve one's ability to hear and process words. In speech therapy applications, speech synthesis can help patients learn how to pronounce a word or phrase correctly in speech treatment exercises. Speech synthesis and ASR-based (AI-powered technology that converts spoken human speech into written text) technologies can be used to increase the intelligibility of disarranged speech by creating speech similar to the loudspeaker's speech. Business application: use speech technology to improve the quality of human communication and specifically, of those suffering from a hearing, speech or language disability.

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  • Bloom - is an AI model that can handle large-scale datasets faster, and through quickly filtering irrelevant information, reduce the computational load, improving efficiency and allowing real-time data processing and analysis. Business application: use to analyze large genomic datasets, leading to more accurate diagnoses and personalized treatment plans.

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  • Stable Diffusion - is a type of generative model that pulls from a sample distribution and gradually refines it to match a complex target distribution. The stable diffusion process involves a random image and progressively refines it, gearing towards a designated synthetic image as the end state. Business application: use high-quality synthetic medical images to augment datasets for training AI models, test the performance of new imaging devices and simulate patient data for educational purposes within the boundaries of security and privacy.

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  • ChatGPT or Google Bard - are examples of generative-based large language models (LLMs) that use an algorithm trained on large volumes of text-based data. The algorithm analyzes the data and the context of words related to one another to create text based on a prompt. Generative AI-powered chatbots are able to produce an endless supply of creative literature and store a vast volume of text data processed through a neural network that continually adjusts the way it interprets data based on a host of factors, including the results of trial and error. Business application: leverage generative AI-powered chatbots to act as virtual assistants to help patients get faster assistance, book appointments, send them reminders and notifications and answer frequently asked questions, removing this way some of the workload that can often overwhelm healthcare professionals and medical institutions.

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  • Copilot - an AI-driven assistant integrated into CRM and ERP systems. It is now possible to analyze vast amounts of historical sales data and by incorporating various factors - some of which are difficult to anticipate - such as and economic conditions and seasonality, use this data to train the AI model to generate accurate demand forecasts. Business application: gives better and visibility to supply chain and more accurate data to help organizations manage inventory, allocate resources, and foresee market trends

Top Healthcare Organizations Currently Using Generative AI​

  • HCA Healthcare - What: integration of Augmedix app for electronic health records (EHRs). How: use of hands-free device to create accurate medical notes from clinician-patient conversations, which, through multi-party medical speech-to-text processing, are instantly converted into medical notes. This data is available for physicians to review and finalize prior to getting transferred in real time to the hospital’s electronic health record. Benefits: eliminates the possibility of inaccurate data, saves time from manually processing and entering data into the system and alleviates clinician burnout by helping industry professionals save time and direct their focus on providing a more efficient care delivery.

  • Bayer Pharma - What: medical imaging to support screening, early diagnosis and monitoring of treatment outcomes. How: AI-enabled software is used across a wide spectrum of clinical conditions such as early detection and prioritization of potential stroke or cancer patients through quantification applications that help automate tasks (e.g., lesion measurements) typically assigned to radiologists who are already burdened with a heavy daily work lift. Benefits: augmenting the role of experts allows time to provide accurate and timely diagnosis for their patients, reduces potential misses and automates and streamlines recurrent tasks.

What does healthcare C-Suite think about Generative AI?

Healthcare executives think of generative AI as a means for revolutionizing the healthcare norms.

References/Useful Links
 

Source: Patient-First Health with Generative AI: Reshaping the Care Experience | White Paper, WEF

Ecosystem and Principles for Generative AI Acceleration

The key to harness the benefits of generative AI, is collaboration between key players across the healthcare ecosystem.

The 4th Industrial Revolution And The Future of Healthcare
The 4th Industrial Revolution is indisputably driven by technology. Generative AI is a key component to driving what is expected to be a massive market shift. Generative AI-powered tools are enablers that can help organizations' approach on data, which is what will essentially help offer deeper and more substantial business insights for strategic decision making. That said, companies will need to put data at the epicenter of their focus – connect and scale it - and through custom cloud services and solutions, eliminate information siloes, achieve consolidation and integration, accelerate innovation and unlock value across their operations.

The Need For Upskill
With the launch of every new technology, comes the need for a new service and by extension, a new skillset. In the onset of generative AI, companies will need to prepare their employees for a fast-paced transition, and for employees to be able to accommodate the new needs that their role will be enhanced with and future-proof their careers, they will need to understand more about upskilling and how it gives one the opportunity to learn new skills to better their career prospects and boost their professional growth. There is a twofold transition that will need to happen for the upskilling journey to be a successful one. The knowledge gap can be filled through online courses, job aids, quick reference guides and a multitude of learning materials, yet this should be with the help organizational change experts who can help provide step-by-step stewardship to walk both employees and employers through the learning curve to a successful upskilling.

But let’s see an example of a new job in healthcare that didn’t exist a few years ago: Telemedicine physician. Even though telemedicine has been a known concept, it has become more popular in the post pandemic era. With this in mind, a telemedicine physician, who is essentially responsible to provide remote patient care through virtual channels (e.g., online chat, phone etc.), will need to be trained to be able to operate under circumstances that by no means, resemble those that a typical physician is known to operate in. This isn’t to say that the traditional way that a physician operates will be going away, yet a new additional option is entering the picture.
 

Generative AI And The Human Factor

Technology is becoming more human in its nature. Generative AI-powered tools work on behalf of humans and are part of an interconnected ecosystem but we need to work together in order to multiply the collective output of workplace and business professionals and generate value across all industries.

''Human-centered technologies like generative AI are poised to unleash human potential and deliver a staggering array of business and societal benefits, but only if we take a balanced, ‘human by design’ approach that ensures these technologies are used fairly and responsibly''

Paul Daugherty, Chief Technology and Innovation Officer (CTIO), Accenture

In this moment and contrary to the human 'configuration', one's instinctive reaction to push back out of fear of the unknown, needs to be replaced with ownership and purpose to drive change and harness technology's business and societal advantages. The human-machine interaction is driven by the human factor, with its impact being not only industry-focused - healthcare or other - but a holistic one, touching on humans as full beings and the life experience as a whole.

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''Data without action is useless, and generative AI has vast potential to empower decisions, especially in middle- and low-income countries, where healthcare resources are extremely scarce''

John Sargent, Founding Partner, BroadReach Group, on data and the ongoing conversation around generative AI

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