How Generative AI is Influencing Healthcare Industry: IBM and Google’s Strategic Leadership

VAEs have been widely used for image generation, text generation, and anomaly detection tasks. In April, Med-PaLM 2, our medically-tuned version of PaLM 2, was made available to a select group of customers to explore use cases and share feedback. Through our close work with these early testers, we’ve been able to progress the technology and are ready to share with more customers. Next month, we’ll make Med-PaLM 2, which supports HIPAA compliance, available as a preview to more customers in the healthcare and life sciences industry — a critical step to developing our LLMs safely and responsibly.

• Generative AI has many potential uses in healthcare, including drug discovery, disease diagnosis, patient care, medical imaging, and medical research.
• According to Gartner, generative AI is estimated to account for 10 percent of all the data produced by 2025.
• By analyzing vast amounts of patient information, including electronic health records, genetic profiles, and clinical outcomes, generative AI models can generate personalized treatment recommendations.
• This data repository is vital for generative AI to access diverse sources of information needed to facilitate research and new drug discoveries.
• Generative artificial intelligence is a groundbreaking force that is sweeping through the healthcare industry, promising transformative advancements and personalized patient care in ways that people have never seen before.
• From analyzing volumes of medical literature to planning clinical trials, deep learning models allow researchers to be more efficient when advancing medical science.

3M HIS has a long history of eliminating revenue cycle waste, driving value-based care and empowering clinicians to spend more time with their patients. We’ve built solutions across health care that help decrease burnout by automating administrative tasks and help improve the quality of care our clients provide. And then we take those learnings back … and if something doesn’t work in the way it was intended, or if it adds to the administrative burden of our providers, we go back to the drawing board.

Generative AI in healthcare: Real-world examples

Online travel agencies and startups are integrating with ChatGPT and Bard to enhance the travel planning (and potentially booking) experience, in an industry that still contains plenty of legacy technology. According to co-founder Paul Dereck, in just two days, 14.6K people used Glass AI to submit 25.7K queries, with users rating 84% of DDx and 78% of clinical plan outputs as helpful. Like Google, however, Dereck also highlighted a lack of total accuracy, with users citing an accuracy rate of 71% for DDx outputs and 68% for clinical plans. Consequently, Dereck adds that the focus should be on Glass AI’s ‘helpfulness’ rather than precision, suggesting that this type of technology should merely be used as an aid (and to sit alongside) human judgement. In health specifically, 28% of working hours were defined as tasks with higher potential for automation (with the potential to be transformed by LLMs and requiring reduced involvement from a human worker).

US healthcare organizations publish hundreds if not thousands of informational pages on their platforms; most are buried so deeply that patients can never actually access them. Generative AI solutions based on internal data can deliver this information to patients conveniently and seamlessly. This is a win-win for all sides, as the health system finally sees ROI from this content, and the patients can find the services they need instantly and effortlessly. Yet healthcare organizations are pushing ahead, with 98% integrating or planning a generative AI deployment strategy in an attempt to offset the impact of the sector’s ongoing labor shortage.

MEDITECH: Making it easier to search and summarize electronic health records

In 2019, reports of the company’s “Project Nightingale,” raised concerns about data privacy and security. Both Google and Ascension said the work was compliant with federal patient privacy laws. Prior authorizationPrior authorization is the arduous process insurance companies impose on physicians to seek approval before they can prescribe certain drugs to a patient or schedule certain procedures.

The research team’s results are published in the Journal of Medical Internet Research. Longer term, I believe generative AI will make a massive contribution to clinical decision-making, how we train and utilize clinicians, and how we drive better healthcare policy. We will have a much more complete and real-time understanding of patients, the efficacy of treatments and the best ways to help optimize the health of populations that share important characteristics. Consent and privacy are major concerns around the use of AI in healthcare and Google generated significant controversy with an earlier partnership with the hospital system Ascension using AI to analyze millions of medical records.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

We develop outstanding leaders who team to deliver on our promises to all of our stakeholders. In so doing, we play a critical role in building a better working world for our people, for our clients and Yakov Livshits for our communities. For example, a study published in NCBI used wearable devices and generative AI to predict influenza outbreaks with high accuracy, enabling timely public health interventions.

Rather than deploying independently, GenAI integrates well with cloud infrastructure and IoT devices powering medical systems. Medical institutions generate and store large numbers of medical and patient data that AI models can process. By training machine learning to work on these data, you can create AI-powered systems that improve healthcare delivery on multiple fronts.

Robust Model Evaluation

As the co-founder of Uptech, I’ve been closely following the developments in generative AI. We’ve also integrated the technology into several apps, including Dyvo, Plai, and Hamlet. Our work marked our understanding, skills, and business acumen in the respective industries. Download this eBook to see how organizations overcome common challenges and realize scaled, widespread, and sustainable growth through intelligent automation. Experience a hands-on demonstration of generative AI’s potential through implementing a selected use case, empowering data-driven decisions for further investment and AI integration.

A study published in Pubmed utilized generative models to predict the progression of chronic kidney disease, aiding in personalized treatment planning and interventions. Generative AI models can simulate diverse patient populations, enabling researchers to conduct virtual clinical trials. This helps optimize trial designs, evaluate treatment effectiveness, and enhance the generalizability of trial results. Generative AI models have the capability Yakov Livshits to produce a wide range of outputs, such as images, text, music, and videos. In fact, it can be including generating art and music, creating content, synthesizing data, and simulating realistic human-like conversations. According to a report by Grand View Research, the market for artificial intelligence in healthcare, which was estimated to be worth USD 15.4 billion in 2022, is anticipated to rise at a CAGR of 37.5% from 2023 to 2030.

As technology advances, it will be essential to address these challenges and ensure that the benefits of generative AI in healthcare are realized responsibly and ethically. For example, generative AI can be used to develop personalized cancer treatment plans. The algorithm can analyze a patient’s tumor DNA and identify the genetic mutations driving the cancer. Based on this information, the algorithm can recommend a personalized treatment plan that targets specific genetic mutations. With its potential to generate images, text, audio, and much more, its applications will not be limited to just the ones stated in this article.

For example, if a patient’s health metrics deviate from the norm, Elasticsearch can trigger an alert to notify trial administrators, who can immediately intervene if necessary. The models displayed in Kibana can estimate patients’ responses to treatment, adverse effects, and the likelihood of success. This article explains the current state of generative AI in healthcare, its potential benefits and challenges, and discusses the future direction of this rapidly-evolving field.

AI algorithms can analyze data from wearable devices, patient-reported outcomes, and environmental sensors to monitor patients’ health status and provide timely interventions or alerts. This has the potential to improve patient engagement, enable early detection of health deterioration, and reduce healthcare costs. Thus, the generative AI in healthcare market is poised for significant growth as the demand for advanced decision-making tools, personalized treatment approaches, and efficient healthcare systems continues to rise. By leveraging the capabilities of generative AI algorithms, healthcare organizations can enhance patient care, accelerate medical research, and transform the healthcare landscape.