Study shows potential for generative AI to increase access and efficiency in healthcare


Getting started with generative AI in healthcare and life sciences

Generative AI – which uses algorithms (such as large language models (LLMs)) to create rather than simply analyse – has captivated the tech world, but brings with it both risk and opportunity. On one hand, the risk of bias and inaccuracy calls into question the ethics of using it in healthcare, but on the other hand, research suggests that it also has the potential to vastly streamline and improve services. In the third episode of our ‘Generative AI’ podcast series, we delve into the fascinating realm of Generative AI and its potential to transform healthcare with Ram Deshpande, EY India Technology Consulting Partner.

generative ai in healthcare

By leveraging generative AI, policymakers can access more detailed demographic information, enabling them to gain deeper insights into specific populations’ health profiles and needs. They can analyze large datasets and identify these populations’ patterns, trends, and disparities. This level of granularity enables the design and implementation of targeted public Yakov Livshits health initiatives, like preventive measures and early intervention programs, that address the unique challenges faced by underserved communities. By understanding the specific health needs and social determinants of health affecting different populations, policymakers can allocate resources more efficiently and effectively to improve population health outcomes.

Complexity of training healthcare data

This innovative approach ensures healthcare decisions are tailored to each individual’s specific context and requirements. Generative AI is poised to revolutionize the healthcare sector, offering tremendous potential for advancements in patient care and outcomes. Generative AI techniques can analyze longitudinal medical imaging data to predict disease progression. By detecting subtle changes in images over time, these models can provide insights into disease trajectories, also helping healthcare professionals make informed decisions regarding treatment plans and interventions. Generative AI algorithms can analyze vast databases of chemical compounds, biological data, and clinical trial results to generate new molecules with desired properties.

Further, patients use generative AI tools to ask questions, converse, and know more about their medical conditions. So, users of generative AI technology need to assess the accuracy and truthfulness of the generated information because AI may find it difficult to keep up with the latest data. AI-generated content is difficult to distinguish from real images, posing ethical complications. Patient engagement is a vital aspect for healthcare facilities as a constant connection with patients regarding their fitness, medication, and health issues accelerates care delivery. With the help of generative AI, healthcare facilities can boost patient engagement as it allows users to start conversations with the AI.

How care orchestration technology improves the perioperative care process

It will change the way creativity is produced in today’s time and the upcoming future. According to Gartner, generative AI is estimated to account for 10 percent of all the data produced by 2025. When I see patients, I have to be cognizant of the three stakeholders I need to serve for every encounter. I need to write notes for care team communication, Yakov Livshits structure data for the revenue cycle teams to help us keep the lights on and be mindful of patients reading my notes on the portal. A. I approached the founding of Abridge first as a cardiologist and, of course, also as a consumer of healthcare who understood that listening, empathy and real dialogue are the heart of effective care.

For example, ChatGPT is a textual genAI tool capable of writing articles, while our product, Dyvo, uses generative AI to enhance product photos with artificially-generated backgrounds. Syntegra’s generative AI technology learns from and replicates any structured data — such as EHR, claims, registries or clinical trials. Generative AI holds immense potential in revolutionizing chronic disease management.

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.

Surgeons, for instance, can practice complex procedures using AI-generated virtual surgery simulations. These simulations mimic various anatomical variations and potential complications, allowing surgeons to refine their skills and gain confidence before operating on real patients. Generative AI is enabling pharmaceutical companies to embrace personalized medicine on a larger scale. By analyzing genetic and molecular data, AI algorithms identify specific factors that affect how patients respond to treatments. These algorithms examine a patient’s medical history, genetic information, and lifestyle choices to create personalized treatment therapies. On the patient side, generative AI has the potential to improve healthcare providers’ call center services.

generative ai in healthcare

LLMs also require huge volumes of data to be trained effectively as the output accuracy of GenAI is highly dependent on the quality of the datasets used to train them, including medical records, lab results and imaging studies. These virtual patient simulations allow students to practice clinical decision-making and hone their diagnostic skills in a safe environment. These simulations provide valuable hands-on experience without risking patient safety. AI-driven chatbots and virtual assistants can also answer students’ questions and provide supplementary information, enhancing their understanding of complex medical concepts. Therefore, hospitals are now embracing the capabilities of Generative AI to optimize their operations. For instance, they can examine past patient records to anticipate the volume of incoming patients.

Generative artificial intelligence is a recent breakthrough that has gained popularity in the healthcare sector. AI models, called “generative,” can produce new content independently, such as text, photos and audio. Generative AI in healthcare can assist practitioners in making better decisions and enhancing patient outcomes due to its capacity to process enormous volumes of data fast and accurately.

Generative AI in healthcare drug discovery can help biopharmaceutical companies generate virtual compounds and molecules tailored with specific properties. By tapping into expansive chemical databases, generative AI models study existing drug structures and their attributes to develop innovative therapeutic agents. This accelerates the recognition of potential drug candidates, significantly shortening the time conventionally required to discover new treatments.


Additionally, the integration of generative AI algorithms with medical imaging technologies can enhance the accuracy and efficiency of diagnostic procedures. AI algorithms can analyze medical images, detect abnormalities, and provide quantitative assessments, aiding radiologists and other healthcare professionals in making more accurate diagnoses. Moreover, the use of generative AI in precision medicine offers opportunities for tailored and targeted treatment approaches.

The Amazing Ways Snowflake Uses Generative AI For Synthetic Data And Natural Language Queries – Forbes

The Amazing Ways Snowflake Uses Generative AI For Synthetic Data And Natural Language Queries.

Posted: Tue, 12 Sep 2023 06:19:25 GMT [source]

But, this chatbot isn’t a clinical decision-making tool, hence it needs human insight too. It assists in suturing wounds or incisions and provides insights Yakov Livshits on surgery procedures based on medical data. In healthcare, generative AI can be used to train medical robots for interpreting health conditions.

  • Earlier this year, the company unveiled AI tools to help health insurers speed up prior authorization.
  • Patient EngagementThere are 3 parts to patient engagement—pre-consultation discovery, patient intake and post-consultation care adherence.
  • Setting comprehensive and transparent guidelines becomes essential to ensure responsible and beneficial integration of this technology in healthcare settings.

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