Generative AI in the Healthcare Industry Needs a Dose of Explainability

These models have a recurrent structure that allows them to capture dependencies over time or sequence. During training, the models are exposed to input sequences and learn to predict the next element in the sequence. Autoregressive models have been used for tasks such as language modeling, speech recognition, and music generation. LLMs can summarize interactions between sales representatives and healthcare professionals (HCPs) through phone and email transcripts with healthcare providers, suggesting the next-best step.

• ChatGPT-based virtual assistants can help patients schedule appointments, receive treatment, and manage their health information.
• Others focus on medical coding, such as Suki, DeepScribe and Regard, and some specialize in medical Q&A, like Atropos Health and Google’s Med-PaLM, she explained.
• This not only improves workflow efficiency but also contributes to better patient outcomes.
• Generative AI models can generate realistic patient avatars that simulate various medical conditions, facilitating virtual consultations.
• GenAI is a branch of artificial intelligence that has the ability to learn from large datasets, resulting in the creation of realistic images, videos, text, sounds, 3D models, virtual environments, and even pharmaceutical compounds.

The global generative AI in healthcare market was valued at USD 1,070 million in 2022 and is estimated to hit around USD 21,740 million by 2032, growing at a healthy CAGR of 35.1% from 2023 to 2032. Another challenge is the need for technical expertise and skillset required to implement and maintain generative AI technology. Healthcare providers would have to invest time and resources into acquiring the necessary skills and talent to develop and maintain generative AI technology. For example, dermatologists can employ this approach to diagnose cases of skin cancer.

Risk prediction of pandemic preparedness

Healthcare organizations see this potential, which is one reason why 64.8% of them are exploring generative AI scenarios and 34.9% are already investing in them, according to IDC Health Insights Analyst Lynne Dunbrack. 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 Yakov Livshits a selected use case, empowering data-driven decisions for further investment and AI integration. A collection of services designed to help you harness AI’s potential, enabling you to make informed decisions, develop effective strategies, and witness firsthand the transformative impact of AI on your organization. Generative AI in healthcare has opened numerous opportunities, and we still have many more sophisticated use cases to discover.

Generative AI techniques, such as federated learning, enable privacy-preserving data sharing among healthcare institutions. This allows researchers to collaborate and train models collectively without directly sharing sensitive patient information, ensuring compliance with Yakov Livshits privacy regulations. Generative AI can generate synthetic patient data, offering valuable resources for various research purposes. Elsewhere, German biotechnology company Evotec has recently invested in UK-based Exscientia, to accelerate AI-powered drug development.

Here are some recent examples of AI in healthcare:

From powering sophisticated chatbots to predicting health outcomes, assisting in drug discovery, and even revolutionising surgical procedures, the applications seem limitless. Doctors, clinicians, and medical staff can also use generative AI technologies as an assistant to support patient care. They can fine-tune the deep learning model with patient data, including previous medical histories. Then, the AI system can aid medical professionals by providing ongoing summaries of the patient’s condition. This allows doctors to focus on prescribing the appropriate treatment instead of being engaged with administrative work. Generative AI in healthcare drug discovery can help biopharmaceutical companies generate virtual compounds and molecules tailored with specific properties.

When added to EHR systems, GAI can write down medical conversations and manage information such as patient histories and lab results. This cuts down on manual work and liberates healthcare professionals, allowing them to redirect their focus from paperwork to direct patient care. Generative AI’s role in healthcare imaging appears promising, as numerous healthcare providers and tech companies are focusing on this application. For instance, NVIDIA introduced RadImageGAN, a cutting-edge multi-modal generative AI for radiology, capable of generating 165 distinct classes across 14 anatomical regions, each with various pathologies. Generative AI in healthcare involves the application of sophisticated artificial intelligence models designed specifically to address the unique challenges and needs of medical practice and research.

The Current State of AI in Healthcare and Where It’s Going in 2023

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.

It could swiftly generate resources like checklists, lab summaries, and clinical orders in real-time. These instant tools could assist medical professionals in decision-making and organization. For instance, if a patient visits a doctor, the system can quickly show the doctor all the important medical information. Generative AI can potentially enable timely intervention by spotting diseases in preliminary diagnoses. The deep learning model can analyze X-ray, MRI, and other medical imaging data to find similarities with patterns it has learned. This way, doctors can prescribe targeted treatment that might result in lesser complications.

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.

Using generative AI ethically

The current process of personalized medication entails healthcare professionals considering individual patient characteristics and medical history to select the most suitable treatment and dosage. However, this approach presents challenges, as understanding how a person’s unique genes and medical history influence drug response is difficult. 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 predicting diseases before symptoms occur to assisting in new drug discoveries, this technology is driving a profound shift in the way humans approach healthcare.

Generative AI also can assist with patient intake processes and medical record collection and retention. Whether through recruitment tools, scheduling assistance or even personalized training programs, generative AI streamlines both administrative and patient workflows. Healthcare organizations must educate their workforce on the use of AI technologies through training programs specific to each AI system. These training programs should teach providers about the limitations of such technologies and the continued need for physician oversight and review of AI outputs.

With Elastic’s data sharing features, the scientific community can share their findings and collectively analyze chemical structures and properties. This can include how molecules bind with each other, how they interact against diseases, and their safety characteristics. The collaborative approach facilitated by Elastic can accelerate drug evaluation and increase collective knowledge in the scientific community. The Elasticsearch platform also supports semantic search and natural language processing, making it easier for generative AI to understand complex search queries and retrieve relevant information faster. Researchers can rely on Elastic to find the information they need to run their drug experiments in a more intuitive and user-friendly manner.

Generative AI systems can generate new data, images, or even complete works of art. In healthcare, this technology holds immense promise for enhancing diagnostics, drug discovery, patient care, and medical research. This article explores the potential applications and benefits of generative artificial intelligence in healthcare and discusses its implementation challenges and ethical considerations. The demand for precise and personalized treatment plans is a significant factor driving the growth of generative AI in the healthcare market. Conventional treatment methods typically rely on a generic approach that may not account for individual patient characteristics and specific requirements.