Generative AI solutions have evolved remarkably well over the past few months. While ChatGPT may have captured the public imagination since its inception last year, major players have been developing generative AI solutions for their businesses that are far more effective than just an interactive chatbot.
Microsoft’s Azure is on the verge of releasing new GenAI capabilities that are meant to make information accessible for clinicians and patients. This includes patient timelines, where generative AI will be used to extract specific information, like medical information, from unstructured data and organize the data chronologically to give a complete view of a medical patient’s history. Now that’s futuristic.
What makes it even better is that healthcare has historically been one of the laggards when it comes to adopting new technologies. Clearly, the healthcare industry is changing for the better.
Generative AI for healthcare is unlocking a slice of an unrealized $1 trillion improvement potential in the industry. Recently, clinicians at a Chicago convention were awestruck by the real-time transcription of patient visits into structured notes. This isn’t just a convenience; it’s become a revolution.
While the promise is immense, caution is crucial.
Data security and accuracy are non-negotiable, necessitating a “human in the loop” for oversight. Let’s discuss the impact of generative AI in healthcare through examples that highlight the future of an AI-powered healthcare experience.
How Generative AI for Healthcare is Helping with Costs and Medical Administration
The healthcare sector is at a crossroads. With PwC projecting a 7% rise in healthcare costs by 2024 due to factors like workforce burnout and inflation, the industry is in dire need of innovative solutions. Enter generative AI for healthcare , a technology that’s capturing both interest and investment at an unprecedented rate.
Accenture reveals that AI could enhance 40% of healthcare providers’ working hours, while Forbes suggests that generative AI in healthcare could save the U.S. medical sector a staggering $200 billion annually. By analyzing unstructured data like patient records and medical images, Generative AI is revolutionizing not just administrative tasks but also medical training, clinical diagnosis, and drug development.
A Generative AI Startup that Received $44 Million in Funding Navina, a medical AI startup, has developed a generative AI assistant that’s already making waves. This tool can sift through a plethora of patient data, from EHRs to insurance claims, providing doctors with real-time status updates and care recommendations. It even crafts structured data , such as documents like referral letters and progress notes. With a robust $44 million in funding, Navina’s success underscores the medical community’s growing trust in generative AI’s ability to alleviate administrative burdens.
Synthetic Patient Data Generation will Help Ease Privacy Concerns for Administration The National Library of Medicine stated that synthetic data generation is a viable answer to restricted access to authentic patient data. Powered by generative AI, it is revolutionizing healthcare research and training. This allows for a risk-free simulation of medical scenarios, from drug interactions to disease outbreaks.
The benefits are tangible. Synthetic data accelerates medical research by providing scalable datasets for training machine learning models. Thus, fast-tracking the development of diagnostic tools and treatment plans. As a result, numerous healthcare institutions are already leveraging synthetic patient data for diverse applications, from epidemiological studies to personalized medicine protocols.
A group of scientists from Germany developed GANerAid , an AI-driven model designed to create synthetic patient data for use in clinical studies. Utilizing the Generative Adversarial Network (GAN) methodology, this model can generate medically relevant data with specific attributes, even when the initial training dataset is relatively small.
Read more: Navigating Challenges for Generative AI Use Cases
Generative AI Can Speed Up Administrative Task Automation Generative AI in medicine is tackling administrative tasks with surgical precision. For instance, in the insurance sector, generative AI can automatically summarize vast amounts of data, a task that traditionally consumes significant human resources.
Generative AI is also revolutionizing the claims and prior authorization processes. On average, it takes ten days to verify prior authorization—a timeline that can be drastically reduced with generative AI solutions.
Leveraging generative AI, Kanerika recently partnered with a leading U.S. insurance company to optimize its financial model, enabling expansion plans. Kanerika devised a comprehensive financial analysis and forecasting strategy that uncovered key financial trends, evaluated risks, and facilitated strategic growth planning.
How Generative AI for Healthcare is Revolutionizing Medical Training
Medical errors are a significant concern, contributing to an estimated 44,000 to 98,000 deaths annually in the United States alone. That’s a staggering 1.8% to 4.0% of all deaths in the US. Generative AI for healthcare is poised to reduce these grim statistics by enhancing medical training through realistic simulations.
Unlike traditional training, which relies on pre-programmed scenarios, generative AI can dynamically generate patient cases , adapting in real-time to the decisions made by medical trainees.
This offers a more authentic and challenging learning experience, better preparing healthcare professionals for real-world situations.
US Universities are Creating Medical Simulations with Generative AI For instance, the University of Michigan has developed a generative AI for healthcare model that creates various scenarios for simulating sepsis treatment.
The author noted in the research paper that “AI in sepsis spans both prediction and case identification. In some patients, the goal is to identify impending sepsis. Conversely, some patients present to the emergency department in septic shock, and the AI goal is to identify and classify the presence of the disease.”
Similarly, the University of Pennsylvania employed Generative AI to simulate the spread of COVID-19. Hence, allowing researchers to test the efficacy of interventions like social distancing and vaccination
Read More – Best Generative AI Tools For Businesses in 2024
Generative AI for Healthcare in Clinical Diagnosis When it comes to diagnosing cancer, time is of the essence. Generative AI is stealing the spotlight by outperforming even seasoned medical professionals. From skin cancer to lung cancer, generative AI algorithms are trained on extensive datasets of medical images. Thus, enabling them to identify disease-specific patterns with remarkable accuracy.
For instance, generative AI can analyze CT scans to detect lung cancer or scrutinize skin images to identify signs of skin cancer. These algorithms are not just assisting but are becoming indispensable tools for radiologists and dermatologists. As a result, it enhances diagnostic accuracy and patient outcomes.
But generative AI’s use cases don’t end there. It’s also revolutionizing the quality of medical imaging. Hospitals are employing generative AI tools to transform low-quality scans into high-resolution images, making it easier for radiologists to spot anomalies.
AI’s Groundbreaking Success in Medical Diagnostics Researchers have harnessed Generative Adversarial Network (GAN) models to transform low-quality medical scans into high-resolution images. The approach was rigorously tested across various types of medical scans. The scans included brain MRIs, dermoscopy, retinal fundoscopy, and cardiac ultrasound images.
The results? A significant boost in anomaly detection accuracy, setting a new standard in medical imaging.
On another front, Google’s Med-Palm 2, trained on the MedQA dataset, has achieved an 85% accuracy rate in answering medical questions. Therefore, marking a promising start for generative AI as a diagnostic assistant.
Accelerating Drug Development with Generative AI for Healthcare
It typically takes up to $2 billion in costs and years of research to develop medicinal drugs. Generative AI is poised to disrupt this paradigm, offering a lifeline to an industry burdened by time and financial constraints.
By employing generative AI algorithms in healthcare, the pharmaceutical sector stands to save an estimated $26 billion annually in drug design alone. Not to mention an additional $28 billion in clinical trial expenses.
Read more: Empowering Business Performance Reporting using Generative AI
Pharma’s Strategic Partnerships with Generative AI Startups The pharmaceutical landscape is undergoing a seismic shift, underscored by Recursion Pharmaceuticals’ recent $88 million acquisition of two Canadian AI startups. One of these startups, Valence, brings its generative AI expertise to the table. It is focusing on designing drug candidates from datasets that traditional methods find insufficient.
Meanwhile, a team from the University of Toronto has developed ProteinSGM. It is a generative AI system that produces novel proteins at an unprecedented rate. These proteins are then evaluated by another AI model called OmegaFold, solidifying their potential as viable protein structures.
Ethical and Regulatory Challenges with Generative AI Harvey Cushing once said, “A physician is obligated to consider more than a diseased organ, more than even the whole man—he must view the man in his world.” A patient, after all, is a human being who values his data privacy and right to equality.
However, bias, lack of regulation, accuracy concerns, and accountability are the four ethical pillars shaking the foundation of generative AI in medicine. Bias, especially, is a dangerous variable in this equation. If training data lacks diversity, AI models can perpetuate existing inequalities that are widespread within our society.
Regulatory oversight is still in its infancy, leaving a vacuum filled with ethical ambiguities. Accuracy is non-negotiable in healthcare. Even minor AI errors can have severe consequences. And when it comes to accountability, the lines are blurred.
Therefore, should the accountability of AI rest with the doctor, the AI implementation partner, or the developers? This is where the need for a skilled and experienced generative AI consulting firm arises.
The Need for a Trustworthy Partner in Navigating Generative AI
Enterprises looking to use generative AI have to be very specific about their need for the technology. Considering their industry and the role of the generative AI solution, businesses need AI solutions tailored to their unique requirements with their own set of data.
From selecting the right algorithms and integrating them into existing systems to ensuring data security and regulatory compliance, the challenges are numerous. This is why it is pivotal for companies to choose the right generative AI consulting partners to work with. Here are some benefits of partnering with AI consulting firms:
Read more: Generative AI Consulting: Driving Business Growth With AI
Proven Process That is Backed by Experience A reliable implementation partner brings a proven process to the table. A roadmap that has been refined through multiple successful past implementations. This level of expertise not only speeds up the deployment but also mitigates risks. Simultaneously, ensuring that common implementation pitfalls are avoided.
Frameworks and Tools Having a partner with a suite of frameworks and tools can be a game-changer for your business. Hence, allowing your AI solution to be built according to your unique set of needs. These resources can streamline various stages of the implementation process, from data collection and analysis to monitoring and maintenance.
Expertise and Domain Knowledge A trustworthy partner comes with expertise not just in generative AI technology but also in the specific domain in which your enterprise operates. This is critical for tailoring AI solutions to your unique challenges and opportunities. Moreover, it helps you follow the ethical and legal requirements for using generative AI for healthcare.
Change Management Facilitation Change is one of the most challenging aspects of implementing new technology. A reliable partner will provide change management facilities to ensure a smooth transition. As well as train your staff, and help you adapt your organizational culture to embrace the new AI capabilities.
Kanerika – Advancing the Future of Healthcare with Generative AI Implementation
A study from BMC Medical Ethics reveals that the volume of data needed to train AI models poses a severe risk to patient privacy. NTT Data’s report echoes this, emphasizing the need for strict data handling protocols. What is a significant challenge here for healthcare companies becomes just another easily solvable step when you work with a trusted generative AI consultant.
Enter Kanerika, your ideal partner to navigate these challenges and create the best generative AI technology for your healthcare business.
With over 20 years of proven experience in data management and AI/ML, Kanerika offers robust, end-to-end solutions that are ethically sound and compliant with emerging regulations. Our team of 100+ skilled professionals is well-versed in cloud, BI, AI/ML, and generative AI. Therefore, ensuring that you’re at the forefront of technological advancements.
With a number of successful generative AI-aligned projects under our belt, our solutions are both scalable and future-proof. Experience a timeless, tailored service with Kanerika.
FAQs How is generative AI used in healthcare? Generative AI helps healthcare by creating realistic synthetic data for training models without privacy risks, designing new drugs and materials faster through simulations, and personalizing patient care with tailored treatment plans and educational materials. It's essentially accelerating research, improving efficiency, and enhancing the patient experience. This is all achieved by learning patterns from vast datasets and then generating new, relevant information.
What type of AI is used in healthcare? Healthcare uses a blend of AI types, not one single kind. You'll find machine learning for diagnosis prediction and personalized treatments, natural language processing to analyze medical records and assist doctors, and computer vision for image analysis (like X-rays). Essentially, it's a toolbox of AI techniques tailored to specific medical needs.
How generative artificial intelligence AI can improve public health? Generative AI can revolutionize public health by creating realistic simulations for disease spread modeling and drug discovery, leading to faster and more effective interventions. It can personalize health information and education, making it more accessible and impactful for diverse populations. Furthermore, AI can analyze vast datasets to identify hidden patterns and predict outbreaks, allowing for proactive public health strategies. This results in improved disease management and resource allocation.
How is generative AI used in clinical research? Generative AI accelerates clinical research by creating synthetic patient data for training models, predicting drug responses, and designing clinical trials more efficiently. It helps overcome data scarcity and privacy concerns, enabling faster and more robust analysis. This leads to quicker development of new treatments and improved healthcare outcomes. Ultimately, it aids in the discovery and testing of therapies with greater precision.
What are generative AI examples? Generative AI creates new content instead of just analyzing existing data. Think of it like this: it doesn't just *understand* images, it *makes* new ones. Examples include tools that write stories, compose music, design logos, or even generate realistic-looking photos – all from scratch, based on its training. It's essentially AI that's creative.
How big is the Gen AI market in healthcare? The healthcare generative AI market is exploding, but pinning down an exact size is tricky. Early estimates suggest billions in current value, with projections into the tens of billions within a few years. Growth is driven by unmet needs in drug discovery, diagnostics, and personalized medicine, meaning its ultimate size depends on adoption rates and regulatory approval processes. It's a rapidly expanding landscape.
Which technique is commonly used in generative AI? Generative AI relies heavily on neural networks, particularly deep learning models. These models, often trained on massive datasets, learn underlying patterns to then generate new, similar content. Think of it like teaching a computer to paint by showing it thousands of paintings – it learns the style and creates its own. Transformer networks are a prominent example, excelling in text and image generation.
What is the application of AI in healthcare in India? AI in Indian healthcare is rapidly transforming diagnostics, improving access to quality care especially in remote areas. It's boosting efficiency through automated tasks like appointment scheduling and medical record management, and accelerating drug discovery and personalized medicine initiatives. Ultimately, AI aims to bridge healthcare disparities and improve patient outcomes across the country.
What is the most used generative AI? Pinpointing the single *most* used generative AI is tricky, as usage metrics are often proprietary. However, models like ChatGPT (powering many platforms) and those powering image generation tools (like Midjourney or Stable Diffusion) currently boast incredibly high user numbers and broad applications. The "most used" really depends on how you define usage – by individual users, API calls, or overall impact.
What is generative AI for disease detection? Generative AI in disease detection uses machine learning to create new medical data – like synthetic images or patient records – to augment existing datasets and improve diagnostic models. This helps overcome data scarcity limitations and biases, leading to more accurate and robust disease detection tools. Essentially, it's using AI to generate helpful training data to better detect illness. This allows for better model training and potentially earlier diagnosis.
What type of AI model is used for generative AI? Generative AI isn't tied to one specific model type; it's an umbrella term. Many different architectures, like large language models (LLMs) and generative adversarial networks (GANs), can be used. The choice depends on the desired output (text, images, etc.) and the specific application. Essentially, it's about the *capability* to generate new content, not a single underlying algorithm.
What is generative AI in medical imaging? Generative AI in medical imaging uses powerful algorithms to create new medical images or enhance existing ones. It goes beyond simple analysis; it can, for example, generate synthetic datasets for training other AI models or create more detailed images from incomplete scans, improving diagnostic accuracy. This ultimately helps doctors visualize and understand medical conditions more effectively and efficiently. Essentially, it's like having a highly skilled image technician working alongside radiologists.
Does generative AI use NLP? Yes, generative AI heavily relies on Natural Language Processing (NLP). Think of NLP as the AI's understanding of human language – allowing it to process, interpret, and generate text or speech. Without NLP, generative AI wouldn't be able to understand your prompts or create coherent responses. It's a fundamental building block.
Which industry is likely to benefit the most from generative AI? Generative AI's biggest boon will likely go to industries dealing with large volumes of creative or data-driven tasks. Think content creation (marketing, media), design (architecture, fashion), and drug discovery – areas where AI can automate complex processes and unlock new levels of innovation far exceeding human capacity alone. Essentially, any field limited by human time or imagination stands to gain the most.
What is the size of AI in healthcare market in India? India's AI in healthcare market is experiencing rapid growth, though precise figures fluctuate depending on the source and metrics used. It's a multi-billion dollar sector projected to expand significantly in the coming years, driven by increasing data availability and government initiatives. Expect considerable variation in market size estimates depending on whether it includes software, services, or hardware components.
