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.
Table of Contents
- How Generative AI for Healthcare is Helping with Costs and Medical Administration
- A Generative AI Startup that Received $44 Million in Funding
- Synthetic Patient Data Generation will Help Ease Privacy Concerns for Administration
- Generative AI Can Speed Up Administrative Task Automation
- How Generative AI for Healthcare is Revolutionizing Medical Training
- US Universities are Creating Medical Simulations with Generative AI
- Generative AI for Healthcare in Clinical Diagnosis
- AI’s Groundbreaking Success in Medical Diagnostics
- Accelerating Drug Development with Generative AI for Healthcare
- Pharma’s Strategic Partnerships with Generative AI Startups
- Ethical and Regulatory Challenges with Generative AI
- The Need for a Trustworthy Partner in Navigating Generative AI
- Kanerika – Advancing the Future of Healthcare with Generative AI Implementation
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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:
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.
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.
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 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.
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.
Generative AI in healthcare is used to extract specific information from unstructured data, such as organizing patient timelines chronologically, transcribing patient visits into structured notes in real-time. Some other applications include generating synthetic patient data for research and training, automating administrative tasks, and accelerating drug development.
Generative AI models for healthcare include algorithms that can generate synthetic patient data, simulate medical scenarios, analyze medical images for diagnostic purposes, and transform low-quality medical scans into high-resolution images. Examples mentioned are GANerAid, an AI-driven model designed to create synthetic patient data. Another example is Generative Adversarial Network (GAN) models used for medical imaging enhancement.
Some of the use cases include: creating patient timelines by extracting medical information from unstructured data, automating administrative tasks such as data summarization and claims processing, simulating medical scenarios for training, and diagnosing diseases by analyzing medical images.
Generative AI refers to algorithms and models that can generate new data samples that resemble a given set of data. In healthcare, generative AI is set to revolutionize various aspects, from administrative tasks to clinical applications. It can streamline processes, enhance medical training, aid in accurate clinical diagnosis, and accelerate drug development. Its potential in healthcare is seen as unlocking a slice of an unrealized $1 trillion improvement. It offers capabilities that can save significant costs and improve patient outcomes.
Currently, virtual assistants and predictive analytics are some of the most commonly implemented AI technologies in healthcare. Companies like IBM and Microsoft have researched extensively into creating these healthcare AI technologies for hospitals and clinics.
Generative AI is making headway in healthcare by transforming traditional processes and introducing innovative solutions. It is aiding in the creation of synthetic patient data for research. Thus, providing scalable datasets for training machine learning models, and fast-tracking the development of diagnostic tools and treatment plans.