Generative AI is a deep-learning model capable of producing different types of high-quality content based on the data used to train those models. Unlike other AI that follows predefined rules and performs assigned tasks, generative AI can produce new and unique things.
As such, it is one of the main drivers of the healthcare tech revolution. GenAI techniques and models are applied to the various subfields to reshape the management and delivery of care on a macro scale. AI systems leverage vast amounts of data to generate new insights, predict outcomes, and help devise solutions to pressing industry challenges. There is a huge potential to transform everything from diagnosis, treatment, personalized medicine, and drug discovery to billing and automation of operational processes.
The latest Deloitte report indicated that 75% of healthcare companies are already experimenting with GenAI, with the majority of leaders in the field saying that they see the potential to improve efficiencies and enable faster decision-making.
According to a recent report, the global generative AI in the healthcare market is projected to surpass $21 billion by 2032. Everyone from hospital administrators, managers, and insurance companies will leverage AI across different aspects of healthcare delivery and management, streamlining patient care and administrative processes.
The mission is to empower healthcare professionals with the tools and assistance they need to deliver the highest level of patient care. For processes that require human contact, generative AI will act as an assistant to the medical professional, helping augment the traditional processes. It should take the burden of sorting through extensive logs and reduce the need for manual tasks by summarizing the data. What is more, it addresses labor costs, ensuring the sustainability of healthcare organizations.
There are multiple GenAI models, but the two most commonly applied in the healthcare context are generative adversarial networks (GANs) and Large Language Models (LLMs). These advanced AI techniques offer unique capabilities that enhance various aspects of medical research, diagnostics, and patient care.
Generative adversarial networks represent a machine learning model capable of creating new different types of realistic data. While more advanced versions include additional performance-enhancing mechanisms, in their basic form, GANs consist of two competing parts: the generator, the part that makes fake data and passes it on to the discriminator to check whether the data is real or fake.
Unlike traditional data generation methods that rely on specific mathematical models to generate data, GANs rely on this competitive approach to learning, which significantly reduces the need for data labeling.
The “competition” part is known as the “adversarial training”, the process in which the generator tries to make realistic data, and the discriminator attempts to get better at spotting the fake data. Over time, both get better at their tasks.
GANs are widely used to help train other machine learning models, especially when data is limited. It has also become a lot better at producing new images and data with specific details, as well as adapting to new environments. Most importantly, GANs are capable of creating synthetic data that looks like real data but without personal information, which addresses privacy concerns.
Large Language Models (LLMs) are sophisticated AI systems designed to understand and generate human language, making them useful for various text-related tasks. To boost the performance of specific tasks, LLMs utilize different techniques, such as transfer learning and fine-tuning, and they are built on transformer architecture, which excels at understanding the context of words within a sentence. To help the model make predictions, transformers rely on an ‘attention’ mechanism that evaluates the importance of words in a sentence, allowing the model to make accurate predictions based on the entire sentence history.
LLMs are trained on vast amounts of text by predicting the next word in a sentence and adjusting based on the accuracy of their predictions. This iterative learning process enables them to continually improve their language understanding and generation capabilities. As a result, Large Language Models can perform a wide range of language tasks, such as generating coherent responses, summarizing text to enhance other models, and classifying text to analyze large datasets or identify issues, all without additional (re)training.
GenAI has made significant strides in healthcare by tackling critical challenges inherent in current practices. For this section, we selected 8 benefits that we believe best demonstrate the transformative impact of generative AI in the healthcare domain.
Generative AI is used to assist radiologists in reading X-rays, MRIs, and CT scans to accelerate and improve the accuracy of the diagnostic process. GenAI models are trained with diverse patient data to learn to identify the earliest signs of different health conditions and biomarkers indicative of certain disorders, as well as predict disease progression.
There are two key AI techniques used to enhance medical imaging: generative adversarial networks (GANs), expected to revolutionize medical image analysis by providing new ways to generate, analyze, and transform medical images, and variational autoencoders (VAEs) that are capable of reducing noise, thus proving useful for accurate image segmentation, classification, and registration.
The GenAI impacts the quality of medical imaging through:
Traditionally, drug development has been a decades-long, billion-dollar investment that was nothing but a trial-and-error process with a disappointingly high failure rate.
GenAI is the catalyst pharmaceutical researchers need to help them analyze molecular structures and biological data to generate chemical compounds with desired properties. It is used to analyze extensive datasets efficiently, quickly identify promising samples, optimize molecular structure, and predict possible molecular interactions and side effects. GenAI can also identify disease-specific biological processes and pinpoint new drug development opportunities that would contribute to more effective treatments.
The ultimate goal for GenAI in pharmaceutical trials is to shed costs and years off clinical trials and accelerate the time it takes to read the pre-clinical phase. In the process, it will optimize molecular structures, predict potential side-effects and drug interactions, maximizing patient safety by reducing associated risks in pharma interventions.
Generative AI analyzes and learns from patient records, MRI and CT scans to identify disease-specific patterns. Relying on GANs, GenAI can create realistic medical images to train other AI systems and improve diagnostic accuracy or expand medical datasets.
Integrating LLMs, GenAI can read and interpret medical records, analyze and process various electronic health records (EHRs), including lab results and doctor’s notes, to provide a comprehensive overview of a patient’s health. LLMs can assist in medical decision-making by suggesting possible diagnoses, recommending tests, and proposing treatment plans, even without specialized medical training. Despite their capabilities, LLMs cannot fully replace the clinical judgment and experience of healthcare professionals.
GenAI algorithms synthesize large volumes of patient data to identify patterns, predict disease development, and help devise personalized treatment strategies. The algorithms comb through electronic health records, clinical notes, and genomic information to learn about the patient’s clinical history. Generative AI models can even process data collected through wearables to monitor health status and identify anomalies.
By analyzing large datasets in a very short amount of time, they can uncover patterns and correlations that would take diagnosticians a lot longer to deduce. This enables early intervention and prompt adjustments to a treatment plan for the best patient outcome. As a result, clinicians move away from administering treatments based on broad population data and set up a plan tailored to a patient’s medical history, genetic profile, and real-time health status.
In the field of mental health, in particular, AI tools are utilized in cognitive behavioral therapy (CBT). GenAI helps generate personalized scenarios and responses that help patients manage conditions by changing their thoughts and behaviors. For instance, an AI tool can simulate anxiety-inducing situations and guide patients through coping mechanisms, offering a controlled environment for practicing skills and potentially enhancing mental health outcomes.
A 2017 study revealed how physicians spend time in ambulatory practice in the age of EHRs and patient portals: only 40% is spent on face-to-face patient encounters, while as much as 40% is wasted on what is known as “desktop medicine”, and 20% on activities not logged in the EHR.
GenAI can automate tedious administration to reduce administrative burdens and enhance operational efficiency, providing faster and more accurate medical services. We are seeing an increasing number of healthcare centers implementing generative AI models to aid in:
Generative AI has the potential to revolutionize rehabilitation and neurotechnology, particularly in restoring speech or movement capabilities by interpreting brain or nerve signals. For instance, in people diagnosed with tetraplegia, generative AI is used to power microchips implanted in the brain to reconnect the brain with the spine, helping them regain movement in the head and experience sensations.
One inspiring example is a team of researchers from the GrapheneX-UTS Human-centric Artificial Intelligence Centre at the UTS who have developed a portable, non-invasive system that decodes silent thoughts and converts them into text. This innovation allows individuals who are unable to speak due to illness or injury to communicate through a machine.
Generative AI helps devise lifelike healthcare scenarios to enhance training and skills development in a risk-free environment, often through integration with VR and AR devices. AI generates a wide array of virtual patient cases based on diverse medical conditions, patient demographics, and clinical scenarios, simulating treatment scenarios for rare or complex cases without risk to patients.
These simulations enable personalized learning experiences for both novice and experienced medical professionals, tailored to individual learning styles, paces, and needs. AI-driven scenarios prepare healthcare providers for unexpected situations and offer a space to advance their communication skills, including delivering difficult news to patients.
Moreover, AI training tools monitor progress, offer feedback, and contribute to curriculum improvement efforts.
The concept behind AI chatbots and virtual assistants was to offer universal access to unobstructed communication with healthcare providers. But we are way past that point.
Today, both large healthcare centers and small practices are integrating virtual assistants with electronic health records to enhance patient care by:
But to harness the potential of GenAI, healthcare professionals have to address the inherent risks it may pose.
GenAI models have been shown to inherit biases against underrepresented groups when the datasets are not properly trained, validated, or diverse enough. Achieving fairness requires proactive measures to identify and rectify those biases, as they may significantly impact and skew outcomes.
This includes incorporating ethical considerations into algorithm design and development, establishing rigorous protocols, validation, and continual testing to mitigate biases that may emerge as models are deployed in real-world healthcare settings.
Using generative AI in healthcare involves feeding it large amounts of detailed and often sensitive patient information, which raises significant privacy and security concerns.
Organizations must establish and adhere to strict data protection regulations and establish clear data-sharing policies to make sure the information used for AI purposes doesn’t pose privacy risks. Healthcare centers must implement strict security measures to safeguard patient data from unauthorized access, thus upholding patient trust in the healthcare system. These measures may involve data anonymization and aggregation, synthetic data generation, regulatory compliance, ethical considerations, and regular AI system audits to detect potential security vulnerabilities.
A large number of healthcare providers operate with legacy systems that cannot integrate with available AI solutions. Even if a compatible system is available, implementing AI may require staff (re)training and cause downtime, disrupting everyday workflow. These challenges often deter healthcare professionals from exploring the AI potential and sticking with what works just fine.
To minimize workflow interruptions, healthcare organizations should assess their existing IT infrastructure for AI compatibility and provide training and support for all staff members.
Weaving GenAI into the fabric of your organization can help you reinforce and reinvent your business and gain a competitive edge. But tread lightly.
Successful integration requires careful planning, clear objectives, and strategic implementation. And here’s how we’d do it.
Rule number one: align the AI solution with your business strategy. Start by gaining a clear understanding of the chosen technology and how it fits your specific use case.
Analyze the potential implications of generative AI on your organization, weighing both benefits and challenges. Establish clear ethical guidelines and ensure transparency across the organization. Most importantly, plan for the necessary training and onboarding to educate key personnel through courses, seminars, and workshops, ensuring advanced AI literacy throughout the organization.
Begin by clearly defining how the generative AI model will add value, and keep in mind that it might lead to job displacement and raise the need for organizational restructuring.
Build an operational framework that offers opportunities for employees previously responsible for routine tasks to transition into new areas. Create learning materials and provide pathways for acquiring new skills and advancing into more complex roles.
Assess existing infrastructure to determine if the existing software and hardware resources are sufficient to support your AI objectives. Address key technical questions regarding how the model will be built, deployed, and monitored.
The choice of the foundational model will depend on your specific use case and will have to meet security requirements. Consider factors such as:
Additionally, explore potential strategic partnerships to enhance AI capabilities across different data sourcing, model development, support, and security.
Once you identify business areas where GenAI could provide value, whether enhancing existing services or exploring new growth opportunities, assemble a multidisciplinary team to partner with data scientists, analysts, engineers, and business strategists.
Gather relevant datasets for AI training, ensuring diversity and compliance with data regulations. Next, clean and preprocess that data to optimize training accuracy, adjusting computational resources based on task complexity. Fine-tune the model for optimal performance and validate it using separate datasets to assess performance, accuracy, and reliability.
Finally, conduct a small-scale pilot project to evaluate GenAI’s effectiveness in your business context. This pilot will shed light on any operational requirements, uncover scaling opportunities, and point out potential challenges that might occur during the large-scale implementation.
The final stage involves the integration and monitoring of the GenAI model to ensure compliance with the EHR and other commonly used tools. This entails setting up an end-to-end view of your organization’s tech and business processes, defining user stories, and using GenAI to improve performance previously obstructed by human oversight.
Tackle the challenges we previously talked about – address bias and enforce security protocols and compliance with healthcare regulations, all while maintaining open communication channels with the stakeholders to ensure maximum transparency in GenAI usage.
And now that the setup is finished, the work can truly begin.
Continuously monitor and assess risks to routinely check for the overall effectiveness of the model. Only the right setup can offer transformational value and help you realize at-scale cost-effectiveness and process efficiency.
And keep track of generative AI advancements, as they will lead to greater integration and wider applications of AI tech in healthcare.
Photo by Google DeepMind
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