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In November of 2022, ChatGPT 3.5 was released and broke a record in the first two months gaining over a hundred million active users. Two years later, generative AI services have become a commonly used technology applicable both in personal life and for business purposes. But what is generative AI in healthcare and can it be trusted? While relying on AI to write an email is a low-risk endeavor, entrusting your health to a machine raises concerns.
In this article, we’ll explore the numerous benefits of this groundbreaking technology, address justifiable worries, and discuss the most promising healthcare cases.
Generative AI (GenAI) is a type of artificial intelligence capable of generating textual, visual, and auditory content. From summaries, essays, and marketing materials to images, videos, and music, current GenAI use cases are quite vast. Although the technology is in no way new, the recent introduction of generative adversarial networks (GANs) and transformers have allowed large language models (LLMs) to evolve drastically.
Data science in healthcare has allowed developers to train models faster and with more data unlocking truly remarkable capabilities. Despite the early stage of the development causing some issues and inconsistencies, the technology will only get better from now on. And even using its current capabilities, companies across industries can optimize their workflow, automate various tasks, and offer personalized patient care.
In healthcare, generative AI models can aid clinicians in diagnosing diseases, offering personalized medicine, advancing drug discovery, and much more. Although the decision-making is still on doctors, numerous administrative and data-analyzing functions can be handed over to a machine.
Research from McKinsey has shown that GenAI adoption rates have skyrocketed in the last year predicting the ever-growing implementation across the sectors. And the data within the healthcare industry echoes the same statistics: The vast majority of healthcare providers either have already adopted AI algorithms or are planning on doing so very soon. Moreover, the same survey showcases that healthcare organizations have already yielded returns on their investments.
From drug discovery and preventive medicine to risk prediction and task automation, generative AI in healthcare can revolutionize the entire industry. Before we dive into concrete applications of generative AI in healthcare, let’s take a look at its potential advantages and disadvantages.
AI’s ability to process and analyze colossal amounts of medical data enables the development of personalized medicine. From tailored treatment plans and medications to early detection, patients can greatly benefit from the enhanced precision of GenAI tools. In addition to this, AI algorithms can aid healthcare professionals in making fewer mistakes, leading to improved patient outcomes.
Generative AI in healthcare can also enhance the understanding of health conditions, from their origins to how they affect the human body. Sometimes, medical professionals rely on empirical data showcasing that a certain treatment works without fully comprehending why. For example, even though Tylenol has been a household name for decades, researchers are still not sure how exactly it relieves pain. The newly unlocked capabilities of AI models can help us strengthen our understanding of existing and new drugs.
Finally, AI tools in healthcare can reduce unnecessary tests and procedures, eliminate numerous time-consuming administrative tasks, and cheapen drug discovery making the entire healthcare system more affordable.
First and foremost, administrative tasks have long been the number one cause of clinicians’ burnout. Reducing and potentially eliminating these routine operations will allow doctors more time to spend with their patients, upgrade their skills, and have a better work-life balance.
Furthermore, generative AI in healthcare enables improved decision support allowing physicians to enhance their diagnostic capabilities. AI algorithms can provide doctors with more detailed medical data and analyze genetic information, offering a more precise triage. On top of this, doctors will be able to communicate their findings to patients in the best possible way.
Lastly, clinicians will be able to stay up to date with the latest discoveries and studies through personalized, AI-curated content. They can even practice their newly acquired skills with virtual simulations to hone in on their expertise.
Hospitals, clinics, and other healthcare organizations will benefit from optimized workflows and increased operations efficiency. Having doctors and nurses who have more time and energy to spend with patients, as well as enhanced resource allocation, will aid hospitals in working faster and better. They can also reduce costs on numerous tasks by outsourcing them to GenAI systems.
The recent pandemic has revealed a lack of preparedness when it comes to health crises. Numerous countries across the globe were not ready to address this public health threat leading to deaths and complications that could have been prevented. Generative AI in healthcare can offer a more effective intervention to limit outbreaks and contain the viruses before they spread to other nations.
Additionally, the overall improvement in population health will yield more content and productive citizens and reduce the expenses of the public healthcare sector. Government officials will also be able to rely on data to make policy decisions that affect millions of people.
AI-powered clinical trials will help pharma organizations reduce costs and accelerate drug development. Drug discovery is notoriously long and expensive due to a large number of regulations and protocols. Generative AI in healthcare can aid researchers in optimizing these processes and delivering new groundbreaking medications faster.
Despite the groundbreaking potential of generative AI in healthcare, there are some caveats that come with the technology. Let’s take a closer look at what can go wrong if we don’t consider ethical and legal implications.
According to Statista, privacy concerns are at the top of potential dangers of AI, and for a good reason. Companies have already used publicly available information—including articles, books, songs, paintings, and videos—to train their models, igniting a lot of controversy. Accessing HIPAA-protected patient data to improve their systems can create additional privacy concerns.
Even though ensuring patient confidentiality can be a challenging task, it’s essential to build ethical AI systems and foster trust with the user base. On top of that, cyber crime is also on the rise, posing another threat to the safety of sensitive patient information. If an AI training company gets hacked, its datasets can be potentially exposed to malicious actors.
To build and fine-tune AI applications, trainers require high-quality data. The lack of diverse and prime datasets poses a threat to the future of AI in healthcare. Not only is quality data a limited source in general, but it’s hard to come by in the medical sector due to countless privacy regulations. Laws that were set in place to protect user data also make it hard to access for training AI.
Potential biases occur precisely when datasets are heterogenic or poor-quality. For example, if the vast majority of your datasets contain information about white patients, this can skew the AI’s understanding of how a certain disease affects people of color. This inequality can lead to biased or false outputs and wrong diagnoses or treatments.
To combat the numerous data privacy and quality issues, governing bodies are in urgent need of creating a healthcare compliance solution. The problem is that legislation often lags behind technological progress, allowing hackers to take advantage of temporarily unregulated spaces. In order to get to the next step and allow generative AI in healthcare to deliver on its promises, we need solid regulatory frameworks to protect patients, medical workers, and AI developers themselves.
Healthcare organizations are still quite behind on the technological progress operating with legacy software. To facilitate a smooth transition, hospitals need to upgrade their existing IT infrastructure and train their staff on the latest tools. Not only is it a costly undertaking, but it will also take a long time to execute across clinics.
Finally, accountability is a big point of contention when it comes to AI-assisted decision-making. Whenever a mistake occurs, who is responsible for patient outcomes? Is it healthcare professionals who made the final decision, the AI developer who trained the model, or the AI vendor who sold the software to the organization? Before we find a way to answer this question and create new protocols and frameworks, the development of generative AI in healthcare will be halted.
AI has been used in numerous medical cases according to a recently published study investigating preliminary evidence of the use of generative AI in healthcare.. Among the generative AI use cases in healthcare are disease detection, diagnostics, and screenings across specializations including radiology, cardiology, diabetes, and others. Their findings point out that GenAI is mainly utilized to improve diagnosis accuracy and precision. In this next part, we’ll try to figure out how generative AI can be used in healthcare.
Traditional clinical decision making involves interviewing and assessing the patient’s health, followed by tests and consulting with other clinicians to develop the proper diagnosis. Healthcare professionals review electronic health records to learn the patient’s historical data and gain a comprehensive picture of their health condition. The issue with this approach lies in the widespread biases within medical research. Historically, women and people of color were left out of clinical trials, leaving a large gap in knowledge. What works for one demographic may not work for another.
AI-powered diagnostics can encompass a bigger database with diverse data that includes patients across races, genders, sexualities, ages, and comorbidities, making diagnostic efforts more precise. By analyzing genetic data and medical history, AI systems will be able to identify patterns that yield more accurate results.
Among other prominent generative AI in healthcare examples, medical image analysis allows doctors to study the internal structures of the body with technologies like X-rays, MRIs, and CT scans. But performing these scans is only the first step—the most important part is to identify the signs of various health conditions. And that’s where generative AI excels. It can process and analyze myriads of images to learn the patterns and swiftly identify anomalies.
Moreover, AI algorithms can uncover new biomarkers and revolutionize preventive medicine as a whole. A recent study from a clinical trial in Sweden shows that radiologists who use AI in diagnosing have a 20% higher chance of catching breast cancer than those who operate autonomously.
On top of this, generative AI can enhance the quality of existing images to improve visibility and help doctors detect early signs. By reducing noise and increasing resolution, these systems can provide enhanced details in the images.
Earlier this year, a team of chemists was awarded the Nobel Prize for decoding the structure of proteins and creating new never-seen-before ones. AI algorithms opened new ways for researchers to analyze and produce data, including proteins that can be helpful in drug discovery and beyond.
This breakthrough is likely only the beginning of advanced and accelerated drug development that will aid us in discovering new chemicals, understanding diseases, and creating cures. AI can help us gain a more nuanced picture of genetics and pathologies, paving the way to drug discovery across the specialties.
AI-powered systems can also cut the time it takes to test a new drug. Traditionally, the development of a new medication takes up to 15 years and costs upwards of billions of dollars. Generative AI models can reduce the timeline by offering more precise modeling, identifying potential drug candidates for trials, and predicting potential health risks.
Physicians and other medical professionals suffer from burnout at an extremely high rate. The biggest cause for their condition is the ever-increasing load of administrative tasks. From extracting patient data from medical records and setting up new appointments to maintaining clinical documentation, doctors have to juggle a lot of routine and time-consuming activities.
Automation of such tasks will also save costs and increase patient care experience. Instead of spending the national average of 13 to 24 minutes on each patient, medical workers will be able to devote more energy to each case. Not only will it improve their diagnostic efforts, but also foster a deeper bond with their patients.
Billing procedures are another time-consuming part of doctors’ daily to-do lists. From insurance verification and claims processing to payment posting and follow-ups, billing activities require a lot of time and financial expertise. Generative AI in healthcare can take on some if not all of these tasks allowing physicians more time for themselves and their patients.
It is known that medicine heavily relies on statistical analysis rather than data. If a drug works on paper, it doesn’t mean it will work the same on a patient. This is due to the overall complexity of our bodies which contain endless processes and cycles that can be hard to account for. Double-blind studies allow researchers to test the drug on a number of different patients while considering external factors, biases, and psychosomatic effects. Unfortunately, some drugs will not yield positive results for certain patients which requires clinicians to come up with alternative ways.
Tailored medicine relies on medical records, genomic data, and lifestyle choices to create personalized treatment plans for each patient. This approach can significantly enhance the efficacy of treatments and improve patient outcomes.
Especially in conjunction with wearable technology, doctors can now collect an immense amount of valuable data that helps them assess patients more accurately. This technology can also strengthen the field of preventive medicine. The earlier a disease is detected, the better the chances for a full recovery. Additionally, early intervention is usually considerably cheaper and faster compared to later stages making it a win-win for both patients and clinicians.
Since the further development of generative AI in healthcare largely relies on the amount and quality of data fed to the model, the ability to generate synthetic medical data is pivotal for the field. Through learning on existing data, AI algorithms can alter existing medical datasets to generate usable data. Additionally, this newly created synthetic data is not subject to privacy laws, as it was not taken from a real-life patient, making it cheaper and easier to work with.
Synthetic medical data can also help us bridge knowledge gaps and increase diversity and representation across datasets. Not only does it allow healthcare professionals to help their patients better but also increases the trust among marginalized communities. In other words, generative AI can aid the healthcare industry in making healthcare more accessible and inclusive.
A recent article from the New York Times reveals how doctors are already relying on generative AI to communicate with their patients. Despite being a new trend, it’s likely to continue. Virtual assistants utilize AI algorithms to create personalized messages, send reminders, set appointments, and notify patients about outstanding bills.
Medical chatbots and assistants can improve patient aftercare by reducing treatment interruptions. After discharge, patients often fail to adhere to treatment plans, either due to miscommunication, high costs, lack of symptoms, or general mistrust towards medical professionals. Well-timed and tailored messages can help bridge that gap and minimize adverse outcomes.
The recent pandemic has made us a lot more conscious of the lack of preparedness amid a catastrophic health crisis. Now we can leverage generative AI to predict potential outbreaks and revise plans to action before they occur. These models are also used in identifying new antibodies that will be integral in developing new vaccines to combat the corresponding virus. Even though COVID-19 vaccines were delivered in an unprecedentedly short time, discovering them in advance will yield enormous benefits for our healthcare systems.
Generative AI systems can also help us combat misinformation about diseases and treatments. Amidst the ever-growing anti-vaccine movement, understanding public concerns and finding a way to respectfully and comprehensively address them can literally save lives.
Among other pivotal generative AI in healthcare use cases is medical training. Studying medicine is notoriously expensive, both in terms of finances and time. Supplementing medical education with AI systems will reduce the overall bill and produce better-trained clinicians. In combination with virtual reality and augmented reality (VR/AR), AI models can walk doctors through numerous scenarios to practice their skills in a simulation.
Aside from saving time and money, AI-powered medical training can also generate a wider spectrum of scenarios that cannot be done in a traditional setting. Additionally, VR/AR simulations feel a lot more real and urgent, which can help trainees feel fully immersed.
In the last few years, we’ve heard of several cutting-edge advancements in neurotechnology, including brain-computer interfaces, neural prosthetics, neurorobotics, and others. This field is largely dedicated to restoring lost capabilities and allowing patients more autonomy and agency. For instance, researchers have created microchips that can be implanted into the human brain to reconnect with the spine, restoring their mobility.
Another one of the generative AI in healthcare examples is language translation. Especially in multilingual areas or medical facilities located in large cities with high tourist traffic, having the ability to seamlessly interact with any patient is key. Relying on natural language processing (NLP), LLMs have become pretty reliable in language translation, including complex medical vernacular. Moreover, modern voice-activated chatbots allow for swift and flawless communication between doctors and patients.
Amid the pandemic, telemedicine app development has taken over the healthcare industry. Although less pertinent right now, telehealth applications remain important for people with limited mobility, elderly patients, and those who live in remote areas. Paired with IoT-powered wearables, AI algorithms can collect and analyze vital patient data and transmit its findings to the physician. Access to real-time data can improve the accuracy of the diagnosis and treatment as well as prevent the condition from exacerbating.
Despite the fact that generative AI is still quite new, startups are emerging to create the next best cutting-edge healthcare solution. Microsoft is planning to upgrade its electronic health records software, Epic, to provide healthcare providers with AI capabilities. Google has developed a new LLM that focuses on medical data to arm hospitals with a system that helps diagnose complex conditions and perform administrative tasks.
Smaller companies are also joining the trend in creating tools that offer a deeper analysis of medical images and streamlines various medical processes. For instance, Artsight, a healthcare IT solutions company that released a tool that enhances operational efficiency in a hospital setting. Suki is another app development company that provides software that automatically generates clinical notes by listening to a conversation.
Despite all the potential benefits of generative AI, many people remain skeptical about the safety and accuracy of its outputs. Biases, limitations in quality data, AI black box, and lack of legislation raise some concerns about generative AI use cases in healthcare. In order to ensure a safe and long-lasting future of artificial intelligence, consider the following best practices:
Generative AI offers an enormous range of potential use cases and can transform healthcare completely. However, a smooth transition requires battling a lot of challenges, including regulatory compliance, patient data privacy issues, training healthcare providers, and ensuring high-quality outputs.
If you’re interested in software development in healthcare and would like to build an AI-powered future, consider reaching out to NIX. We’re an IT agency with decades of experience in developing, modernizing, and marketing software solutions across industries. Our seasoned team of professionals will guide you through the digital transformation and help you leverage generative AI to improve your services.
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Generative AI is a type of AI that can produce new content based on the data it was trained on. In the healthcare industry, large language models (LLMs) are fed medical and patient data to identify patterns and comprehend complex medical concepts. Users can enter a prompt into the LLM while the machine outputs corresponding information in the form of text, images, audio, and videos.
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The most common biases in generative AI derive from the datasets that were used to train the model. Leveraging historical medical data allows AI models to extract valuable information. However, if the original dataset presents a skewed reality of the field, the machine will output skewed answers. The key is to train AI on diverse and high-quality data to minimize prejudices and biases.
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Currently, the primary generative AI use cases in healthcare include the analysis of medical images, automation of administrative tasks, and drug discovery. Emerging applications extend to neurotechnology, preventive medicine, personalized medicine, and others. The potential use cases are still not fully known or understood as the technology is relatively new.
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Personalized medicine is about developing a tailored approach and treatment plan for each patient. Integrating generative AI can help healthcare organizations identify specific differences in patients and leverage these findings to offer personalized medications and treatments.
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Healthcare systems can utilize AI capabilities to enhance medical imaging analysis. AI models are great at identifying patterns that humans cannot. Either due to complexity or the sheer amount of data, humans can’t come close to finding new connections among extensive datasets. By learning from billions of medical images, AI systems can identify similarities and draw meaningful connections, helping doctors in bettering their diagnostic efforts.
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Drug research and development can also be strengthened and expedited with the use of generative AI in healthcare. From creating new proteins and revealing more about our genetics to identifying potential drug candidates, AI models can significantly accelerate the drug development process.
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Current AI capabilities are eons away from operating fully autonomously. Applying generative AI in healthcare should be paired with a medical professional who can assess the accuracy of outputs, interpret patient data, and make an intelligent and data-driven diagnosis.
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Among the largest concerns are data privacy, quality of training data, and potential biases. Additionally, many healthcare organizations still rely on legacy software, making the integration quite difficult and costly. Another important concern is associated with the lack of up-to-date legislation that regulates how AI models are developed, tested, and distributed.
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