With generative AI, healthcare organizations could experience improved patient outcomes and greater efficiency of care. However, the healthcare sector must move forward with caution to ensure AI is used properly in this sensitive and critical industry.
Generative artificial intelligence (AI) is a technology that uses trained algorithms to produce new outputs in the form of data, text, images and beyond. The healthcare industry is ripe for change, and many healthcare organizations are looking to generative AI to drive innovation.
Professionals within the healthcare industry are continuously exploring the potential advantages of using AI. In this guide, we’ll discuss those potential advantages and the challenges associated with generative AI.
On a related topic: What is Generative AI?
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Healthcare and Generative AI: Ripe for Innovation
The healthcare industry is constantly evolving to meet the ever-changing needs of the population. However, there are some areas of healthcare that have required change for quite some time.
For example, the continued shortage of healthcare professionals such as physicians and nurses often results in consequences, including medical team burnout and inefficiencies in patient care. And factors such as COVID-19 and changing patient demands are making care options such as tele-health non-negotiable.
Of course, these challenges exacerbate other difficulties such as the consistently high costs of healthcare. To face these challenges head on, many organizations are gunning to implement technology such as generative AI.
With generative AI technology, hospitals, clinics and beyond may be able to streamline patient care, improve health outcomes and boost healthcare team morale.
To be sure, generative AI is a new concept in healthcare – so much so that even professionals in the field can’t fully predict how it will reshape patient care.
Also see: Generative AI Startups
Generative AI in Healthcare: Four Potential Advantages
The future of generative AI offers great promise and various advantages, from personalized healthcare for patients to predictive maintenance.
1. Personalized Patient Care Plans
Every patient is different. So in call cases, care needs to be tailored to fit their unique healthcare needs for the best outcomes. However, personalized care plan development requires teams to get to know patients on a deeper level by analyzing complex health data such as medical histories and genetics.
Technology such as generative AI and machine learning can simplify data analytics. For example, generative AI may be used to find patterns in patient health data that point to the potential development of chronic diseases. As a result, providers can develop care plans to help prevent these diseases.
2. Enhanced Medical Imaging
Medical imaging methods such as MRI, CT and PET scans are key components of patient care. They’re used to diagnose diseases and pinpoint critical injuries quickly. However, new technology such as generative AI can simplify the imaging process to help healthcare teams deliver faster results to patients.
In fact, medical imaging is one sector of healthcare where AI is well on the way to greater adoption.
For example, generative AI solutions already exist to reduce image noise for clearer scans. Other solutions can also use machine learning coupled with AI to reduce overall scan time. Another potential use case is using artificial intelligence and machine learning to automatically detect common abnormalities in patient images without human intervention.
The potential result of these capabilities is faster patient care, which is critical when time is of the essence.
3. Predictive Maintenance
Patient care requires the use of a wide range of medical devices, from critical defibrillators to complex MRI imaging devices. These devices must perform for the sake of diagnosing patient medical conditions and providing life-saving medical intervention.
Predictive maintenance can help prevent operating issues with this equipment by alerting medical teams of potential future failures before they occur. Generative AI may be used to quickly find patterns in large data sets that point to equipment failures.
As a result, medical teams can keep their equipment maintained so it’s available for medical intervention at all times, improving overall patient care.
See also: Predictive Analytics Best Practices
4. Administrative Support
Beyond patient-facing care, healthcare organizations require administrative support. For example, hospitals and clinics require key players such as medical billers and office administrators. Generative AI may be able to support administrative tasks to improve efficiency.
For example, generative AI can complete tedious, manual tasks that take time away from more important tasks. For example, AI can perform data entry, take patient payments, communicate to teams which patients are due for exams and much more.
Generative AI can also be used for the admin tasks physicians and other patient-facing individuals must complete. Of all the potential uses of AI in healthcare, this is an area seeing a higher degree of interest and investment.
For example, physicians are already using AI to document the details covered during patient visits in electronic medical records. As a result, doctors and nurses alike can spend more time with their patients and less time on manual tasks.
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Generative AI in Healthcare: Five Challenges of Using AI
While generative AI may have potential benefits for the healthcare industry, it isn’t without its challenges, including data inaccuracies, potential bias and a lack of AI governance.
1. Patient Privacy
Patient privacy and the ability to access it are heavily regulated in the healthcare industry. Federal laws such as HIPAA require healthcare organizations to protect sensitive data, including everything from social security numbers to personal health records.
HIPAA requires organizations to keep health records private and safe from unauthorized access. Generative AI algorithms add difficulty to these requirements as AI technology must access patient information and data to analyze it and provide insights.
Organizations must be very careful to avoid using patient information that they do not have permission to access. And any generative AI tool an organization uses must meet critical regulatory requirements.
2. AI Data Inaccuracies
Healthcare is one industry where decisions made based on inaccurate data can be severe. In fact, inaccuracies could result in patients experiencing lower-quality care or worse. Unfortunately, the large language models (LLM) used in generative AI are known to result in inaccurate content and data.
For example, LLMs can hallucinate or provide information that may sound accurate but is completely false. ChatGPT is one generative AI tool that’s been proven to do this at times. This means those using the data must still comb through it to spot errors before putting it to use, which may defeat the purpose of using AI in the first place.
Hallucinations and other inaccuracies must be prevented when patient health is at risk. And experts have yet to develop an AI tool with the level of accuracy required for this industry.
See also: Understanding the ChatGPT AI Chatbot
3. Lack of Governance
As with any technology, generative AI requires proper governance to ensure it’s used safely and effectively within an organization.
Unfortunately, AI governance is tricky for many situations, due to its complexity and other factors. We just don’t know enough about AI to fully govern its deployment and use.
A lack of governance can result in AI being misused within a healthcare organization, which may lead to serious consequences for medical teams and their patients.
See also: What Is Data Governance?
4. Bias & Other Ethical Concerns
Generative AI tools have been found to exhibit bias in various forms. This is partly due to LLMs being trained on existing biased datasets. This may result in outputs showing negative or discriminatory results regarding certain races or genders.
Unfortunately, in the healthcare industry, bias can lead to less-than-ideal care for patients belonging to certain groups. This challenge can be exacerbated by potential discriminatory outcomes delivered by generative AI algorithms.
Beyond bias, there are other ethical concerns organizations must consider, including the potential of job loss for healthcare employees.
5. Lack of Knowledge
It’s no secret that AI, in its many forms, is challenging to understand. And it’s often even more challenging to train.
For algorithms to work and outputs to be usable, generative AI tools must be trained to deliver the results healthcare organizations are looking for. This requires organizations to find knowledgeable talent that can do just that.
Ironically, while artificial intelligence may replace some jobs, there’s a talent gap when it comes to professionals skilled in developing, testing and training AI tools. An organization’s lack of AI knowledge can result in generative AI being used ineffectively.
See also: Generative AI’s Drawbacks: IP to Ethics
For generative AI to be truly useful in the healthcare industry, these challenges must be mitigated. Healthcare is critical to the health and well-being of those who call this planet home.
While healthcare is ripe for change, it isn’t an industry that should be subjected to generative AI experimentation. All of us, whether we’re patients or healthcare professionals, must be very careful in how we choose to move forward.
Bottom Line: Generative AI in Healthcare
It’s true that generative AI could have some positive effects on the healthcare industry. For example, it could provide physicians with the tools they need to deliver personalized care and ensure medical equipment is available for intervention at all times.
However, the lack of governance and knowledge, the potential for bias, resulting inaccuracies and privacy challenges should cause the healthcare industry to pause before diving in headfirst to generative AI. Healthcare organizations should tread carefully to protect their patients, staff and the industry as a whole.
Also see: What is Artificial Intelligence?