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MedTech
6 minutes read

Generative AI in Healthcare: The Benefits and Risks 

By Robert Kazmi
Generative AI in healthcare
By Robert Kazmi
MedTech
6 minutes read

As Artificial Intelligence technologies gain popularity and increased visibility thanks to tools like ChatGPT, healthcare industry experts are examining ways to use generative AI in healthcare. 

Many believe that generative AI can be effectively used to improve health outcomes and patient care. Still, healthcare professionals also worry about the potential errors and other ethical concerns generative AI raises. 

There is no doubt that generative AI tools are here to stay. However, healthcare organizations will have to weigh the benefits and risks of using AI technology and determine if there are practical and ethical ways to utilize Artificial Intelligence. 

This post will examine generative AI in the healthcare industry. We will explore the potential benefits and risks of generative AI in this capacity to help healthcare professionals get a complete sense of the discussion. 

Artificial Intelligence and the Healthcare Industry 

The use of AI technology is already pretty commonplace in healthcare organizations and health systems. AI is used to analyze large swaths of patient data to help improve service offerings and patient outcomes. 

Healthcare providers use AI tools to assist surgeries, analyze imaging, and monitor treatments. However, generative Artificial Intelligence is like nothing seen before in the healthcare industry. 

Generative AI solutions have immense potential to drive innovation and improve the quality and delivery of medical care. However, like any new technology, generative AI technology also poses risks and challenges that are not fully understood. 

You are not alone if your healthcare organization is interested in generative AI. The University of Kansas Health System and the University of Pittsburgh Medical Center plan to roll out generative AI to their facilities. 

Therefore, this is the time to evaluate the impact of generative AI algorithms on the broader healthcare ecosystem and weigh the benefits and risks. 

The Positive Impacts Generative AI Can Have in Healthcare 

Generative AI has incredible potential to be a positive force in the healthcare industry that radically changes and improves patient care and outcomes. Generative AI can radically change healthcare, especially when combined with the essential features of a care coordination software. Integrating generative AI ensures an optimized workflow for patient care and data management, fostering better health outcomes.

From a health insurance perspective, generative AI can simplify the explanation of benefits notices and other documents sent to patients. In addition, these tools can be used to write prior authorization request forms and expedite the entire process, getting patients the care they need quickly. 

Medical providers can use generative AI to rewrite patient conversations in a standardized format that can be easily stored, shared, and analyzed. In addition, generative AI can help providers interpret novel, complicated medical cases. 

When integrated with EHR systems, generative AI can improve patient communications and respond to messages promptly. Generative AI can also prompt medical databases instead of relying on a data scientist to parse and analyze data. 

Healthcare and IT experts are also researching ways generative AI can effectively summarize a patient’s medical history and more effectively translate patient-facing documents to improve health literacy for individuals who may not speak or read English very well. 

Currently, medical providers and doctors are most excited about generative AI’s ability to streamline the note-taking process and free up more time to spend with patients and provide care. ChatGPT4 has begun integrating into medical documentation products like Nuance, which Microsoft owns. 

The Potential Risks Generative AI Poses in Healthcare

Generative AI comes with a lot of positive potential, but risks are involved with using this technology and trusting it too much. It is important to note generative AI models and tools make mistakes; they are not infallible

There are serious concerns about using generative AI in a field like medicine, where mistakes can have devastating consequences for patients. One of the most pressing issues surrounding this technology is the “black-box dilemma.” 

Essentially, how can you trust the results of a generative AI system if you can’t see how it arrived at them? One of the most challenging aspects of this problem is that companies like OpenAI, the developer of ChatGPT, don’t publicly disclose the training data used. 

Tools like ChatGPT are so effective that they fool users into thinking that they are reasoning or thinking for themselves when in reality, tools like ChatGPT are just hyper-sophisticated large language models that predict the next string of words that make the most sense in the given context. 

If generative AI is going to be used in a medical setting to make a diagnosis or provide a treatment plan, it is crucial for doctors and other staff to be able to understand the reasoning, and the truth is generative AI is incapable of reason since they are just outstanding Machine Learning models and not sentient beings with actual thought. 

Furthermore, even the most highly sophisticated Machine Learning models, like ChatGPT4, have serious issues with accuracy. There are countless examples of ChatGPT4 “hallucinating” or providing fictional information or factually inaccurate information. 

The most high-profile example of generative AI hallucination occurred recently when a New York lawyer used ChatGPT to write a legal brief. ChatGPT created fictional cases to cite as legal precedents in the brief. 

Cases like this make people uncomfortable, and rightfully so, about integrating this technology in healthcare settings. An additional concern for many people is bias. Generative AI can exhibit bias if the training data or implementation of said data is biased. 

Since the creators of these tools are so secretive about the training data and training methods used for generative AI, there are valid concerns about bias. 

Finally, the most important issue from a legal perspective is accountability. Who is responsible for the oversight and accountability of generative AI? Is it the developer, like OpenAI? Is it the doctor or health organization using it? 

Significant accountability issues have not been adequately addressed for many people concerned about the rapid implementation of these tools. 

Final Thoughts 

Generative AI has significant potential for good, and, to be fair, it also has significant potential for bad or negative outcomes. Health organizations interested in this technology should always have human oversight to ensure this technology is used and implemented responsibly. 

If you want to learn more about the impact of generative AI in healthcare, contact an experienced MedTech development partner like Koombea

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