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The healthcare industry has been facing diverse challenges in the recent past. From clinicians experiencing burnout and diminishing profits to staff shortages and escalating operational costs, the challenges are numerous.
A practical solution to face these challenges is to leverage advanced technologies while ensuring the best healthcare support for people.
That’s exactly where generative AI can help the industry in more ways than one. In fact, generative AI has already shown promising support to the industry.
In this blog post, we discuss generative AI use cases in healthcare and how it can help the industry.
Let’s start.
The applications of Generative AI in the healthcare industry are diverse. Here are a few ways how Gen AI can be used in the industry.
With features like convolutional neural networks and deep learning frameworks, Advanced generative AI models can improve the quality of medical imaging in the following ways.
Predict or identify pathological conditions by analyzing patterns in medical images to detect conditions early and ensure timely interventions for better healthcare support.
Synthesize medical images to create easy-to-understand visual illustrations for training medical professionals, explaining medical conditions to patients, etc.
Streamline the process of medical image analysis to automate segmenting abnormalities in these images and save time for medical professionals.
With the help of generative AI, pharmaceutical companies and medical research organizations can accelerate drug development and discovery.
Here are a few ways to use generative AI to develop drugs.
Using generative AI, healthcare institutes can understand individual medical preferences and design medical support accordingly for the best results.
Here are a few ways to do it.
Generative AI is exceptional at research and analysis. The healthcare industry can make a lot of difference in the field with the analytical capabilities of Gen AI.
Here are some scenarios where it can be used.
The healthcare industry can benefit from efficient and ethical utilization of generative AI in diverse ways.
Let’s check out some of the crucial benefits here.
Human errors and inefficiencies due to biases are common in the healthcare industry. Such concerns can affect disease diagnoses, treatment plans, and patient outcomes. When properly trained and validated, generative AI can help fully avoid these issues.
Generative AI has the knowledge and analytical skills to offer consistent and objective analysis when treating a patient.
These data-backed insights can help healthcare professionals accurately understand the disease and its extent and plan the best medical course of action.
All these lead to more suitable and accurate diagnoses and medical support.
AI is becoming skilled at analyzing large amounts of patient data at once, including medical records, genes, and lifestyle data that doctors might miss.
Understanding patient data and history is crucial in medical diagnoses. And, Gen AI can accurately predict how diseases might progress and determine the best treatments for each person.
This also helps discover potential health issues early on.
Generative AI can help institutes optimize their resources effectively by allocating the resources where they are needed on priority.
With Gen AI, you can schedule appointments, coordinate care, and manage patient flow efficiently, all of which lead to smoother medical operations in a healthcare institute.
Such efficient resource allocation and utilization lead to reduced operational costs and improved patient care.
And that’s what today’s healthcare institutes need.
When properly trained in medical data, generative AI can be a game-changer for all medical institutes, including research institutes. Gen AI can offer medical expertise in challenging situations.
In addition, it can also generate synthetic medical data to be used in medical training and educational endeavors.
With its support, medical professionals can improve their medical knowledge, learn from experts, and build expertise in special areas.
These elements contribute to creating a better medical industry for everyone.
The healthcare industry is not just about disease diagnosis, medications, and patient care. It is also about accurate medical reporting and documentation. It is not the most exciting part, either, but it is extremely important.
However, with generative AI, any healthcare institute can automate these repetitive, mundane tasks, such as data analysis, document analysis, administrative processes, etc.
There are several advantages to automotive these tasks, such as:
Generative AI can improve the operational efficiency and quality of healthcare offered by healthcare businesses. However, you need to implement it well to take full advantage of the same.
Here is a step-by-step process to successfully implement Gen AI in healthcare businesses.
Before implementing Generative AI in your business, you need to understand its use cases and what value it can add to your business. This is crucial to ensure that Gen AI will help your business improve.
Analyze various options, such as natural language processing, predictive analysis, personalized medicine, image analysis, etc., and pick one or more use cases.
After finalizing the use case, you can move forward to the next stage.
You need to train your generative AI model before implementing it in your business. To do so, you need relevant and high-quality data sets.
Collecting adequate data may not be easy. Hence, you may be required to spend money and purchase data. Also, while getting the data, make sure that it is diverse, relevant, and meets the industry’s regulatory and privacy standards.
It is imperative to clean and preprocess the data to make the training more efficient and accurate.
Here, you need to select a generative AI model that can offer you the use case you want for your business.
There are diverse Gen AI models that you can pick from, such as:
GANs (Generative Adversarial Networks)
VAEs (Variational Autoencoders)
GPT (Generative Pre-trained Transformer)
To prepare the AI model to complete the tasks you want, you need to train it. You can use the collected and prepared data to train the selected Gen AI model.
The training can be extensive and take a lot of time, depending on the complexity of the tasks you want the AI to carry out. It will also require considerable computing power and capabilities.
You may need to train the model multiple times to make its performance meet your expectations. Fine-tuning the AI model is essential to get accurate results.
Here, you need to validate that the responses given by your AI model are accurate. For this, you can use different data sets that are not used while training your chosen AI model.
Once you are happy with the validation, you can move on to testing the AI model.
When testing, understand the model’s performance, reliability, and accuracy. Assess these KPIs to make sure that the AI can meet the established healthcare standards when deployed in your business.
If you are unhappy with any of these factors, you can go back and train the AI again.
In this step, you must integrate the AI model you developed into your current systems and platforms. Ensure that the AI model is compatible with EHR and other tools you use in your business.
You may not be able to integrate the AI model all by yourself into the systems and workflows. If you need external help, you can look for agencies that offer generative AI consulting services.
Effective integration of the AI model into the internal systems is crucial for the top performance of the AI.
Ensuring regulatory compliance with the AI model is important for its business-wide utilization. To do this, you need to look for the existing regulations in your country of operations. In the US, it is the Health Insurance Portability and Accountability Act. The regulations can change from country to country.
These regulations mandate the use of robust privacy protocols to safeguard data.
Implementing the AI model is just one part of the process. The next is monitoring and improving the AI’s performance and efficiency.
There are several ways to improve the performance of your generative AI model. Here are a few ways:
Ethical considerations are crucial when using AI in healthcare. You need to consider various biases that can creep into the model.
Therefore, there must be enough systems to ensure AI’s transparency in healthcare.
You need to constantly communicate with everyone who’s using and benefiting from AI in healthcare and educate them about AI’s impact on improving healthcare processes.
While the benefits of implementing Gen AI in the healthcare industry are numerous, you need to overcome a few challenges to ensure its success.
Here are a few challenges you need to take care of.
As generative AI models use lots of data and medical information of patients, it leads to privacy and data security concerns. This is the biggest challenge that a business looking to implement generative AI in healthcare needs to address.
Here are a few ways to address the challenge of data security and privacy.
This is another challenge that businesses looking to use generative AI in the healthcare industry must overcome.
The AI may learn from the biases in the training data and treat certain groups of people differently. This is unethical and morally wrong on many levels, so your healthcare business must develop and integrate safety measures to avoid such biases.
Further, you need to ensure that your Gen AI model is fair and unbiased to win the trust of patients, investors, and other stakeholders, such as medical professionals.
Understanding the outputs of the AI models is crucial to providing the right healthcare advice and support to patients. Sometimes, the outputs of Gen AIs can be complex and hard for medical professionals to interpret.
Therefore, you need to ensure transparency and explainability in AI’s outputs. It is crucial to build a sense of trust and help medical professionals make accurate decisions.
Many healthcare businesses may use legacy systems that make it difficult for AI models to be integrated. In addition, when an AI model is introduced, it is sure to disrupt the workflows and established processes.
This may not be welcomed by healthcare professionals and other employees who have become accustomed to these processes and workflows.
To avoid these challenges, businesses must assess their systems before developing AI models and creating interoperability points. Further, providing staff with training is also crucial to winning their trust.
AI hallucinations are a considerable challenge for businesses that want to use Gen AI in their businesses. The same is true for the healthcare industry.
Inaccurate results and outputs can be dangerous in medical diagnosis and treatment plans. The best way to address the issue of accuracy is to ensure human review before any output is used in diagnosis and to create treatment plans.
You can work with agencies, like Fullestop, that offer generative AI consulting to ensure the accuracy of outputs.
As generative AI is making immense contributions to every field in the world, the healthcare industry is undoubtedly one of them. In fact, the healthcare sector has many opportunities to leverage generative AI to ensure accurate diagnoses, create individual treatment plans, and analyze data to provide better healthcare support. As the healthcare industry moves towards a more technology-driven environment, integrating and using generative AI models to help healthcare businesses will become more prevalent.
However, careful research and planning are needed to use Gen AI efficiently in the healthcare industry. That comes when you work with IT businesses that can offer generative AI solutions that meet your needs. At Fullestop, we offer tailored and customized generative AI solutions for healthcare businesses according to their exact requirements. Our team of analysts, strategists, and developers has extensive experience in the field.
