Everything You Need to Know About AI in Healthcare

AI in healthcare

Artificial Intelligence (AI) has revolutionized many industries in recent years, and healthcare is no exception. With the future of healthcare technology in full view, some experts say that AI will transform the healthcare industry as we know it by becoming faster, more powerful, and better at accurately making clinical decisions as doctors would. With the advancement of AI in healthcare, however, that also means there are challenges healthcare professionals must face to ensure the safe and effective use of this powerful technology.

Let’s explore the applications, benefits, challenges, ethical considerations, and future of AI in healthcare to help healthcare providers better understand and embrace this powerful technology.

Applications of AI in Healthcare

Although the uses of AI in healthcare have been hypothesized since the 1950s, the true applications of this advanced science took hold in the 2000s with the advancement of deep learning. Since then, AI has been integrated into various healthcare applications, slowly becoming more advanced and accepted as a healthcare practice. 

Diagnosis and Treatment of Diseases

One of the applications of AI in healthcare that is discussed prominently is the accurate diagnosis and treatment of patients with AI assistance. Much of the data in the healthcare industry is unstructured. This means that it is unsorted and undefined within data models, making it much harder to sift through and gather relevant insights. It is estimated that 80% of data in the healthcare industry is unstructured. With this large amount of unstructured data, it is difficult for trained physicians to even recognize patterns in population health and apply their findings for better treatment.

However, with the advancement of AI in healthcare, diagnosing and treating patients can be improved even with the large amounts of unstructured data prevalent in the industry. This is due to Natural Language Processing (NLP) in healthcare. NLP allows computers to read, understand, and interpret written human language. As this technology advances, NLP will begin to continuously sift through large amounts of unstructured data. It will also offer healthcare providers patterns and insights that can aid them in making more informed decisions about how to diagnose and subsequently treat their patients. 

Personalized Medicine

In addition to better patient diagnosis and treatment, AI in healthcare also helps personalize medicine for patients with predictive analytics. With the help of NLP, AI can predict the development of certain medical conditions in patients based on their personal medical history, lifestyle, family history, and surrounding environmental factors. Once an AI identifies conditions you may be at risk for, it can develop a personalized medical plan to prevent their onset. If the condition has already progressed beyond prevention, the AI can recommend targeted treatments to manage it. 

Drug Discovery and Development

AI also increases the efficiency of drug discovery and development. Typically, developing a new drug requires large amounts of research and funding. With the assistance of AI in healthcare, however, the time and money it takes to develop a new drug can be significantly reduced. One example of the use of AI in drug discovery and development came from the partnership between biotechnology company Evotec and the AI company Exscientia, which uses AI for small-molecule drug discovery. Under this collaboration and their “Centaur Chemist” AI platform, the team found a drug candidate in eight months—rather than the commonplace four or five years. As AI evolves, this process will only expedite the development of important medicine, eventually reducing costs and improving patient care.



Medical Imaging Analysis

AI is also significantly impacting medical imaging analysis in healthcare. In traditional healthcare settings, medical imaging has to be analyzed by a physician to determine possible conditions a patient may develop. Medical imaging analysis is often a lengthy process. It takes away the precious time physicians have to treat patients and instead requires them to look at images to determine what is needed to diagnose a disease or chronic condition. While this process is important, AI in healthcare can be used to examine medical images in place of physicians. Although AI still requires training and human oversight, implementing this process allows physicians to use the time they save to treat patients rather than examine medical images.

Electronic Health Records (EHR) Management

Much of the unstructured data prevalent in the healthcare industry is caused by healthcare providers’ mismanagement of EHR data. An EHR is an Electronic Health Record. This record follows a patient around no matter where they receive treatment so that each physician knows their medical history, allergies, conditions, etc. While these records are very beneficial, they also contribute to the plethora of data that clogs the healthcare industry. With AI in healthcare, these records can be read and interpreted more quickly, streamlining patient care and giving physicians access to a patient’s most pressing conditions.

Benefits of AI in Healthcare

As applications such as the ones previously discussed become more prevalent in the healthcare industry, the benefits of utilizing technology in healthcare will become more widespread.

Improved Accuracy and Efficiency in Diagnosis and Treatment

One of the key benefits of utilizing AI in healthcare is the improvement in diagnosing and treating medical conditions. In healthcare, physicians use medical algorithms to standardize how medical conditions are diagnosed or treated. This sometimes comes in the form of tables and flowcharts, but can also come in the form of AI. Training AI with medical algorithms can be done in a standardized flow, similar to how physicians have a step-by-step process for diagnosing patients. Once AI is trained with medical algorithms, it can scour medical data and recognize predictive symptoms for conditions that may be unnoticeable by the human eye. This gives healthcare providers more insights into a patient’s condition and can advise them on how to diagnose and treat patients as time goes on.

Increased Patient Satisfaction

With the implementation of AI in healthcare comes increased patient satisfaction. Most notably, this is due to the increased efficiency of treating patients. As the capabilities of AI grow, the workload faced by physicians can be diminished by removing administrative tasks from their responsibility. Once those administrative tasks are taken off of their plates, they can use the time they get back to spend more time with patients discussing treatment options and next steps. 



Reduced Healthcare Costs

Along with these other factors, AI in healthcare also reduces costs by accurately identifying risk adjustment factors. A risk adjustment is a number given to a patient by Medicare that indicates how much their healthcare should cost. There are many health conditions and demographic factors that impact a person’s risk adjustment. However, the integral idea is that risk adjustments incentivize healthcare providers to treat sicker patients by reimbursing them based on a patient’s condition severity. However, one of the inefficiencies of risk adjustment is its reliance on big data. 

Without big data, risk adjustment doesn’t work because it averages all patients in a patient population; many EHRs don’t have accurate or structured enough data to support this system. However, by combining data from multiple providers, reducing the number of coding errors, and reading through colossal amounts of data, these problems can be solved. With better data and predictive analytics, risk adjustment scores can be more accurate, and healthcare providers get reimbursed for their treatment. This means that they will continue taking on sicker patients and not force them to pay exorbitant amounts for their treatment, reducing healthcare costs overall.

Better Patient Outcomes and Quality of Life

Best of all, utilizing AI in healthcare produces better patient outcomes and quality of life. As the process for operating and performing examinations becomes faster, more patients can be treated overall. As more patients get treated for their medical conditions, they have better outcomes and their quality of life improves. Luckily, this process is cyclical, so the better the patient outcomes are, the more investment there will be in technologies that improve patient outcomes and the quicker patients receive treatment.

With all of that being said, some challenges also come along with embracing AI in healthcare.



Challenges of AI in Healthcare

Data Privacy and Security Concerns

One challenge of AI in healthcare is the data and privacy concerns that arise, especially with the emergence of big data. While big data is great for predictive analytics—as AI can scour tons of data much more efficiently than humans—it is susceptible to compromising patient information through data breaches and cyberattacks. 



Due to this, healthcare providers must ensure that the data they use has secure storage and data transmission that complies with government regulations and consistently improves security measures.

Bias in Algorithms and Decision-Making

Unfortunately, one of the ways AI can impact your healthcare practice negatively is with bias, which leads to improper decision-making. An example is when a data set disproportionately favors a particular demographic. This bias can occur whether it’s introduced during the data-gathering process or the administration phase. Consequently, even with AI intervention, data sets lacking proper demographic distribution may fail to accurately represent the entire patient population. This could lead to inaccurate predictions and ineffective treatment methods.

This also applies to the formulation of an AI algorithm. Developers must mitigate any bias they have when developing an AI algorithm to ensure that when data is sorted through and interpreted, it is not examined through an improper lens that leads to inaccurate predictions.

Among the challenges of AI in healthcare, there are also ethical considerations to keep in mind as this technology grows. 

Ethical Considerations of AI in Healthcare

In terms of ethical responsibility, patients have the right to be informed about their medical information and must consent to any treatments they are given. With the rise of AI in healthcare, concerns have arisen based on the ideas of consent and autonomy, mainly about the risks associated with using AI in healthcare treatment. One of the most pressing matters is a patient’s right to know who is responsible when a robotic medical device like AI fails or malfunctions. 

We’ve already seen that AI advancements are ineffective in some areas, like the diagnosis of COVID-19. According to TechCrunch, an AI that was being used to diagnose COVID-19 from the sound of coughs failed to do so “at a rate better than chance.” As more studies and cases like these arise, healthcare providers and researchers will need to ensure that the technology in question has passed numerous tests for effectiveness and will be applied only when they’re exceptionally confident about its success. Additionally, it will be imperative for healthcare providers and patients to agree on AI use in their treatment regimens and decide where the blame lies if AI fails in this capacity.


The Future of AI in Healthcare

AI has the potential to revolutionize the healthcare industry by improving patient outcomes, reducing costs, and enhancing operational efficiency. Emerging trends and technologies such as machine learning, natural language processing (NLP), and big data are further expanding the capabilities of AI in healthcare.  

If you’re looking to fully realize the benefits of AI in healthcare, healthcare providers can embrace this technology and work collaboratively with AI systems. At IRIS, we’ve partnered with AEYE Health and Thirona to use AI screening as a tool to provide optionality in our solutions. However, there are advanced solutions currently being used that continually close patient care gaps without the use of AI, including traditional physician-graded screenings with IRIS

With the IRIS Solution, degenerative eye diseases such as Diabetic Retinopathy can be detected in patients earlier, allowing for more effective treatments and improved patient outcomes. So, whether by AI in healthcare or other advanced screening solutions, improved technology will be key to actively closing patient care gaps worldwide. For more questions about the prevention of Diabetic Retinopathy, contact us


Frequently Asked Questions

How Is AI Being Used in Healthcare?

AI in healthcare is revolutionizing medical care by improving the expertise of medical professionals. It’s being employed to facilitate early disease detection, optimize treatment plans, and enhance patient care. Some applications of AI in healthcare include predictive analytics, medical imaging analysis, and the development of personalized treatment strategies, which utilize machine learning to process vast data sets and deliver actionable insights.

What Is a Good Example of AI in Health?

One noteworthy example of AI in healthcare is the application of machine learning algorithms in medical imaging. These algorithms that are trained on thousands of patient images are capable of detecting subtle patterns that may not be immediately apparent to the human eye. This can lead to earlier detection and treatment of harmful conditions that enhance patient outcomes and efficiency in the healthcare system.

What Are the Benefits of Using AI in Healthcare?

AI in healthcare presents numerous benefits, including enhanced diagnostic accuracy, improved patient outcomes, and increased operational efficiency. Through analyzing vast datasets, AI can identify patterns and insights that human clinicians might miss, resulting in more accurate diagnoses. Additionally, AI can predict patient outcomes based on historical and real-time data, enabling personalized treatment plans. Lastly, AI can automate routine tasks, freeing up healthcare professionals to focus more on patient care. 

How Is Machine Learning Used in Healthcare?

Machine learning, a significant subset of AI in healthcare, is leveraged to decode vast datasets and uncover patterns that humans may miss. It’s instrumental in areas such as predictive analytics, personalized medicine, and early disease detection. For instance, machine learning algorithms can analyze patient data and predict potential health risks or outcomes, empowering clinicians with actionable insights to provide tailored, effective care.

What Is Machine Learning Technology in Healthcare?

Machine learning technology in healthcare refers to artificial intelligence (AI) methods that empower computer systems to learn, draw insights, and predict outcomes from healthcare data without explicit instruction. This practice is revolutionizing healthcare, from predicting patient risks, and optimizing workflows, to enhancing diagnostic accuracy. 

What Is the Future of Machine Learning in Healthcare?

The future of machine learning in healthcare is very promising. It’s paving the way for personalized medicine, predictive analytics, and early disease detection by analyzing vast amounts of patient data. As technology advances, AI in healthcare will not replace clinicians, but will instead empower them to make more accurate diagnoses and treatment plans.



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