Artificial Intelligence has seeped into basically every industry you can think of. Advancement right now means automating and making tasks easier. Things we would normally see in science fiction are becoming a reality.
In health, new innovations are really important to solving the never ending questions that medicine poses. But with the advent of AI, we are closer than ever to reaching a breakthrough in health advancement.
From diagnosing diseases with incredible accuracy to personalizing treatment plans, AI is transforming how doctors, researchers, and patients interact with medical technology.
Perhaps the best thing about AI is its ability to analyze vast amounts of data at lightning speed. By doing so, it is making healthcare more efficient and saving lives. But what are the areas where AI is needed in healthcare? It definitely does not replace doctors and other healthcare practitioners.
In this article, we’ll explore the most impactful AI use cases in healthcare and highlight how AI technology is improving patient care, medical research, and health outcomes.
What is The Main Advantage of Using AI in Healthcare
Artificial intelligence is basically machines and computer systems performing tasks that typically require human intelligence. These tasks include learning from data, recognizing patterns, making decisions, solving problems, and even understanding and processing language.
As a casual viewer, you might see AI as being necessary in only tech spaces, but not have a solid idea as to what it can do in healthcare. After all, this is people’s life we are talking about. However, there have been so many groundbreaking technologies that have saved millions of lifes–all thanks to AI.
If we had to pick one major advantage of using AI in healthcare, it would be the ease. With medical research, it often takes years of studies to uncover information and new techniques. But with AI, you can compress millions of data and get results in minutes. The speed, and quality are unparalleled.
AI also has a branch called Machine Learning. It is a concept based on computer programming and uses extremely large data and algorithms to break down and solve problems just as well as a human would.
As an aspect of AI, it helps to improve patients' experiences by finding out the problem and postulating a solution right away. There are many cases where this has proven to be life saving.
Let’s see some common AI use cases in healthcare and how they work.
Top 15 AI Use Cases in Healthcare
Healthcare is divided into many fields. It is a network of many activities all centered around making sure patients have the best experiences. AI is here to help and here are 15 top AI ML use cases in healthcare:
Medical Diagnosis
A problem cannot be solved without finding out the cause. Many patients’ outcomes hinge on getting the correct diagnosis. And even with technological advancements, it is not exactly still a walk in the park. The patient might need to do numerous tests and health practitioners have to still decipher results and conclude on a best possible diagnosis.
However, with AI, it is becoming easier to get a diagnosis for many conditions. And how does AI fit into diagnosis? Remember we spoke briefly about machine learning earlier? A computer program might see an X-ray and interpret it as such or not–it might make mistakes.
However, over time, it gets better the more it is used to interpret results. The program gets better at identifying conditions based on the sheer number of data it has collated. It becomes smarter to the point of razor sharp efficiency.
This is how AI can be used in medical diagnosis. Instead of doctors pouring over books and reference materials to get a diagnosis correct, they can simply feed the data into an AI tool that is super knowledgeable in collating data. This applies to and is not limited to cancer, and other diseases, diagnosis.
Personalized Medicine
Conditions usually have standardized treatment plans, but not everything works for every single patient. AI is making personalized medicine a thing with tailored treatment plans for every patient based on their genetic, clinical, and lifestyle data.
This concept is also known as precision medicine. This approach is particularly helpful for rare diseases, where AI helps identify new drug targets in the body and make existing medications work better. With it, healthcare providers can predict disease risks, optimize drug dosages, and design targeted treatments.
Pregnancy Management
Pregnancy is a delicate period. In the United States, the maternal mortality rate was 32.9 deaths per 100,000 live births in 2021, and the number was significantly higher than in other high-income countries.
AI is trying to solve this problem by making it a tad easier to gather information and make it less stressful. AI can improve pregnancy management by making it easier to detect complications early.
Machine learning algorithms analyze medical history and lifestyle factors to predict risks like preeclampsia, gestational diabetes, and preterm labor. Additionally, AI monitoring tools track maternal and fetal health 24/7 and report on any changes.
Medical Imaging and Radiology
Medical imaging and radiology is where AI-powered algorithms analyze X-rays, MRIs, and CT scans with accuracy. It helps to detect anomalies such as tumors, fractures, or lung diseases faster than traditional methods.
Consequently, AI assists radiologists in making more precise diagnoses and reduces human error. It also reduces manual labour that comes with analyzing numerous test results. AI-driven automation speeds up image interpretation and healthcare professionals can focus more on patient care rather than manual analysis.
Drug Discovery and Development
AI has shown remarkable potential in drug discovery. For one, researchers successfully trained a deep learning algorithm on a vast dataset of cancer-related compounds, and this led to the discovery of new candidates for cancer treatment.
Similarly, machine learning has been used to identify small-molecule inhibitors of MEK, a challenging cancer drug target, as well as inhibitors of beta-secretase (BACE1), a protein linked to Alzheimer’s disease.
AI has also contributed to antibiotic discovery. It narrowed down powerful compounds from a dataset of over 100 million molecules, including one effective against drug-resistant bacteria. And during the COVID-19 pandemic, AI screened large databases to identify potential antiviral drugs in record time.
Managing Chronic Diseases
Chronic diseases can be managed more adequately with AI. It enhances risk prediction and treatment consistency. A recent AI model improved breast cancer risk discrimination by 22% compared to traditional methods by analyzing comprehensive patient history data.
Additionally, AI-driven treatment recommendations have shown remarkable alignment with oncologists' choices—97% in rectal cancer cases and 95% in bladder cancer cases—ensuring greater consistency in care.
These advancements highlight AI’s potential to support early detection, personalize treatment plans, and improve patient outcomes, which improves how chronic diseases like cancer are managed.
Virtual Health Assistants
Many activities have moved online. It's no different in the health space. Chatbots and virtual nurses can take the stress out of many tedious operations. They can;
- Answer medical queries
- Remind patients to take medications
- Assist in scheduling appointments
- Reduce the burden on healthcare providers.
Furthermore, in mental health, AI can offer cognitive behavioral therapy (CBT)-based support, mood tracking, and crisis intervention–which is very important. These virtual assistants enhance patient engagement and improve healthcare efficiency.
Surgery and Robotics
Surgeries require a high level of precision. AI robotic-assisted surgeries are helping surgeons get even better results and improve patient outcomes. AI algorithms analyze many amounts of surgical data to help with preoperative planning. And robotic systems enable surgeons to perform minimally invasive procedures with better dexterity and control.
These AI systems can make real-time adjustments based on patient-specific factors. They can also reduce complications and improve recovery times. In complex procedures like neurosurgery and orthopedic surgery, AI enhances accuracy and allows for delicate maneuvers that were previously difficult with traditional techniques.
Administrative and Operational Efficiency
Even in healthcare, there is tons of administrative work. And AI can help with this. It can automate time-consuming tasks like paperwork, billing, and scheduling. It can also process patient records, verify insurance claims, and generate reports with minimal human intervention.
The end goal of this particular use case is to reduce administrative burdens on healthcare professionals. In hospitals, AI scheduling tools reduce wait times by optimising patients’ schedules.
Remote Patient Monitoring
Wearable health technology controlled by AI tracks vital signs, detects abnormalities, and provides real-time alerts to healthcare providers. This is particularly beneficial for managing chronic diseases like diabetes, hypertension, and heart conditions.
AI natural language processing tools help physicians document patient encounters more efficiently, minimizing paperwork and allowing them to focus on patient care. All of these tasks reduce the documentation burden.
Electronic Health Records (EHRs)
Electronic Health Records are online systems used to collate and manage patient information. It is already a computer system, but with AI’s help, there are bigger things in the future. It helps to improve data management, reduce administrative burdens, and enhance patient care.
Traditional EHR systems often require extensive manual entry, leading to physician burnout and documentation errors. With AI, there is powered automation to make this process easier.
Prescription Auditing
Mistakes can happen at any time, and the role of auditing is to make sure it doesn't happen–especially in healthcare. Prescription auditing by AI improves medication safety by detecting errors, preventing fraud, and ensuring compliance with regulations.
Traditional auditing methods rely on manual reviews, which can be time-consuming and prone to human error. AI automates this process by analyzing large amounts of prescription data. They flag inconsistencies, and identify potential drug interactions or dosage errors.
Machine learning algorithms can also detect patterns of overprescription, prescription fraud, and opioid abuse. It can give healthcare providers and regulatory bodies the information they need to make any decisions necessary.
Patient Data Analytics
We have already seen predictive medicine, prescription auditing and many more AI based cases. Sometimes it can be a lot overall. This is where analytics comes in. Patient data analytics uncovers patterns, predicts diseases, and personalised treatment plans.
It can help identify risk factors for chronic illnesses, detect early signs of diseases, and recommend steps to prevent them. AI also helps healthcare providers track patient progress, detect anomalies, and prevent complications, ultimately leading to better outcomes and more efficient healthcare systems.
Healthcare Management
One can argue that the biggest challenge in healthcare today is efficiency. So much happens at once and there is a need to stay on top of it all. AI healthcare management is basically all about streamlining operations and improving efficiency. Additionally, AI supports remote monitoring, surgical robotics, and virtual health assistants, making healthcare more accessible and effective.
Medical Research and Clinical Trials
In drug discovery, AI accelerates the identification of potential compounds. Research that would take decades is simplified because AI can pinpoint compounds that are beneficial to the research.
It does not stop there, it can also find drug interactions and improve existing medications for new treatments. Clinical trial design also benefits from AI, as it helps identify suitable participants through electronic health records and genetic data.
Challenges of AI in Healthcare
The benefits of AI in healthcare are numerous. However, integration still remains a problem for AI. Here are some reasons why that might be the case:
- Data Privacy and Security – AI needs a large number of patient data to apply some of these use cases. Thus, there are concerns about confidentiality, data breaches, and compliance with regulations with implementing AI.
- Data Quality and Bias –Aside from data confidentiality, there might be issues with incomplete, inconsistent, or biased datasets with AI in healthcare use cases. They might lead to inaccurate predictions and worsening healthcare disparities.
- Integration with Existing Systems – Many hospitals and research institutions use outdated or fragmented electronic health records. It is not easy to overhaul old systems and bring in new ones.
- High Implementation Costs – The cost of developing, maintaining, and integrating AI solutions can be a barrier for many healthcare institutions.
Takeaway
AI use cases in healthcare exist because there was a need for innovation and AI wants to bridge that gap. With it, patients can now boast of more comprehensive care across the board. And in areas where it is not as predominant, there are still small ways it impacts the system overall. Meanwhile, there are still kinks in the system but overall, AI is in a good place with healthcare and it will only get better.
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