How AI Is Helping Doctors to Save Lives

Artificial intelligence (AI) has kept reinventing and reinvigorating itself. Advanced machine learning systems today can act and anticipate. This is a stride forward from the surgery-assist machines and genetic sequence connecting that were initially powered by AI.

Artificial intelligence is altering healthcare. Its application in a range of diagnostic sectors is becoming evident. To discover and solve healthcare problems, stakeholders and medical practitioners can use AI. 

They are able to deploy machine learning (ML) and deep learning (DL) technologies. These can provide solutions quickly and accurately. It will also help leverage data trends to make intelligent medical or commercial recommendations.

Let's check out what artificial intelligence is and how it assists doctors in saving lives.

What Is Artificial Intelligence?

Artificial intelligence (AI) is the replication of intelligence in systems that have been trained to act and think like humans. It can also refer to the system that demonstrates human-like characteristics. These characteristics involve intelligence and problem-solving.

AI is deployed by using machine learning algorithms. Machine learning algorithms are trained with accurate data. They also become more precise with time. In the systems where you need to deploy many algorithms, MLOps is required.

MLOps is a field that focuses on supplying machine learning (ML) models consistently and efficiently. MLOps helps install, manage, regulate, and protect models in operation. It consists of different factors. These factors, when combined, create an automated machine learning pipeline. It maximizes your ML efficiency and ROI. These factors are:

• parallel processing and feeding models
• all models in operation have their maintenance catalog
• supervision of the model version
• administration of infrastructure

AI Is Helping Doctors

AI algorithms can learn to recognize correlations in the same way that doctors do. Algorithms require a large number of actual examples. Machines can't understand the message in books. Thus, doctors must scan these instances.

As a result, AI is beneficial in situations such as:

• using CT scans to diagnose liver cancer or migraines
• using electrocardiography and cardiac MRI scans to test the chance of a heart attack
• identifying skin conditions in skin photos and classifying them
• finding retinal fundus images signs in eye photographs

AI is becoming as effective as doctors at diagnosing. The reason is there is so much good data accessible in many circumstances. The only difference is that the algorithm can reach conclusions in just milliseconds. 

This can be easily replicated all over the world at a low cost. Soon, everybody will have access to the same high-quality radiological tests from top experts at a low price. It will also decrease the time taken to produce the results.

1. Treatment Plan

Patients respond to drugs and treatment plans in various ways. As a result, tailored treatment has a considerable potential to extend patients' lives. However, it is challenging to determine which criteria should impact therapy choice.

AI can assist in determining a person's response to the medication. It includes different variables such as heart rate and bacteria. So, the algorithm can predict how a patient would react to a particular therapy.

By analyzing and cross-referencing identical patients' therapies and results, the system understands this. The result forecasts make it straightforward for clinicians to construct the best treatment plan.

2. Biomarkers Identification

You can only offer treatment for an illness if you know what they have. Complete genetic analysis, for example, is expensive. It requires specialized laboratory equipment and professional knowledge.

Biomarkers are chemicals found in biological fluids, usually human blood. They determine whether a person has a disease with 100% certainty. They make detecting a disease both safe and affordable.

They can also be used to track the development of an illness. It enables clinicians to select the best treatment and assess its effectiveness. But, finding relevant biomarkers for a specific disease is difficult. It includes evaluating tens of thousands of possible molecular combinations.  

AI can speed up the process by automating a significant percentage of the manual effort. The algorithms divide molecules into excellent and bad options. It allows physicians to concentrate on the most promising prospects. So it is another time-consuming and costly approach.

3. Emergency Help

The time it takes for an ambulance to arrive after a severe cardiac arrest is critical for recovery. Rescue operators must recognize the signs of a heart attack to take immediate action. It helps in increasing survival chances. AI can examine both verbal and nonverbal evidence in establishing a diagnosis from a range.

Corti is an artificial intelligence (AI) tool that supports emergency medical personnel. If it identifies a heart attack, it alerts emergency personnel by evaluating:

• Caller's speech
• Ambient noise
• Statistical details from the patient's medical history

Corti, like other ML systems, does not look for specific signals. Instead, it trains itself by attending to many calls to find essential elements. A report published in 2019 found that utilizing AI algorithms recognized heart attack incidents superior to human responders.

4. Kidney Disease 

Doctors may have difficulty detecting acute kidney damage (AKI). But, it can cause patients to worsen and become life-threatening. With an approximate 11% of hospital mortality due to its failure, early recognition and diagnosis of these instances can reduce life-long care and renal dialysis costs.

AI can now assist hospitals and doctors in managing these problems. It can also reduce the chance of recurrence. It can help analyze and interpret research and data obtained on a patient. This information is utilized to build a "mathematical clone" of the person. It permits doctors to examine their patient's condition makeup. 

They can perform simulations to see possible treatment results. In the end, this could reduce treatment and rehabilitation time. It enables doctors to map out various scenarios to see which one works best for the individual before starting therapy.

AI can also estimate the probability of a patient acquiring future illnesses. It includes renal disease, transition to end-stage kidney damage, or hospitalization. For more phenotypic and result projection in kidney illness, AI can help with personalized medicine.

5. Reduce Time to Analyze Images

A clinical trial might generate data for verification. These datasets can be analyzed using AI algorithms. The results compared to other research to find patterns and out-of-sight linkages. Medical imaging providers can use the procedure to track essential data.

It aids in the review of photos and scans by a doctor. It allows radiologists and cardiologists to find crucial information for prioritizing critical patients. It will avoid errors in interpreting health records and establishing more accurate assessments.

For instance, Hardin Memorial Health (HMH) wanted a mechanism to retrieve relevant information from EHRs in a focused format for imaging doctors. The institution's emergency room opted to work with IBM to develop a tool. This tool locates patient data pertinent to the image operation performed on that patient.

Final Thoughts

AI usage in the medical field is still a work in progress. The difficulties include a lack of trust in an ML system's findings. It necessitates fulfilling specific standards. AI in healthcare has already provided advantages to healthcare entities. 

Doctors can improve processes by utilizing AI in their workspace. It can help in discovering results fasters from investigations. It can assist us in identifying valuable patterns. Doctors can use these patterns to make real-time decisions to save patients' lives.