An Overview: Applications and Improvements

There are many possible uses of AI and ways to improve processes within the healthcare field.

Firstly, AI can help to provide significant data-driven support to healthcare workers. By using data algorithms, technologies can identify patterns and deliver automated insights for common applications like health monitoring, managing medical records, treatment design, and digital consultations. 

With less time being spent on repetitive administrative tasks, medical professionals can deliver better care while having a less-demanding number of tasks to complete. Additionally, this could alleviate clinician burnout, which has been a critical issue, especially since the start of the COVID-19 pandemic. There are several ways AI can be integrated into the daily workflows of healthcare providers.

“A few broad examples would be the automation of routine administrative tasks, freeing up time for healthcare providers to focus on patient care, and analysis of large amounts of medical data quickly, accurately, and efficiently, which can lead to faster and more accurate diagnosis and treatment,” Brockett said.

Currently, diagnostic errors account for 60% of all medical errors and an estimated 40,000 to 80,000 deaths each year. According to a study, around 12 million people in the United States are misdiagnosed annually, and 44% of those are cancer patients. 

AI is helping overcome this issue by improving diagnostic accuracy and efficiency. However, although AI can offer more accurate diagnostics, there’s always a chance that it can make mistakes, which causes some companies to hesitate about adopting AI for diagnosis.

Another area for huge innovation is the training of AI to interpret medical images such as X-rays, CT scans, and histopathology slides to identify abnormalities, lesions, and tumors. AI-provided automated diagnosis can also make diagnoses faster and more accurate.

Thus, the development of new AI programs is actively redefining radiology, especially since AI has the ability to extract data that is not visible to the human eye. While AI can be more skilled than radiologists in analyzing most medical data, it’s not yet mature enough to completely replace radiologists. 

The Massachusetts Institute of Technology (MIT), for example, has created an ML system based on a hybrid approach that can diagnose different types of cancers by analyzing medical reports or referring the task to an expert radiologist.

In addition, AI can be used in several other ways to improve pathology in healthcare.

“AI can help identify patients at risk of developing certain diseases, such as specific types of cancer, and could enable earlier intervention to possibly prevent occurrence or worsening of a specific disease. AI can also help to identify the most effective treatment plans for each individual patient based on their unique characteristics, resulting in better outcomes and improved quality of life,” Brockett said.

For instance, AI can be used to provide clinical decision support, assisting pathologists in making accurate diagnoses and treatment decisions. It can also be used to analyze large volumes of pathology data, including patient records, medical images, and genomic data, to identify patterns and insights that can inform future research and treatment strategies.

AI can also be used to ensure quality control in pathology labs, detect errors, and identify areas for improvement.

Additionally, AI can drastically improve drug development as well as the clinical testing associated with it.

AI can be used to analyze data to identify potential drug targets, design new molecules with specific properties, find existing drugs that could be repurposed for new indications or conditions, and predict drug efficacy, toxicity, and adverse effects. All of these processes would significantly reduce the time and cost of preclinical testing and clinical trials.

Thus, AI would optimize clinical trials by identifying suitable patient populations, predicting patient recruitment and retention, and monitoring patient safety and progress during the trial.

“Many companies are leveraging AI to help in patient recruitment for clinical trials by identifying eligible patients and matching them with enrolling studies, which would help expedite the overall trial and get therapies to the patients who need them sooner. AI is also being leveraged to improve the accuracy of trial endpoints. A failed trial due to inaccuracy of endpoint assessment wastes a tremendous amount of resources and significantly delays approval of therapies for patients who need them,” Brockett said.

AI can analyze patient data, such as genetic information and medical history, to predict an individual’s response to a particular drug and develop personalized treatment plans. Patient outcomes could show great improvement through these methods of medicine personalization.

Within the healthcare sector, there are many departments with varying focuses. So, naturally, there are plenty of additional ways AI can impact healthcare.

However, there’s one more highly important aspect of healthcare that AI can change. Many Americans see promise for artificial intelligence to help issues of bias in medical care based on race or ethnicity. In fact, Americans who are concerned about this bias are more optimistic than pessimistic about AI’s potential impact on the issue.

According to a recent research survey, 64% of Black adults say bias based on patients’ race or ethnicity is a major problem in health and medicine. And over half of the people concerned with this issue, 51%, believe that relying on AI could be an effective way to solve the problem.

Overall, it’s evident that utilizing AI in healthcare has the potential to be vastly beneficial. Despite this, there are still many challenges that may hinder its implementation.