AI in Healthcare: Benefits, Applications, and Cases

What Does AI in Healthcare Mean?

AI in healthcare signifies the integration of machine learning (ML) algorithms and other technologies in a medical context. 

To put it simply, AI is a process through which machines imitate human cognition. It is capable of thinking, analyzing, and taking decisions or actions to predict a specific outcome. Additionally, AI-enabled devices are capable of sorting through massive amounts of data to locate patterns and trends.

AI in healthcare offers certain opportunities and enhances medical processes that include 

• Diagnosing diseases, 
• Optimizing patient care, 
• Analyzing medical data, 
• Assisting medical decisions, and more.

Interestingly, AI-enhanced robotic surgical equipment can improve surgical outcomes by delivering real-time information and reducing physical instabilities. Doctors and other medical professionals can effectively improve their medical services with the use of AI. They will be better able to provide accurate diagnoses and treatment solutions.

What are the Different Types of AI in Healthcare?

Some of the most common and different types of AI in healthcare include:

• Machine Learning (ML):  ML provides data-driven clinical decision support (CDS) to physicians and hospital staff. It helps to analyze and track medical data and provide insights. Also, it helps to perform tasks such as categorizing information or providing health outcomes. 
• Deep Learning: It’s a subset of machine learning that builds neural networks capable of increasingly challenging tasks by using larger data sets, extended training times, and numerous ML algorithms. 
• Neural Language Processing (NLP): The NPL includes uses such as text analysis, speech recognition, translations, and other goals associated with the language. It assists in streamlining workflow, reducing administrative workload, and enhancing predictive analytics. 
• Robotic Process Automation (RPA): RPA is used by many healthcare businesses to enhance the patient experience and daily operations of their facilities. Automation of administrative tasks and clinical processes can be easily employed with the use of AI in computer programs. 

Source: Mater Private

What is the Significance of AI in Healthcare?

With continuous advancements, AI has grown in its capabilities. It helps to improve efficiency in medical practices. The development of AI-powered tools has minimized the workload of huge data and information sets, reduced costs, and boosted safety. 

Also, AI deep learning assists in identifying diseases, improving patient outcomes, providing personalized treatment plans, automating certain processes including drug diagnostics or discovery, and much more.  

The adoption of AI technology in the UK’s healthcare sector reached 46% of organizations in 2019; this demonstrates how widely it has been embraced.

People around the world are becoming more aware of AI integration in healthcare. It also requires medical personnel to understand the power of AI technology and how the world is continuously changing its way of working with the help of this technology. 

Areas of Impact for AI in Medical and Healthcare?

The influence of AI is growing substantially in medicine and healthcare. AI transforms the effectiveness of healthcare. Areas of the impact for AI in medicine and healthcare include:

AI assists in eliminating human errors, improving population health management, boosting workflow operations, reducing workload, strengthening innovation, and increasing efficiency. 

Additionally, the impact of AI on the healthcare sector also helps medical personnel deal with work burnout, which as a result helps them to stay active throughout their hectic working hours. 

What are the Benefits of AI in Healthcare?

Following, we’ve discussed the benefits of AI in healthcare. Let’s learn:

Improved Patient Care

A recent poll found that 83% of patients attribute the problem to poor communication.

Artificial intelligence can help to resolve that concern. AI for patient treatment machine learning in medicine assists in effectively navigating through data, collecting reports, and directing them with the next steps for seamless communication. It also helps to improve patient care by facilitating better use of resources, reducing research time, and minimizing errors. 

Accurate Information 

Making a diagnosis as soon as feasible is essential when dealing with medical problems. Medical practitioners need reliable information that enables them to make critical decisions. 

Minimizes Costs

AI helps in reducing costs in terms of both finances and time. AI tools ease up the burden of administrative tasks, minimize medical errors, reduce labor shortages, and much more. Furthermore, AI handles tasks more quickly than humans which offers more time to medical professionals to handle other important medical tasks. 

Analyzing Data and Improving Diagnosis

AI can evaluate medical records and other healthcare-related data far more quickly than humans can, in terms of both speed and efficiency. Also, it helps medical professionals to reach a diagnosis much more quickly and lets them work more effectively. 

Monitoring and Consultations 

Wearable gadgets and healthcare app development that track a patient’s health or digital consultations that can be conducted on a smart device are just two examples of how AI might improve the way medical professionals collect and evaluate data. Additionally, patients can monitor their health and get expert assistance online.

What are the Disadvantages of AI in Healthcare?

Following, we’ve discussed the disadvantages of AI in healthcare. Let’s learn:

Training Concerns

Extensive training needs to be employed for both medical professionals and AI. It is because many professionals still are unaware of how to use AI. 

On the other hand, AI needs to be trained to collaborate with huge data sets and working procedures of the medical institute as it varies for every institute. 

Risk of Unemployment

The introduction of AI has posed a risk of unemployment in every sector, including healthcare. However, there is no such risk of unemployment when it comes to the healthcare sector because human intervention is quite important as AI cannot beat human intelligence and a sense of empathy in any case. 

Security Risks 

Technology can be vulnerable to security risks that could disrupt the healthcare system. Moreover, every patient’s data must be kept secure and confidential. Nowadays, cyber attacks can cause high damage to any technological system if not taken any protective measures. Therefore, it is important to secure all the databases to avoid any such risks or harm to medical information and data. 

What are the Applications of AI in Healthcare?

Here we have outlined some crucial applications of AI in healthcare.

Let’s discuss this in detail:

Medical Imaging 

AI analysis of medical images can help medical professionals find problem areas or subtleties that the human eye might miss. With the help of this information, doctors can quickly detect anomalies in X-rays, MRIs, and CT scans. Doctors can assess the severity of an injury or the development of a disorder using medical imaging. 

For example, an AI-powered medical image may analyze data points in medical reports to distinguish between a diseased and a healthy component.

Diagnosis and Treatment 

Machine learning could be used in healthcare to spot more complex ailments that don’t detect right away following a diagnosis. It might also be used to monitor patients to detect any worsening of their illnesses. This would enable the AI system to acquire important data and alert medical personnel.

Huge amounts of patient data can be analyzed by machine learning algorithms to uncover patterns and correlations that may be hard to detect by human experts. As a result, it is possible to make diagnoses more swiftly and precisely and to develop treatment plans that are specific to each patient. 

Artificial intelligence (AI) systems can assess a treatment’s effectiveness and make changes in real time, improving clinical outcomes and reducing healthcare costs. 

Drug Discovery & Development 

AI helps accelerate the process of drug discovery and development. It aids in the development of novel drugs by studying the data set and currently available medications. 

If done otherwise, then this entire process is quite expensive and time-consuming. However, AI makes it possible to reduce costs and new drugs can be found faster enhancing the chances of effective medical treatments. 

Managing Medical Records 

Artificial intelligence (AI) solutions can be used to manage and streamline automated data entry, sorting, and categorization processes. By employing neural language processing (NLP) to extract important information from clinical notes that are not structured, medical personnel can more quickly access patient information. 

Provides Improved Care

Due to the ongoing demand, there is frequently a staffing shortage in the medical industry. The situation worsens especially in developing nations.  In that case, AI can be used to facilitate patient diagnosis, close skill gaps, and provide access to life-saving therapy for people everywhere.

What are the Important Cases of AI in Healthcare?

Following, we’ve discussed important cases of AI in healthcare. Let’s learn:

Dermatology Scans for Melanoma

Skin cancer is one of the most prevalent types of cancer. One of its types – Melanoma is less common but is responsible for over 7000 deaths this year in the United States. Unfortunately, Melanoma is likely to spread out to other body parts and invade nearby tissues. 

Source

Here’s what you need to know–

If detected early, up to 86% of melanoma skin cancer types are preventable and treated. For that, artificial intelligence and machine learning come to the rescue. 

Also, know this–

Differentiating early-stage melanoma from benign moles and other malignant neoplasms can be difficult. Because of this, medical practitioners are experimenting using AI to help them make decisions. 

Lung Cancer

Earlier, detecting cancer was a huge challenge. It still is. But with the help of artificial intelligence (AI), cancers can now be found using chest X-rays.

Clinical researchers from the Seoul National University Hospital found that on the first and second reads, a team of four thoracic radiologists was surpassed by a commercially available AI tool. 

“Windowing” enables both humans and robots to view X-ray details that are beyond the color spectrum that our monitors can display.

The artificial intelligence algorithm:

Produced results with a better specificity during initial reads than the radiologists. During the second read, assisted the team members in better interpreting the findings. 

The results of the experiment are positive, nevertheless.

Medical professionals are still skeptical about the ability of AI algorithms to produce consistent results in clinical settings. 

Also, implementing machine learning technologies in healthcare businesses is quite difficult. 

Many need to update their current software systems before they can use advanced machine learning technologies. Others demand more advanced data management and governance processes to ensure the highest level of security for the processed imaging and other patient records.

Digital Pathology

In a recent study, board-certified pathologists diagnosed colorectal cancer with 96.9 accuracy and AI slightly outperformed them reaching an accurate diagnosis 98 % of the time.

Professionals can virtually represent glass pathology slides for in-depth investigation due to a rapidly expanding field of conventional microscopy known as digital pathology. 

Artificial intelligence assists pathologists with the following aspects:

• Accuracy of early detection and diagnosis
• Examination of sample tissues in great detail
• Analysis and interpretation of images 
• Identify pathology categories that correspond to past cases 

A group of cancer specialists recently looked through WSIs from 11,000 cancer patients in a public database with 32 cancer subtypes. Annotations and image data were combined by the trained software to reach a “computational consensus” regarding the disease being displayed. 

The algorithm accurately recognized various pathology types on frozen section slides:

• 97% for kidney renal clear cell carcinoma
• 93% for bladder urothelial carcinoma 
• 99% for ovarian serous cystadenocarcinoma

And performed equally accurately with histopathology slides:

• 99% for skin cutaneous melanoma
• 100% for thymoma
• 98% for prostate adenocarcinoma

What is the Future of AI in Healthcare?

Grand View Research predicts that the market for AI in healthcare will be worth $208.2 billion in 2030, which is a huge increase from the $15.4 billion market size in 2022.

Over the following ten years, there will likely be a major increase in the application of artificial intelligence in healthcare.

The good news is that many major companies are starting to employ AI in healthcare. We’re still in the early stage of learning AI capabilities that would make healthcare better. However, it still plays an important role in contributing to the healthcare in future. 

The development of accurate medicine, which is widely regarded to be a vital improvement in healthcare, is primarily supported by machine learning. We also expect AI to eventually become proficient in generating recommendations for diagnosis and therapy, despite early attempts being challenging.

Given the rapid developments in AI for image processing, it seems likely that most pathology and radiology images will eventually be examined by a computer. With the help of neural learning processing, speech & text recognition will be used increasingly frequently for tasks like patient communication and clinical note transcription.

To be widely used, AI systems must be approved by regulatory bodies, sufficiently standardized so that similar products perform similarly, integrated with EHR systems, training clinicians, paid for by public or private payer organizations, and improved over time in the field. 

Eventually, it will take far longer than it takes for technology to improve, but these issues will eventually be handled. Therefore, we forecast an extensive application of AI in clinical practice over 10 to 20 years.

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