Artificial Intelligence in Healthcare: Its Rising Trends and Implications

Artificial Intelligence in Healthcare: Its Rising Trends and Implications

AI in the global healthcare market is predicted to reach $188 billion by 2030. AI is helping with personal profiling in many industries like Healthcare, Retail, eCommerce, Banking and Finance, Logistics, etc. With artificial intelligence in healthcare, doctors can suggest the diagnosis by incorporating the essence of past history. Moreover, AI reduces the possibility of human errors with regard to test results, imaging, claims processing, etc. There is no doubt about the myriad benefits that AI brings to the table. Before going into AI’s rising trends and implications in healthcare, let’s quickly learn the benefits.

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How AI is Revolutionizing Healthcare

  • AI automates tasks and analyzes big data for faster and better healthcare process.
  • AI can automate administrative tasks like insurance pre-authorization and unpaid bill follow-ups.
  • Processing big data sets can lead to predictive benefits and improved healthcare service.
  • Wearable healthcare technology, like FitBits and smartwatches, uses AI to alert users and healthcare professionals about potential health issues.
  • AI in technology eases healthcare professionals’ workload and prevents unnecessary hospital visits.

6 Trends That Will Define AI for Health and Healthcare

We see an overlap of several trends when it comes to analyzing how AI works its way into our lives. Let’s look at how six latest trends are converging to make AI in healthcare:

1.      Big Data

There is a vast amount of varied data in the healthcare sector. For example, it was found that around 11000 new articles are published in dermatology alone every year.

Dealing with data manually has become outdated, but integrating AI into the analysis will surely speed up the process. Healthcare providers can plan how to deal with certain procedures by accessing records and the latest research articles on certain diagnoses. Innovations can be done with more confidence since they have data to back them. Test results can be analyzed and shared anonymously among providers having similar visions.

2.      Wearable Devices

The number of people willing to include wearable devices is increasing. Healthcare providers are taking advantage of this to monitor heart rate and other vitals so that diagnoses can be determined and procedures planned accordingly. The elderly community seems to benefit from this immensely. Integrating AI into ordinary medical devices and others might even diminish the need for a large number of devices to monitor each vital separately. Some experts are even trying to figure out whether the information found from such devices will help in preventing disease.

3.      Willingness of the General Public

Is it because it’s the latest healthcare marketing trend? Maybe, but perhaps the educated population sees the benefits garnered from easily using AI. The general public’s keenness to play a more active role in their health has reached a crucial point. A Care Anywhere survey discovered how advancements in technology and the extensive use of the Internet of Things (IoT) are breaking down healthcare barriers and letting care to be accessed from anywhere.

4.      Resource Allocation

A lot of the decisions lie on the shoulders of C-level executives. AI helps in reducing infrastructure costs and space that can be better allocated for technology and diagnoses identification. A lot of healthcare providers find that the infrastructure used currently is actually not required. Having a patient-centric system by integrating AI is found to be better than the hospital-centric approach. When dealing with a larger population, this might turn out to be the most efficient manner.

5.      Machine Learning (ML)

Machine learning (ML) algorithms are being used to figure out patterns and aid in diagnoses. Artificial intelligence and ML in healthcare helps detect even the slightest of changes in reports and is also used in precision medicine. The simulations created for pharmaceutical developers can speed up the discovery and development process.

6.      Natural Language Processing (NLP)

This form of AI detects human language. You might have already experienced it on your smartphones, smart TVs and more. But when it comes to healthcare, NLP improves accuracy as well as gives rise to more of a personalized approach to diagnoses. Dealing with more data at a time can also be done easily. Relevant treatments and potential threats can be found with an analysis of past history.

AI Usage in Each Healthcare Department

Want to know how AI helps in different healthcare departments? Let’s find out for some of the major ones.

Cardiovascular

AI algorithms in cardiovascular medicine are proving successful in diagnosing heart disease and stratifying patients, serving as a useful triage tool. These algorithms also predict patient mortality, medication effects, and outcomes post-treatment for acute coronary syndrome. Wearables and smartphones track cardiac data, improving early detection of heart issues. AI is being explored for classifying heart sounds and detecting valvular disease, but it is facing challenges like limited training data and a lack of comparisons between AI and humans. Recent research shows AI to be as effective as humans in interpreting cardiac tests and diagnosing heart attacks. AI also enhances cardiovascular tissue engineering and organoid studies by aiding in microscopy image analysis and integrating electrophysiological data.

Dermatology

Studies have shown successful skin cancer detection and classification using deep learning techniques. However, concerns have been raised about disparities in skin color representation. Recent developments have discovered the use of AI in assessing facial surgery outcomes and cleft palate therapy. Research has shown that a deep-learning AI system can perceive skin cancer more precisely than human dermatologists.

Gastroenterology

Endoscopic tests like Esophagogastroduodenoscopy (EGD) and colonoscopies need to detect abnormal tissue quickly. Integrating AI into these procedures can help clinicians identify diseases faster, assess their severity, and view areas that are difficult to see. Initial studies on AI detection systems for early gastric cancer have demonstrated similar sensitivity to expert endoscopists.

Infectious Diseases

AI in infectious diseases has demonstrated potential in both laboratory and clinical settings. While AI has been utilized for various purposes such as detecting COVID-19 responses and identifying antimicrobial resistance, its direct use in clinical practice during the pandemic has been limited. Other applications include AI analyzing blood smears for malaria, improving Lyme disease testing, and enhancing diagnosis and treatment prediction for various diseases.

Musculoskeletal

AI has been utilized to detect reasons for knee pain missed by doctors. Underserved populations have higher pain levels, even after adjusting for severe diseases like osteoarthritis. This suggests that external factors like stress may contribute to their pain. Researchers used a machine learning algorithm to reveal overlooked features that affect the diagnosis and treatment of underserved populations with knee pain.

Neurology

AI technologies have been investigated in neurology for diagnosing and predicting Alzheimer’s disease (AD). Machine learning models using structural magnetic resonance imaging (MRI) inputs have been developed for diagnosis, drawing data from databases like the Alzheimer’s Disease Neuroimaging Initiative. Convolutional neural networks have enhanced early diagnostic accuracy, while generative adversarial networks have shown promise in diagnosing AD. Additionally, efforts are being made to create machine learning models for predicting patient prognosis, using generative models to forecast outcomes and generate larger training datasets for future models.

Oncology

In oncology, AI has been explored for various purposes, including cancer diagnosis, risk assessment, tumor molecular profiling, and pharmaceutical discovery. One of the critical challenges AI addresses in oncology is accurately predicting the most effective treatment protocols tailored to each patient’s unique genetic, molecular and tumor characteristics. AI has shown promise in cancer diagnostics by converting images into numerical sequences for interpreting imaging studies and pathology slides. Recent studies have demonstrated that AI systems surpass human experts in breast cancer detection and achieve high accuracy in identifying prostate cancer. Additionally, AI has been successfully used for CT-based (computed tomography) radiomics classification to assess the aggressiveness of retroperitoneal sarcoma, outperforming traditional lab analysis of biopsies.

Challenges of AI in Healthcare

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Like any other, AI in healthcare doesn’t come without its own complications. With healthcare organizations spending more and more on utilizing AI across various tasks, tackling challenges specific to this technology is the need of the hour since numerous ethical and regulatory concerns may not be relevant in other industries.

Major challenges in the medical field include maintaining data privacy and security, patient safety and precision, training algorithms to identify patterns in medical data, integrating AI with current IT systems, obtaining physician acceptance and trust, and following federal regulations. Data privacy is of utmost importance as AI collects vast amounts of personal health data that could be misused without proper handling. Strong security measures are essential to safeguard sensitive patient information from exploitation.

Patient safety and precision are pivotal when utilizing artificial intelligence in healthcare. AI must be taught to identify patterns in medical data, comprehend connections between various diagnoses and treatments, and offer accurate recommendations customized for each patient. Additionally, integrating AI with current IT systems can add complications for medical professionals, requiring a thorough understanding of technology for seamless functioning.

Establishing trust and acceptance among healthcare providers is predominant for the effective implementation of AI in the medical field. Physicians must have faith in the AI’s reliability and transparency to allow for advice based on its research analysis. Furthermore, following federal regulations is necessary to guarantee the ethical use of AI and safeguard patient well-being.

Wrapping it up

There is no doubt that AI plays a major role in healthcare. Not only will it make all the processes quicker, but it will also greatly help humankind as a whole. Artificial intelligence in healthcare will improve overall health and bring prosperity to both the receivers and providers. Awareness of the rising trends will help us focus on the greater picture – a road to improvement.


Angelina Smith

Angelina Smith

Angelina Smith is a senior content editor who served 12 years in the execution of marketing strategies at eSalesData. Today, she leads the marketing department at eSalesData, a renowned database company based out of Fremont, CA.




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