Top 7 Applications of AI in Healthcare

Daffodil Software
5 min readJan 30, 2023

As the world around us changes rapidly, our aging populations, as well as public health crises ramp up the workload of providers, causing patient safety risks and negatively impacting patient satisfaction.

There exists an untapped potential for modern technology to propel changes in the healthcare system to increase productivity, improve the efficiency of care delivery and enhance patient satisfaction. And, Artificial Intelligence (AI) is one such technology that has offered a real opportunity for medical organizations to deliver better quality care.

As per the new report by market research firm Acumen Research and Consulting, the global market of AI in the healthcare industry is expected to rise to $8 billion by the year 2026.’

AI is being used or trialed for various healthcare and research purposes, including the management of chronic conditions, detection of disease, delivery of health services, and drug discovery. It allows physicians and other medical professionals not only to automate their routine tasks but also to be more efficient, quickly solve problems, and improve the patient experience. In this article, we’re going to discuss the role of AI in the healthcare industry and how it makes care adoption and delivery a seamless experience for patients & providers, respectively.

Applications of AI in Healthcare

From using it to detect links between genetic codes or to maximize hospital efficiency, AI is being leveraged to deploy efficient and precise inventions that will help take better care of patients. AI in healthcare offers several benefits over traditional methods of analytics and making clinical decisions. By using AI algorithms, the systems become more accurate due to their ability to understand training data, which further allows humans to gain unprecedented insights into treatment variability, diagnostics, and patient outcomes.

1. Better Clinical Decision-Making

While diagnosing patients, it is essential for health professionals to take all crucial pieces of information into account, which means working with unstructured data in medical records. Using multiple AI engines and Natural Language Processing, we can store and process large sets of data, facilitating evaluations and recommendations of each patient individually, thereby improving clinical decision-making. We can also extract the main crux of the patient’s conversation with the physician and summarize it into better-structured and more accurate data points using Transcription Generation and Text Summarization techniques. This helps save time in documentation, reduces physician burnout, and improves quality care.

ALSO READ: How Does NLP Fully Leverage Unstructured Healthcare Data?

2. Improved Primary Care through Chatbots

Chatbots, which is an AI-powered service, incorporated with ML algorithms and NLP allow patients to raise their health-related queries and guide them to deal with the problem. These chatbots are 24/7 available and help in enabling smooth flow and automation of primary care. This healthcare solution aids in reducing the load on healthcare experts, allowing them to focus on other crucial and dire cases. It also saves time and money on avoidable trips to physicians as well as unnecessary hospitalizations.

3. AI-enabled Robotic Surgeries

AI and collaborative robots have revolutionized surgical procedures. Several healthcare providers are implementing robotics that assists them during surgeries that require speed, precision, control, and flexibility. This precision helps prevent unintentional or accidental movements during surgeries, resulting in lesser complications, decreased patient safety risks, and a faster recovery rate. An example of a robotic surgery
that successfully repaired abnormal openings between the uterus and the bladder using AI-enabled robots.

4. Disease Prediction

Chronic diseases are increasing in prevalence and mortality worldwide. However, artificial intelligence techniques are being used to predict such diseases based on available patient data. These techniques and algorithms can help physicians to identify such conditions much quicker, promoting early intervention and a faster decision-making process. It can also reduce the time and cost involved in analyzing scans, potentially allowing more scans to be taken to offer better target treatment. This technique has shown significant results in detecting conditions such as breast and skin cancers, cardiac arrest, pneumonia, and eye diseases. Therefore, early disease risk identification is beneficial for patients to start treatment early and also motivates them to change their lousy eating, lifestyle, and exercise habits.

5. Monitoring Health through AI-enabled Wearables

AI-powered wearables combined with edge computing might be the new edge for hospitals and healthcare systems. According to GMI, wearables AI in the healthcare market size is projected to reach $180 billion by 2025. It can help patients with greater efficacy by offering them virtual instructions, and if needed, can connect to live healthcare experts for assistance. Additionally, they are responsible for coordinating medical records and covering sensitive data for patients, scheduling appointments with physicians, sending follow-ups and clinical appointment reminders to patients, and so on.

For instance, machine learning algorithms can be found in wearable heart monitors and smartphone apps, that allow healthcare experts to monitor heart conditions and give instructions remotely. Because of their mobility, accuracy, and ease of use, AI-powered wearables can be wonderful aids for healthcare providers who need to improve their clinical processes and gain cost efficiencies.

6. Automated Analysis for Radiographic Images

By leveraging big data and deep learning technologies, healthcare professionals can extract useful information from complex images, including those from CT scans and MRIs. As a result of analyzing the patterns in a digital image, the algorithm can provide additional metrics and results to complement the radiologist’s analysis, which can contribute to an earlier diagnosis.

In addition to making an ideal diagnosis on time, it can also improve radiation workflow by achieving quality control and efficiency, as well as automating risk stratification, which is an effective way to identify patients at higher risk. AI-based medical imaging diagnosis will not eliminate radiologists, but rather expand their role in diagnosis and decision-making, which will help reduce medical errors and accidents.

7. Decreasing the Burden of EHR Usage

EHRs have played a pivotal role in advancing healthcare’s digitalization. However, the switch to EHRs has also introduced several issues for admin staff such as cognitive overload, endless paperwork, and user burnout. By leveraging AI in the healthcare system, EHR developers have created more intuitive interfaces and automated some of the routine tasks that are time-consuming for end users. Additionally, AI can be used to process routine requests from inboxes, such as medication refills, and send notifications as a result. Likewise, it can prioritize tasks that need the clinician’s attention, making it easier for the users to manage their to-do lists.

Deliver Quality Patient Care with AI Solutions

The adoption of AI in healthcare continues to face challenges, including a lack of trust in the results delivered by ML systems and meeting specific requirements. However, the applications of AI in healthcare have already brought multiple benefits to healthcare providers.

By enabling faster service, improving workflows, assisting medical and nonmedical staff with repetitive tasks, and developing accurate diagnosis and data analytics, patients, researchers and physicians can all benefit from the use of AI in healthcare.

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