AI Implementation in Healthcare

AI can make human jobs easier, we all know. Applying AI in human healthcare is the most remarkable achievement ever made.

A study tells that there are a lot of technical challenges healthcare leaders are facing while implementing AI in their jobs.

Latest advancements and algorithmical improvements in AI are moving towards the goal of achieving it too.

The global healthcare AI market size was valued at USD 15.4 Billion in 2022 and is expected to grow with a CAGR of 37.5% from 2023-2030

TL;DR -  This blog is going to discuss how artificial intelligence is being used or implemented in the healthcare system. If you are already aware of your pain point, you could make use of iMeta’s AI consulting service for your project.

AI in Radiology

Radiologists have a significant potential to raise the standard of treatment and highlight the importance of radiography in patient care and public health through the use of artificial intelligence (AI). 

Given that radiographs are the most common imaging tests carried out in the majority of radiology departments, the potential for AI to assist in the triage and interpretation of traditional radiographs (X-ray pictures) is especially noteworthy.

The development of AI algorithms for the interpretation of chest and musculoskeletal (MSK) radiographs has advanced significantly in recent years, with deep learning now holding a leading position in picture analysis.

AI in Early Diagnosis

A clinical pattern set that was retrospectively generated from the records of 351 hospitalized patients who had a high risk of suffering a myocardial infarction was used to train an artificial neural network. 

331 consecutive individuals who presented to an emergency room with anterior chest discomfort were prospectively screened for it.

The surprising fact is the physicians had a diagnostic sensitivity of 77.7% while artificial neural networks had a sensitivity of 97.2%.

Early diagnosis is the biggest lifesaver for many patients and helps doctors in guiding them towards best treatment options.

AI in Medical Paperwork

The patient's data will be stored in the electronic system and an AI model is built to interpret it. In old times, physicians have been poring over patient notes for years in order to piece together a picture of a patient's blood results that now have years' worth of data at their fingertips.

When the usual records of the patient get abnormal, the AI model will alert the system and alert will be sent to doctors. 

Physicians at the Royal Free NHS hospital claim that working with Google's artificial intelligence division, DeepMind, may free up more than half a million hours annually from paperwork to be used directly for patient care. 
 

AI in Predicting Errors

Based on an Oxford academic study, artificial intelligence can predict physician errors by sampling the output of health outcomes.

By using an algorithmic model to calculate a patient's chance of having a heart attack. The AI model may find instances in which doctors' testing choices diverge from expected risk. 

Then, AI can assess whether those variances are the result of medical errors or better understanding on the part of the doctors using real health outcomes. 

There are two outcomes with this method. Doctors overlook: low-risk individuals are screened foreseeably but receive no benefit from the testing. 

Simultaneously, doctors low value care: it is predictable that high-risk patients who are not evaluated would experience unfavorable health outcomes.
 

AI in laboratory Testing

Testing for infectious diseases is about to change as a result of machine learning (ML) and artificial intelligence (AI). In the laboratory, infectious disease testing is one of the few technologically varied fields where several equipment and methods may be needed to assist clinical decision-making.

Human analytical constraints restrict the huge diversity of infectious illness data, even with breakthroughs in laboratory informatics. 

Machine learning may overcome human constraints and leverage many data sources, including but not limited to laboratory information, to give doctors answers that are both predictive and actionable. 

AI in Customer Support

The majority of clinical workers time is spent in responding to patients enquiry, aligning them in waiting rooms, distributing medicines guidelines etc. This can be achieved by using an AI model that can answer patients' queries on predictive analysis.

With the help of AI, you can provide best healthcare customer support by offering 24/7 chatbot assistants, proactive outreach, improved wait times, multilingual accessibility, and sentiment analysis. 

Related - Best AI Implementation with Examples
 

AI in Health Lifestyle

AI-powered wearables and apps track your activity, sleep, and eating habits, providing personalized insights and recommendations for exercise, nutrition, and overall health goals. 

AI assistants help schedule tasks, manage time effectively, and form healthy habits, like regular exercise or meditation.

You can connect your health records like walking distance, heart rate with mobile devices and get personalized advice on it. Apple’s lifesaving SOS in the watch alerts healthcare professionals if any abnormal heart rate is found out. 

Besides that, there are numerous applications where AI engineers are working in the healthcare niche that improves the ecosystem.

AI in Surgery

AI algorithms can analyze medical images, such as MRIs and CT scans, to create 3D models of organs and tissues. This allows surgeons to visualize complex anatomy in detail and plan procedures with pinpoint accuracy, reducing the risk of complications. 

AI-powered surgical robots can perform delicate procedures with risk-free movements and unparalleled precision, minimizing tissue damage and improving patient’s healing time.

Implement AI in Healthcare with us

Implementing AI in healthcare with iMeta Technologies goes beyond your expectations. We create a connected AI ecosystem where information flows seamlessly between hospitals, clinics, and patients.

Not just that, we research and develop AI solutions from your pain points in healthcare and come up with the specific answer to sort out.  The in-house R&D in Artificial intelligence will assist you from project ideation to finish.

We can empower healthcare providers with a comprehensive suite of AI-powered solutions. It will definitely improve the doctor's-patience relationship, break down data interpretations, and ensure everyone involved in a patient's care has access to the most up-to-date information.

Read out our AI healthcare app case study to understand our capability better. If you need a similar AI powered healthcare app, or any custom AI solutions, contact us.

Reference

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9250210/

https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-healthcare-market

https://www.acpjournals.org/doi/epdf/10.7326/0003-4819-115-11-843

https://www.bbc.com/news/health-38055509

https://academic.oup.com/qje/article/137/2/679/6449024

https://www.livemint.com/technology/tech-news/apple-watch-detects-undiagnosed-heart-condition-saves-life-of-a-36-year-old-11678599305199.html