The Role of Artificial Intelligence in Healthcare Diagnosis

Access to quality healthcare services is a basic human right that is out of reach for many in the world. With covid raging havoc, now more than ever, the need for technological innovations in the healthcare division is the need of the hour. Healthcare Artificial intelligence (AI) and data science is a growing market that undoubtedly is going to impact our lives. 

Many hospitals have already embraced chatbots for appointment scheduling. An AI-powered chatbot can take the load of the front desk by handling more than just appointment queries. From diagnosing respiratory illnesses to forecasting diseases, AI as a tool can be trained to correctly interpret health data by feeding large complex datasets. Hospitals need to embrace healthcare artificial intelligence in the coming years to improve their service level.

At SolutionChamps Technologies, we have years of experience working with healthcare clients and creating AI-backed tools. In this article, we will explore how AI-backed tools and technologies are aiding the healthcare industry. 

Overview

• AI in Disease Diagnosis
• AI techniques and Clinical diagnosis

AI in Disease Diagnosis

It takes years of medical training and hands-on experience for a doctor to skillfully and correctly diagnose a disease. From assessing a patient’s symptoms and analysing the test results, diagnostics is a crucial and time-consuming medical process. In many branches of human science, there is a huge demand for expert physicians and patients need to wait for days for clinical diagnosis even in developed countries. This is where AI-backed Machine Learning and Deep Learning algorithms play a pivotal role. AI-backed clinical diagnostics of diseases are accurate, cheap and convenient. 

A recent study published by Science Daily says that AI systems help in detecting even the most subtle changes in a patient at a very early stage. From remarkable detection of breast cancer to ECG, EEG or even X-ray images, machine learning and AI techniques are paving the way for healthcare advancements.

How AI diagnoses Work 

Just as a doctor would need to identify patterns to diagnose a disease, machine learning algorithms are pre-trained with datasets to identify patterns. Multiple predictive analysis techniques and deep neural networks (DNNs) are used in AI clinical diagnosis. 

 Machine learning algorithms trained by expert professionals can help in an accurate diagnosis. 

AI-backed machine learning is currently in action to perform diagnoses such as-

• Detect lung cancer and stroke based on CT scans
• Predict cardiovascular risks by measuring blood flow and diagnose cardiovascular disease based on electrocardiograms and cardiac MRI images
• Classify skin lesions using Skin images and patterns
• Detect diabetic retinopathy by accurately interpreting retinal scans
• Identify areas containing tumor or healthy tissue using MRI imaging 
• Detect cancer-causing lesions in Endoscopic procedures

AI techniques and Clinical diagnosis

Clinical diagnosis using artificial intelligence is implemented using the following AI techniques and AI fields: 

• Computer vision
• Time-series analysis
• Speech recognition, and
• Natural language processing

Computer Vision

Computer vision and machine learning are fields of AI that involve training the AI system’s algorithm to see and process images just like a human would. The system is trained using digital images, videos and other visual inputs. The supervised or unsupervised AI system then takes actions or makes a recommendation. When it comes to healthcare, computer vision is applied in tumor and cancer detection, medical imaging, in the fight against and health monitoring. According to a recent study, computer vision in the healthcare market is projected to reach USD 1,457 million by 2023 from USD 210 million in 2018, at a CAGR of 47.2% during the forecast period. 

Time-Series and Predictive Analysis 

Time series analysis is beneficial in collecting observational data over a period of time frame. It is helpful in predicting a patient’s prognosis and potential risk factors. From cardiac arrest prediction tools to predicting brain tumour and even predicting the prognosis of respiratory illnesses and the prognosis of a covid patient, this branch of AI is churning out accurate predictions.

Speech Recognition Techniques

Documentation is a time-consuming administrative task that takes away the time of a clinician. Speech recognition tools are used in healthcare to increase productivity. Less paperwork has already benefited clinicians and frontline workers to focus on the patients. When it comes to clinical diagnosis, speech-recognition techniques are used in identifying neurological disorders. Recently, Amazon has applied for a patent for Voice-based determination of physical and emotional characteristics of users. From detecting sore throat to analysing a person’s voice and predicting a risk of suicide, researchers are hoping to use speech analysis in diagnostics.

Natural Language Processing 

AI-based chatbots can interpret natural language and do a preliminary assessment of a patient’s symptoms. Natural Language Processing is also helpful in extracting data from digital health record data made using Optical Character Recognition (OCR).  

At SolutionChamps, we help organizations in their digital transformation journey by incorporating innovative techniques and resilient solutions. Contact us today for a quote.