There are, a handful of latest AI research and implementations in healthcare industry. One example is the use of chatbots by healthcare providers to communicate with patients. Another is the use of machine learning algorithms to predict and prevent diseases.
Studies of Artificial Intelligence is wide spreading now a days. Let us have a look through all the criteria belong to healthcare.
1. AI in Primary care
AI in primary care is a process or application that uses cognitive computing or artificial intelligence technologies to assist primary care physicians and other healthcare professionals in making better decisions and diagnoses for their patients. AI in primary care can help to improve the accuracy and speed of diagnosis, as well as provide recommendations for treatment.
Primary care has become one fundamental development area for AI technologies. This AI comprises for supporting decision making, predictive modelling, and systematic computational analysis for business. However, the role of AI in primary healthcare has not been established in wide range.
2. AI in Diagnosis of Disease
The application of artificial intelligence techniques to the diagnosis of disease. This could include the use of machine learning algorithms to analyse medical images or text data from patient records in order to identify patterns that could indicate the presence of a particular disease.
The most popular AI techniques such as support vector machines, neural networks, and decision trees used to diagnose several different diseases,. Each of these techniques has a “training goal” so “more accurate result as much as possible”.
Medical Learning Classifiers (MLC’s), Artificial Intelligence diagnoses through the manipulation of mass Electronic Health Records (EHR’s). There is a vast history of deceases and medical conditions. AI will detect the decease for which a physician could not be able to diagnose. AI is to help physicians in this way.
3. AI in Screening
AI in screening is the use of artificial intelligence technology to help identify potential risks or issues in a process or system. AI can be used in a number of ways in screening, including to help identify potential risks or issues in a process or system, to help identify potential areas for improvement, to help assess and track performance, and to help automate decision making.
Maxillo-facial surgery or the evaluation of cleft lip and cleft palate therapy regarding age appearance or facial attractiveness, AI describes and evaluates the best results.
Google Deep Mind algorithm, that is capable of outstanding in breast cancer detection.
AI system (deep learning Convolutional Neural Network) detects skin cancer more accurately. On average, the human dermatologists accurately detected 86.6% of skin cancers from the images, compared to 95% for the CNN AI.
An AI algorithm by the University of Pittsburgh achieves the highest accuracy in identifying prostate cancer, with 97% specificity and 98% sensitivity.
4. AI in Psychiatry
AI Psychiatry is the application of artificial intelligence techniques to psychiatric problems and research. AI Psychiatry seeks to improve psychiatric diagnosis, treatment, and research through the use of machine learning, natural language processing, and other artificial intelligence techniques.
AI Chatbots and conversational agents are checking human behaviour which evaluates anxiety and depression. Facebook (in 2017) has tried for the screening suicidal ideation. However AI engineers are facing challenges outside the healthcare system raise various professional, ethical and regulatory questions.
5. AI in Telemedicine
Telemedicine is the use of telecommunication and information technology to provide clinical health care from a distance. It has been used to provide care to patients in remote areas and to provide support to health care professionals in the diagnosis and treatment of patients. AI in telemedicine is the use of artificial intelligence technology to assist in the diagnosis and treatment of patients. AI can provide support to health care professionals in the diagnosis and treatment of patients by providing information on the patient’s condition, predicting the outcome of treatment, and providing recommendations on the best course of treatment.
Usage of pulse oximeter to measure blood oxygen levels in aged people.
Term Telemedicine, the treatment of patients remotely, is the sign of rise of possible AI applications. AI devices can assist in caring for patients remotely by monitoring their information through sensors. The general concept is to use sensors may be attached to body of a patient or put around him/her. These devices may keep for continuous monitoring of a patient and the ability to evaluate changes that are very hard to keep track, not possible for humans for 24/7. All the information gathered can be compared to other data with AI algorithms which alert doctors in case of any issue.
6. AI in Radiology
Artificial intelligence (AI) is the process of making a computer system “smart” – that is, able to understand complex tasks and carry out complex commands. In radiology, AI is used to help doctors diagnose diseases and to help radiologists read and analyze x-rays and other medical images. AI can also be used to predict how a disease will progress or to plan the best treatment course for a patient.
Through Computerized Tomography (CT) and Magnetic Resonance (MR) Imaging, AI is to detect and diagnose diseases within patients by radiologists. Many organisations have their modules such as QUIBIM, icometrix, Robovision, and UMC Utrecht’s IMAGRT to provide a trained machine learning model to detect a wide range of diseases.
However, one should not be too pessimistic nor optimistic. But AI engineers are exerting their energies to bring a revolution in radiology.
7. AI for Creation of New Drugs
Artificial intelligence (AI) is a technology that can be used to create new drugs. AI can be used to identify new drug targets, design new drugs, and test new drugs. AI can also be used to predict how a particular drug will interact with a patient’s body.
Through joint efforts of Exscientia and Sumitomo Dainippon Pharma (Japanese pharmaceutical firm) invented DSP-1181, a molecule of the drug for OCD (obsessive-compulsive disorder) treatment, by artificial intelligence spending a year, whilst pharmaceutical companies usually take about five years on similar projects. DSP-1181 was accepted for a human trial
Generative Tensorial Reinforcement Learning (GENTRL), designed the new compounds in 21 days via artificial intelligence, in detection of fibrosis and other diseases.
Canadian company Deep Genomics is also using AI-based drug discovery platform has identified a target and drug candidate for Wilson’s disease regarding genetic mutation.
8. AI in Drug Interactions
Artificial intelligence (AI) can help clinicians predict and prevent potentially harmful drug interactions. AI can analyze patient data to identify risk factors for drug interactions and recommend interventions to prevent them. For example, if a patient is taking multiple medications, AI can recommend dose adjustments or drug interactions to avoid.
AI algorithms were successful to determine the drug interactions for the patients who are using multiple drugs at time.
FDA Adverse Event Reporting System (FAERS) and the World Health Organization’s (WHO), VigiBase authorize doctors to submit their reports of possible negative reactions of medications. Deep learning algorithms will developed to compute these reports and evaluate patterns that comply drug-drug interactions.
In this blog, I did my best to discuss the latest advancements in artificial intelligence (AI) research and implementations in the healthcare industry. I provided an overview of state-of-the-art AI techniques and their applications in different domains of healthcare such as diagnosis, treatment, and patient care. The blog also discusses some of the challenges and opportunities in deploying AI technologies in the healthcare domain.