The healthcare industry is evolving constantly, and the data used in it is becoming more complex. Hence, the industry’s need for accuracy and speed in gathering and accessing data has become paramount for healthcare providers to offer effective medical care. Generative AI for marketing is being used for managing health information, pharma marketing, surgical operations, etc. AI technologies such as machine learning, deep learning, and natural language processing, are now being used to imitate human functioning which can possibly supersede humans in areas like disease diagnosis. One cannot deny that artificial intelligence has proven its capabilities to learn tasks and perform them like humans do.
For example, biopharmaceutical companies can use AI along with real world evidence for drug development and improve patient outcomes. Its application can be seen in many other ways in the healthcare industry, some of them are:
- Disease diagnosis and specific treatment planning
- Assisting medical research
- Enhanced patient monitoring and interactions
- Supporting administrative activities
AI is used in many ways to improve productivity, streamline tasks, and provide guidance during complex procedures to make them less time-consuming and reduce effort. It can also be used to bridge gaps and make healthcare accessible to all in developing countries that have limited resources and infrastructure. Some of its capabilities that can make healthcare accessible are remote monitoring, digital self-assessments, and data extraction.
Types of AI used in healthcare
Various technologies are used individually or in conjunction to support tasks in healthcare. These include: –
1. Machine Learning
Machine learning is one of the most used forms of AI in healthcare as it is the core technology that has given rise to many AI applications. Lately, it is being used extensively in precision medicine for customized lines of treatment taking in factors including genes, lifestyle, etc., for specified outcomes.
Deep Learning is a form of machine learning and a popular neural network that is used to categorize raw medical data to drive valuable healthcare decisions. With the help of machine learning and deep learning, researchers can develop models to predict drug molecules accurately.
2. Physical Robots
Physical robots are being used in many industries, including healthcare. Till now, surgical robots have helped extensively to make surgical procedures quicker and easier due to their high adherence to precision and perfection. Their incisions are minimally invasive, and they use 3D mapping of surgical sites for improved visibility to stitch wounds quickly. These are some of the advantages of physical robots used to reduce errors and make surgeries successful.
3. Natural Language Processing
NLP, or Natural Language Processing, is a technology that aims to understand and interpret human language. NLP can mimic human language abilities as it can recognize spoken words and understand text. Speech-text analysis and translation use NLP, which can help with the creation and classification of medical documentation and research. Transcribing doctor-patient interactions, which can give better analysis and outcomes, improving triage systems, creating diagnostic models, etc., are some examples of the uses of NLP.
4. Robotic Process Automation
Robotic process automation uses computer programs or bots, not physical robots to perform repetitive tasks. It helps to simplify administrative workflow and makes support systems in healthcare effortless with improved patient care and better efficiency. Using RPA in healthcare includes appointment scheduling, updating patient health records, insurance claims processing, bill settlement, and more. Robotic process automation is used extensively to perform data extraction for system input along with image recognition technology, including x-rays, producing reports or digital images of patients located in remote areas for medical support from healthcare professionals.
Use Cases of AI in Healthcare
AI in the healthcare industry has been found to tackle some of the most common and significant problems efficiently. Here are some of its examples:
1. Accurately diagnose and minimize errors
Accurate diagnosis is critical in healthcare as faults in the result can cause physical suffering and pain, lead to substantial medical expenses, and cost lives. Though AI is still in the initial stage, it does have the potential to diagnose diseases, which includes cancer as well.
2. Development of new drugs
It is a continuous effort to find new drugs in the medical field to tackle constantly evolving diseases. AI has a significant efficiency and accuracy rate, which allows it to perform tasks with the help of machine learning. This is quite helpful in the process of drug discoveries. Its models can run different programs to identify resources and carry out clinical trial stage to analyze, monitor, and produce results in less time.
3. Monitor patients with virtual nurses or bots
AI can help with inpatient remote monitoring at faraway locations or in hospitals. For instance, virtual nurses equipped with AI can help with round-the-clock monitoring of critical patients’ activities in intensive care units or ICUs. They can also provide blood reports and analyze images of patients located in remote areas for better diagnosis and medical advice. Additionally, AI-based virtual bots can be used to facilitate ongoing communication with patients when hospital visits are not scheduled. It can help answer questions of the patients regarding admission, help with diet schedules, doctor availability, etc.
Due to AI, high-quality healthcare access to a larger audience has been made possible. This is because it has the capacity to understand, learn, adapt, interpret, predict, and guide in healthcare.