This blog discusses some major use cases of AI in healthcare.
Using AI in Healthcare Sector:
Some of the present and future use cases of artificial intelligence in healthcare include:
Intelligent Health Assistants
Artificial intelligence domains like natural language processing are improving chatbot technology remarkably. Intelligent medical chatbots are now able to understand human language and communicate with them effectively.
Virtual medical chatbots are playing an important role in assisting patients struggling with mental health. Such AI systems are trained to listen to the patient, detect their tone, and advise them accordingly. In case the person is suicidal or needs immediate help, chatbot assistants alert human professionals to intervene.
Intelligent health assistants are also making work easier for healthcare providers. There is no more need for a constant presence of a medical caregiver to monitor patients’ conditions, especially those suffering from chronic diseases.
A lot of clinical administrative processes can be handled by intelligent assistants. Moreover, these assistants can run initial medical checkups, give reminders for appointments, etc.
Read more about the benefits of healthcare chatbots in our blog here.
Robotic Medical Procedures
Robotic technology is gaining in popularity in all industries. Robotic-assisted surgeries are now becoming common in the healthcare field for several procedures like kidney transplants, cutting away cancer tissue from sensitive body organs, coronary arteries, etc. Robotic surgeries give medical surgeons more precision and motion area in complex surgical procedures.
Robotic technologies also help with the shortage of medical professionals. Robots can be trained to carry out procedures that require special skills. Although there is a need for constant monitoring, robots can help take off the load from doctors while they can focus on tasks that require more intellect.
Patient data available today is stored and managed through EHR (electronic health records) and EMR frameworks. This medical data helps healthcare individuals to implement predictive algorithms. Historical data regarding genetics, nutrition, diseases, etc. help to improve the medication procedures.
Doctors now get actionable insights on what the future course of a patient’s illness could look like and what treatment and medication can prove to be helpful.
Improvement of the new drugs discovery process is another aspect of precision medicine. Drug discovery is a complex and time-consuming process that requires a lot of research and testing. AI can prove helpful when it comes to understanding complex data patterns and implementing predictive modeling for adverse drug effects in long term, etc.
Medical imaging is an important application of AI in healthcare. Machine learning technologies like deep learning models, cognitive learning, image recognition, etc. help to improve diagnostic imaging accuracy.
Research studies have shown that AI-powered image analysis exceeds the accuracy of human experts’ diagnostics through medical images.
Image diagnostics in radiology, dermatology, pathology, etc. based on convolution neural networks are proven to give better diagnostic results.
AI in healthcare also helps with the planning of radiotherapy procedures. AI helps in the precise image preparation for radiotherapy cancer treatments. This is a laborious process for oncologists and takes a lot of time on specially designed software to help them locate tissues in narrow areas.
Artificial intelligence can largely make healthcare services efficient. Intelligent telehealth can help in providing timely medical care to patients. Digital healthcare infrastructure can connect hospitals, clinics, healthcare professionals, and patients through sensors and smart devices.
IoT-connected devices can monitor users’ health conditions like heart rate, sleeping pattern, etc., and at the same time alert the healthcare providers in case of abnormal conditions.
AI-powered Emergency Medical Assistance
Machine learning can significantly help emergency healthcare providers. Artificial intelligence tools are able to detect the severity of emergencies by sensing caller voice and surrounding sounds.
A research study has revealed that ML-powered tools can make more accurate diagnoses in emergencies like cardiac arrest calls than human emergency dispatchers. Such AI tools work on machine learning algorithms and continue to improve their performance by learning on the latest available relevant data.
This is an amazing contribution of AI in healthcare. In emergencies like a cardiac arrest, there is a need for instant medical assistance. There is not much room for wrong judgment of emergency scenarios.
AL solutions can help with efficient analysis of the emergency and dispatch medical assistance before a patient’s condition declines.
Predictive Intelligence for Medical Assistance
Predictive analytics is improving business processes across every industry. The healthcare industry is not behind with timely disease management and clinical decision support.
Predictive models in healthcare organizations make use of historical health data to learn patterns and behavior and to predict future health outcomes of clinical decision-making and medical treatment.
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Patients suffering from chronic diseases require constant monitoring of their health conditions. Predictive software can help caregivers to identify any potentially threatening situations to improve patient care accordingly.
AI is thus helping in making quick yet informed decisions at exactly the right moment in emergencies where a patient’s life can be at stake.
Predictive analytics also assist the healthcare system with predictive maintenance of medical equipment. Sensors are used to timely alert the maintenance staff in case a part of medical equipment needs replacement.
In this way, sudden downtime of medical machinery can be avoided which ultimately results in smooth clinical workflows.
Another important use case of AI predictive modeling is in supply chain management. Such machine learning models help with the accurate placement of orders and procurement of materials. This helps to avoid unnecessary waste of resources while making the overall process more efficient.
Predictive analytics is also helping to combat cybersecurity threats. Cyber attacks are becoming quite common with the availability of big data and their use in AI models. Cyberattacks target applications using customers’ confidential information to steal data. Some of these attacks also affect the smooth running of the software.
This is obviously a major concern in the healthcare industry making use of integrated digital health records for efficient work processes. Artificial Intelligence is helping cybersecurity engineers deal with cyber threats through predictive analytics.
Machine learning models using predictive analysis train on the dataset of network traffic including, normal and abnormal data packets. In this way, they are able to learn about existing and potential cyber threats.
These trained models can detect any potential cyber threat on time and alert the concerned personnel in case of any unusual data traffic. Cybersecurity professionals then pre-emptively tackle the situation before there is some major data theft or application breach.
Our blog here discusses 5 ways to use predictive analytics in healthcare software,
Human Brain Interface Technology
This is a prominent healthcare research area. Direct interfacing between a human brain and the software device without the use of keyboards, touch screens, monitors, etc. is called brain-computer interface technology (BCI).
Such healthcare technology can effectively help patients suffering from severe neurological diseases leaving them unable to interact with their environments meaningfully.
Latest technologies in the domain of artificial intelligence like deep learning can greatly improve BCI technologies. Large datasets on human interaction behavior can train such software to deliver a near-human experience to patients who have lost this ability.
According to the director of the Center for Neurotechnology and Neuro recovery at MGH, “By using a BCI and artificial intelligence, we can decode the neural activates associated with the intended movement of one’s hand, and we should be able to allow that person to communicate the same way as many people in this room have communicated at least five times over the course of the morning using a ubiquitous communication technology like a tablet computer or phone.”
For more on leveraging AI in healthcare, read this article put together by professionals from Harvard medical school and Massachusetts General Hospital.
Planning to Adopt AI in Healthcare Services?
The market value of AI in healthcare is expected to reach 36.1 billion US dollars by 2025. Big data, IoT, and cloud computing technologies, together with AI, can help achieve great benefits in the healthcare sector.
As opposed to traditional machine learning and rule-based systems, intelligent machine learning models are helping clinical staff by taking over repetitive tasks and implementing efficient predictive analytics.
Improved healthcare services, personalized patient experience, robotic surgeries, augmented digital healthcare infrastructure, etc. are some of the prominent uses of AI in healthcare. If you are planning to implement a machine learning model for your healthcare product, you are making a good business decision.
Keep in mind that you will need expert software engineers to implement AI in healthcare effectively. Your software engineers should be experienced in advanced machine learning, predictive analytics, data science, industry compliance standards, etc. to develop a market-competitive application powered by AI.
You can read our blog here to build a competent team of software engineers for this project. If you are still looking for an expert software development team, DevTeam.Space can help you.
You can get in touch with DevTeam.Space via this quick form. One of our technical managers will get back to you to answer your questions in detail and to connect you with the right software developers who are experienced in developing market-competitive solutions for AI in healthcare.
Top FAQs on AI in Healthcare
Artificial intelligence in healthcare is the application of AI technologies like machine learning, cognitive learning, natural language processing, and big data analysis to medical procedures to assist not only healthcare providers but also patients.
Prominent applications of AI technology in the healthcare sector include preventive care, predictive diagnosis, robotic medical procedures, drug discovery, medical imaging, clinical documentation and medical records management, etc.
AI tools for emergency medical treatments are able to detect and monitor verbal and non-verbal clues from emergency callers. AI tool, Corti, is an example. Corti is trained on machine learning algorithms and is able to detect the seriousness of an emergency situation through the caller’s voice, background noise, etc.