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AI in Healthcare

How to Leverage AI in Healthcare

Estimated read time: 10 minutes

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 the following:

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 in real-time. According to a study, 52% of US consumers want to use virtual voice assistants for healthcare use cases.

Virtual medical chatbots play an essential 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 also make work easier for medical providers. There is no more need for a constant presence of a medical caregiver to monitor patients’ conditions, especially those suffering from chronic diseases. 

The remote patient monitoring (RPM) market is expected to reach 175.2 billion USD by 2027. Cardiovascular RPM AI solutions dominated the US market in 2023, according to a research study.

A lot of clinical administrative processes can be handled by intelligent assistants. These assistants can run initial medical checkups, give reminders for appointments, etc.

EBO.ai, an AI company working on automating conversations, has surveyed NHS users and found out that 76% of users would be happy to receive an automated appointment reminder from an AI-powered virtual assistant instead of being contacted by a human.

Read more about the benefits of healthcare chatbots in our blog here.

Robotic Medical Procedures

Robotic technology is gaining popularity in all industries. The global robotic technology market value is expected to exceed USD 255.6 billion by 2030.

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Robotic surgeries have now become 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.

Robotic surgery technology is expected to shift from semi-autonomous robots to autonomous robots increasing the market share of autonomous robots from 43.8% to 46.9% by 2029.

Precision Medication

Patient data available today is stored and managed through EHR (electronic health records) and EMR frameworks. This medical data helps healthcare individuals implement predictive algorithms. The global precision medicine market size is expected to reach USD 254 billion by 2032.

Historical healthcare data regarding genetics, nutrition, diseases, etc., help improve 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 could be helpful.

Improvement of the new drug 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 intricate data patterns and implementing predictive modeling for adverse drug effects in the long term, etc. Given these benefits, the global market revenue of AI in the drug discovery process is to reach USD 4 billion by 2027.

Precision Diagnostics

Medical imaging is an important application of AI in healthcare. Machine learning technologies like deep learning models, cognitive learning, image recognition, etc., help improve diagnostic imaging accuracy. 

The global medical imaging market size is projected to reach $68.8 billion by 2030. The main growth driver is the surge in demand for high-quality optical images for better diagnoses and treatments.

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.

The United States holds the largest share in the AI Oncology market, with a projected global market size of $1.142 billion by 2032.

Augmented Healthcare

Artificial intelligence makes healthcare services efficient greatly. Intelligent telehealth can help in providing timely medical care to patients.

Digital healthcare infrastructure connects hospitals, clinics, healthcare professionals, and patients through sensors and smart devices, such as

  • Wearable devices, like smartwatches, sensor patches, etc.;
  • Wireless vital monitors;
  • Implanted devices, like pacemakers;
  • Connected inhalers, etc.

IoT-connected devices monitor users’ health conditions like heart rate, sleeping pattern, etc., and at the same time alert the healthcare providers in case of abnormal conditions.

According to a research study, the average cost of telehealth services for patients dropped by 61% compared to in-person healthcare sessions.

cost reduction due to AI-based teleheathcare

AI-powered Emergency Medical Assistance

Machine learning can significantly help emergency healthcare providers. Artificial intelligence tools can detect the severity of emergencies by sensing the caller’s voice and surrounding sounds. 

A research study has revealed that ML-powered tools can make more accurate diagnoses in emergencies, like during cardiac arrest calls, than human emergency dispatchers.

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This is an amazing contribution of AI in healthcare. In emergencies like cardiac arrest, there is a need for instant medical assistance. There is not much room for wrong judgment. 

AI solutions help with efficient analysis of the emergency and dispatch medical assistance before a patient’s condition declines.

For example, an automatic speech recognition system (ASR) for the detection of out-of-hospital cardiac arrests (OHCA) has proven to increase the sensitivity of the OHCA system by 11.6% and decrease the median time-to-recognition by 10 seconds.

Predictive Intelligence for Medical Assistance

Predictive analytics is improving business processes across every industry. The healthcare industry is not behind with several use cases, like timely disease management and clinical decision support. 

Predictive models in healthcare organizations make use of historical health data to learn patterns and behavior and predict future health outcomes of clinical decisions and medical treatment.

Patients suffering from chronic diseases require constant monitoring of their health conditions. Predictive software can help caregivers identify any potentially threatening situations to improve patient care. AI thus helps make quick, informed decisions at exactly the right moment when a patient’s life can be at stake.

For example, a medical company, Siemens Healthineers, is actively leveraging machine learning in AI-driven clinical decision support tools. One example includes the liver disease severity algorithm.

The predictive model could help with early identification and intervention to avoid progression to liver failure and liver transplant.

Predictive analytics also assist the healthcare system with the 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.

Hitachi, a multinational corporation, used machine learning to define normal operational states and create a mechanism for early detection and investigation of abnormalities that lead to MRI device failures. As a result, the downtime of MRI systems was reduced by 16.3%.

Predictive analytics also helps 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 that use customers’ confidential information to steal private data. Some of these attacks also affect the smooth running of the software.

This is a major concern in the healthcare industry as it uses integrated digital health records for efficient work processes. In the third quarter of 2022, 1 in 42 healthcare organizations was affected by ransomware attacks.

Artificial Intelligence helps cybersecurity engineers deal with cyber threats through predictive analytics. Predictive machine learning models train on the network traffic data that includes normal and abnormal data packets. In this way, the ML model learns to differentiate between normal traffic and potential cyber-attacks.

In case of any unusual data traffic, these trained models alert the concerned cybersecurity personnel, who then pre-emptively tackle the situation before there is some major data theft or application breach. 

Such artificial intelligence-powered cybersecurity in the healthcare sector reduced the cost of breaches by 50%. Our blog here discusses 5 ways to use predictive analytics in healthcare software,

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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 that leave them unable to interact with their environments meaningfully.

BCI industry is projected to grow at a CAGR of 14.3% from 2021 to 2027. The massive funding programs for BCI-based research all over the world worth billions of dollars indicate the rising interest in the technology.

Some initiatives include the Human Brain Project of the European Union, the BRAIN Initiative by the United States, and the China Brain Project.

The latest advancements in the domain of artificial intelligence, like deep learning algorithms, including convolution neural networks, Generative Adversarial Networks, etc., can greatly improve BCI technologies.

Large datasets on human interaction behavior could train such software to deliver a near-human experience to patients who have lost their communication abilities.

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.”

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 help clinical staff by taking over repetitive tasks and implementing efficient predictive analytics.

The healthcare industry benefits from improved healthcare services, personalized patient experience, robotic surgeries, augmented digital healthcare infrastructure, etc., through AI adoption.

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, and industry compliance standards 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 with experience in developing market-competitive AI solutions for healthcare.

Top FAQs on AI in Healthcare

1. What is healthcare AI?

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.

2. What are the top applications of AI in the healthcare industry?

Prominent applications of AI technology in the healthcare sector include preventive care, predictive medical diagnosis, robotic medical procedures, drug discovery, medical imaging, modeling patient outcomes from clinical trials, clinical documentation, medical records management, etc.

3. How does AI help in emergency medical assistance?

AI tools for emergency medical treatments can 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 can detect the seriousness of an emergency through the caller’s voice, background noise, etc.


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