10 Advanced Use Cases of AI in Healthcare

Artificial intelligence (AI) has emerged as a catalyst in healthcare. The healthcare domain has seen a massive transformation in the way medical professionals diagnose, treat, and manage patients. With the use of AI, healthcare providers can deliver more accurate and personalized care, reduce costs, and save more lives. Here is a list of 10 advanced use cases of AI in the healthcare domain.

1. Medical Imaging and Diagnostics: AI is revolutionizing medical imaging and diagnostics. With the use of deep learning algorithms, AI can analyse medical images such as X-rays, CT scans, and MRI scans to detect abnormalities, tumors, and other diseases at an early stage. AI-powered diagnostics systems can provide medical professionals with faster and more accurate diagnoses, allowing them to provide patients with appropriate treatments sooner.

2. Drug Discovery and Development: AI is transforming the drug discovery and development process. AI-powered algorithms can analyse vast amounts of data to identify new drug targets, design new molecules, and predict the efficacy of new drugs. AI can also help researchers repurpose existing drugs for new indications, reducing the time and cost of drug development.

3. Personalized Medicine: AI is enabling personalized medicine, which tailors medical treatments to individual patients based on their genetic, environmental, and lifestyle factors. With the use of AI-powered predictive models, medical professionals can identify patients who are at risk of developing specific diseases and recommend preventative measures.

4. Virtual Health Assistants: AI-powered virtual health assistants can help patients manage chronic conditions such as diabetes, heart disease, and hypertension. These assistants can provide patients with personalized recommendations for diet and exercise, monitor their symptoms, and remind them to take their medication.

5. Medical Records and Administration: AI can help healthcare providers manage patient records and administration more efficiently. AI-powered systems can analyse medical records to identify patterns and trends, helping medical professionals make better decisions. AI can also automate administrative tasks such as appointment scheduling, billing, and claims processing, freeing up medical professionals to focus on patient care.

6. Clinical Decision Support: AI-powered clinical decision support systems can help medical professionals make more informed decisions. These systems can analyse patient data, medical histories, and other relevant information to provide medical professionals with recommendations for treatment and care.

7. End-to-end Adoption: The application of AI in healthcare is a demonstration of augmented intelligence rather than solely artificial. It relies on the expertise of healthcare professionals, including doctors, researchers, and hospital staff, to provide the technical foundation for AI to learn. By handling the challenging tasks of big data processing and pattern recognition, AI relieves healthcare professionals from the burden of these tasks and allows them to focus on their strengths, such as problem-solving and innovation.

8. Intelligent Robot-assisted Surgery: The use of robot surgeons has been under testing for several years now, as they can mitigate the errors caused by human fatigue and exhaustion. Surgical procedures demand extreme precision and patience, and mechanical surgeons can maintain their level of expertise even when operating for long hours without interruption. By combining AI with computer vision software, robot surgeons can attain a new level of accuracy in even the minutest of movements, enabling them to carry out procedures independently. In addition, human surgeons can also leverage this technology as algorithm-assisted instrumentation can be utilized to perform procedures on a smaller scale that cannot be achieved by hand.

9. Improving Security and Reducing Frauds: The intelligent utilization of machine learning (ML) can enhance the security of patient data by safeguarding it from unauthorized access and potential threats. With the aid of AI, malicious activities such as hacking or suspicious access attempts can be traced and monitored, enabling security teams to focus only on the most vulnerable areas and relevant leads. Additionally, AI can help detect irregularities in billing and flag potential anomalies such as signs of kickbacks, upcoding, downcoding, or other fraudulent activities. By analysing vast amounts of data from electronic health records (EHRs), insurance claims, and annual budgets, algorithms can identify hidden patterns that deviate from the norm and alert healthcare providers to potential security breaches.

10. Automating Evaluation & Management (E&M): Scoring: Natural language processing (NLP) can be integrated with AI to streamline the laborious tasks associated with evaluating and managing facilities and professionals for the purpose of E&M leveling. E&M leveling is a medical coding process that supports medical billing by enabling healthcare providers to document and bill for the services they offer. By utilizing NLP in conjunction with AI, healthcare providers can expedite the E&M leveling process, thus reducing the workload and time required to complete this task.

AI is transforming the healthcare domain, enabling medical professionals to deliver more accurate, personalized, and efficient care. With the use of AI, healthcare providers can diagnose diseases at an early stage, develop new drugs more quickly, provide personalized treatments, carry out robotics surgery, improve security and reduce fraud, and manage patient records and administration more efficiently. AI is revolutionizing healthcare, and we can expect to see many more advanced use cases of AI in the future.