Introduction
AI is driving a new era in healthcare, with significant improvements in diagnostic accuracy, individualized treatment plans, and operational effectiveness. AI technologies, such as machine learning algorithms and natural language processing, are being employed to analyze complex medical data, predict patient outcomes, and assist in medical imaging analysis. The impact of AI on healthcare is growing rapidly. According to a report by Grand View Research, “the global AI in healthcare market size was estimated at USD 19.27 billion in 2023 and is expected to grow at a CAGR of 38.5% from 2024 to 2030”. The following use cases illustrate the specific ways in which AI is transforming the healthcare sector.
Use Cases
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1. Monitor blood pressure and collect blood samples
AI-assisted robots handle repetitive tasks such as monitoring blood pressure and taking blood samples. This frees up medical staff to address more complex issues that require human judgment and empathy, improving overall patient care.
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2. Therapeutic robots
AI-based therapeutic robots are created to assist special needs patients, including the elderly and those with autism. These robots offer emotional support and help alleviate stress, contributing to better mental health and well-being.
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3. Robot-assisted surgery
Robot-assisted surgery has become a standard practice, allowing doctors to execute complex procedures with enhanced precision, flexibility, and control. This technology leads to better surgical outcomes and faster patient recovery times.
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4. Analyze medical images
AI-enabled software analyzes medical images, including CT scans and x-rays, to identify diseases like skin cancer, tooth decay, and pigmentation. The use of 3D visualization enhances diagnostic accuracy and helps in planning effective treatments.
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5. Process cardiac data to identify irregularities
AI-enabled systems process cardiac data, including ECGs, to identify irregularities like atrial fibrillation. Early detection of such conditions can lead to timely interventions and better patient outcomes.
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6. Portable scanning devices
AI-enabled portable scanning devices allow consumers to self-diagnose medical conditions and take basic vital measurements, such as blood pressure and heart rate. This empowers individuals to monitor their health and seek medical advice when necessary.
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7. Drones equipped with lifesaving technologies
Drones equipped with Computer Vision and lifesaving technologies, including Automated External Defibrillators (AEDs), medication, and CPR, are deployed in emergency situations. These drones can quickly reach and assist individuals in critical conditions, potentially saving lives.
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8. Nanorobots
Nanorobots are engineered to be introduced into the body to perform various tasks, such as monitoring vital signs, carrying oxygen, eliminating infectious agents, and delivering targeted medications. This technology promises to revolutionize medical treatments and patient care.
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9. Healthcare assistants for managing appointments and medications
AI-based healthcare assistants offer guidance to users for managing clinical appointments and medications. They also monitor patients’ health status and alert nurses in case of critical conditions, ensuring timely medical intervention.
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10. Personalized treatment plans
AI systems analyze various data sources, including patient notes, external research, and clinical expertise, to determine the most appropriate, personalized treatment plans for patients. This approach enhances treatment effectiveness and patient outcomes.
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11. Monitor blood-glucose levels and administer insulin
AI-integrated treatment equipment, such as insulin pumps, can autonomously adjust to patients’ conditions by monitoring blood-glucose levels and administering insulin as needed. This ensures precise and timely treatment, improving patient health management.
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12. Voice-to-text transcription of physician-dictated notes
AI-enabled voice-to-text transcription for physician-dictated notes has become widespread. This technology streamlines documentation, allowing doctors to focus more on patient care and less on administrative tasks.
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13. Wearable health trackers
Wearable health trackers monitor various health metrics, including blood sugar, blood pressure, heart rate, and activity levels, and share this data with doctors. AI systems process this information to provide near-real-time analysis and insights, aiding in proactive health management.
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14. Sensors on hospital beds
Hospital beds equipped with sensors send patient data to help nurses monitor statistics such as movement and weight changes, enhancing patient care. By monitoring patient movement and alerting healthcare staff, these beds can prevent falls and potentially save lives.
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15. Analyze healthcare data to identify seasonal diseases
AI systems can identify seasonal diseases by analyzing extensive healthcare data, such as insurance claims, drug prescriptions, and weather forecasts. This capability allows for early detection and timely public health interventions.
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16. Robotic harnesses in patient recovery
AI software integrated with robotic harnesses assists patients in recovering their ability to stand and walk. This technology is particularly beneficial for rehabilitation, helping patients regain mobility and independence.
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17. Identify the best treatment plans
AI systems analyze patient and third-party data to uncover insights and recommend actions. They help identify the best treatment plans tailored to individual patients by considering their medical history and genetic profile, enabling personalized medication or care plans.
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18. Real-time case prioritization and triage
AI-driven prescriptive analytics on patient data allow for accurate real-time case prioritization and triage, optimizing patient flow through automation. By integrating call centers with language processing tools, healthcare providers can prioritize urgent cases and minimize errors, enhancing overall patient care.
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19. Analysis of chronic conditions
AI systems analyze chronic conditions by leveraging lab and medical data to enable early diagnosis. They provide detailed reports on the likelihood of developing certain diseases based on genetic data and integrate appropriate care plans to eliminate or reduce risk factors.
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20. Superior care management
AI systems suggest the best treatment options based on patient complaints and other relevant data. They create control mechanisms to detect and prevent potential diagnosis errors, identify the most effective active compounds for individual patients, and provide accurate statistics for superior care management.
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21. Identify potential risks in pregnancies
AI systems monitor the health of mothers and fetuses to alleviate concerns and enable early diagnosis. Machine Learning is used to quickly identify potential risks and complications, thereby reducing the rates of miscarriage and pregnancy-related diseases, ensuring better maternal and fetal health.
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22. Analyze and transform images to detect potential diseases early
Utilize advanced medical imaging techniques to analyze and transform images, modeling various scenarios. Diagnostic platforms with high image processing capabilities can detect potential diseases early, improving patient outcomes.
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23. Automatically review publicly available data
Optimize hospital and clinic services by using AI to automatically review healthcare insurance plans, drug prices, and other publicly available data. Natural Language Processing (NLP) and Large Language Models (LLMs) can analyze unstructured data, providing valuable insights for decision-making.
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24. Precise targeting in marketing campaigns
Develop a marketing strategy tailored to the brand’s market perception and target segments. Tools with high granularity enable precise targeting, enhancing the effectiveness of marketing campaigns and increasing sales.
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25. Assess the effectiveness of drugs using real patient data
Evaluate the effectiveness of drugs and medical devices by testing on real patient data, rather than relying solely on simulations. Compare the results with benchmark drugs to assess the impact and efficacy of the new drug.
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26. Enhance the patient onboarding process
Enhance the patient onboarding process with a digital workflow that automates task routing, monitors prioritization, and escalates exceptions. This ensures a smooth and efficient onboarding experience for patients.
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27. Gather patient data from other hospitals
Gather comprehensive patient data, such as medical history, demographics, and information from other hospitals. This process includes creating patient accounts, verifying eligibility, processing enrollments, and managing benefits to ensure accurate and efficient patient management.
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28. Process scanned documents or picture
Implement Intelligent Document Processing (IDP) to efficiently process scanned documents or pictures, such as identification documents and insurance policies, into the patient record system. This streamlines data entry and ensures accurate record-keeping.
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29. Secure patient connections and biometric authentications
Ensure secure patient connections and biometric authentications using voice recognition. Support new appointment requests through an online workflow platform, directing them to the appropriate work queue based on attributes like location, diagnosis, and insurance carrier for efficient scheduling.
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30. Create condensed reports with releavnt patient medical history
Prepare for appointments by compiling a condensed report that includes relevant patient medical history retrieved from various systems. This report is then sent to referral management representatives or doctors to ensure they have all necessary information for the appointment.
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31. Schedule patient appointments
Efficiently schedule patient appointments by considering factors such as diagnosis, location, expected examinations or tests, and doctor availability. Automate reminders via email or SMS, update physicians’ calendars in real-time, and manage appointment cancellations as necessary.
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32. Virtual nurses
Offer 24/7 remote medical consultations using virtual nurses (chatbots) to collect patient information, provide initial diagnoses, recommend actions, and suggest appointments with healthcare professionals when needed. This ensures continuous patient support and timely medical advice.
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33. Update patient records
Continuously collect and update comprehensive patient data, such as personal information, diagnoses, examination and test results, treatment cycles, and hospital appointments, from both internal and external sources. This ensures accurate and up-to-date patient records.
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34. Extract semantic meaning from various sources
Extract semantic meaning from various sources, including PDF images of faxed laboratory reports, voice recordings of patient interactions, and free-text EHR inputs. This allows for plain text search queries about patient diagnoses, outcomes, and treatment cycles, facilitating easy access to critical information.
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35. Insights from patient data
Create analytics from collected patient data to offer clinical staff valuable insights. These insights support more accurate diagnostics and enable the development of tailored treatments for patients, improving overall healthcare quality.
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36. Risk scores for patients and providers
Develop risk scores to identify healthcare patients and providers who could benefit from enhanced services or wellness activities. Use wearable devices, such as sensors and smartwatches, to monitor patients in real-time and suggest necessary adjustments to their care.
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37. Monitor patient health and collect information in real-time
Continuously monitor patients’ health in hospital rooms in real-time. Collect visual information, body movement data, and vital signs such as heart rate and blood pressure 24/7 using connected devices, ensuring timely detection of any health issues.
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38. Identify patterns in patients' health
Analyze collected data to detect patterns in each patient’s health. This enables the prediction of serious complications such as heart attacks, strokes, and sepsis, allowing for timely intervention and improved patient outcomes.
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39. Real-time alerts during emergencies
Provide real-time alerts to nurses during emergencies. Customize the frequency of nurse visits based on patient needs and free up physicians’ time by automating data collection and charting, allowing them to focus on patient care.
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40. Track medication use
Track medication use by prescriptions and ensure patients adhere to their medication regimen. Use visual detection through webcam video recordings to verify that patients are taking their medication as prescribed.
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41. Gather real-time verbal observations from physicians and analyze tests
Gather real-time verbal observations and notes dictated by physicians during examinations and automatically update electronic health records (EHR). Compile comprehensive charts and summaries about a patient’s overall health. Rapidly detect the likelihood of diseases through the analysis of tests, X-rays, CT scans, data entry, and other documents. Assist cardiologists and radiologists by identifying subtle changes in imaging scans more quickly, potentially leading to earlier and more accurate diagnoses and the design of customized treatments.
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42. Determine the most individually tailored treatment plans
Review notes and reports from a patient’s file, along with external research and clinical expertise, to determine the most individually tailored treatment path. This ensures that each patient receives the most appropriate and effective care.
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43. Examine available chemical data
Examine available chemical data and compounds to determine the optimal combinations using scientific design principles. Summarize the chemical data to describe preferred conditions, size, functionality, toxicity, bioactivity, and bioavailability, ensuring the most effective and safe use of compounds.
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44. Forecast side effects of drugs
Forecast a drug’s effects and side effects by analyzing and interpreting biomedical data from research experiments. Support the identification of suitable candidates for clinical trials by examining genetic information and related documentation, ensuring the selection of the most appropriate participants.
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45. Facilitate clinical trial approval workflows
Facilitate clinical trial approval workflows by utilizing document platforms. Employ Intelligent Document Processing (IDP) to convert and reconcile documents, streamlining the approval process and ensuring accuracy.
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46. Adapt staffing levels
Adapt staffing levels by identifying factors that cause fluctuations in patient numbers, such as weather conditions or flu epidemics. This ensures that the hospital or clinic is adequately staffed to handle varying patient volumes.
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47. Predict increases in patient volumes
Forecast potential increases in patient volume and use scenario analyses to anticipate the necessary staffing and supplies. Adjust schedules and staff numbers according to patient traffic predictions. Continuously assess and adjust personnel needs, updating staff schedules and sending real-time alerts and reminders to ensure optimal staffing levels.
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48. Calculate bill amounts
Calculate bill amounts by considering costs for medicines, wardroom, food, and doctor fees. Generate the bill and notify patients of the amount via email or SMS. Generate and post claims by retrieving appropriate code eligibility and treatment information. Verify claims, patient prescription pickups, upcoming medical tests, and doctor’s appointments to ensure accurate billing and patient information.
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49. Estimate patient traffic and wait times
Estimate patient traffic and wait times by predicting volume and transfer capabilities. Propose corrective actions to decrease wait times and communicate with other hospitals to manage patient flow. Resolve patient queries regarding appointments, services, transactions, and other details through a self-service digital platform that leverages chatbots for efficient and timely responses.