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Homework 6

Student Name: Fauzan Isnaini

How AI Helps Diagnosis and Decision Making in Health Care Facilities

Radiology Assistant

Radiology is a branch of medicine that uses imaging technology to diagnose and treat disease. Diagnostic radiology helps health care providers see structures inside your body. Using the diagnostic images, the radiologist or other physicians can often [3]:

  1. Diagnose the cause of your symptoms
  2. Monitor how well your body is responding to a treatment you are receiving for your disease or condition
  3. Screen for different illnesses, such as breast cancer, colon cancer, or heart disease Within radiology, trained physicians visually assess medical images and report findings to detect, characterize and monitor diseases. Such assessment is often based on education and experience and can be, at times, subjective. In contrast to such qualitative reasoning, AI excels at recognizing complex patterns in imaging data and can provide a quantitative assessment in an automated fashion. More accurate and reproducible radiology assessments can then be made when AI is integrated into the clinical workflow as a tool to assist physicians.[1] Some examples of AI’s clinical application in radiology are [1]:
  4. Thoracic imaging AI can help in identifying pulmonary nodules, which can be applied in early detection of lung cancer
  5. Abdominal and pelvic imaging AI can help in detecting lesions in abdominal and pelvic. For example, AI can analyze data from computed topography (CT) and magnetic resonance imaging (MRI) to detect liver lesions, and characterize these lesions as benign or malignant. Furthermore, AI can also help in suggesting the follow-up actions for the patient.
  6. Colonoscopy Colonic polyps that are undetected or misclassified pose a potential risk of colorectal cancer. AI can help in making an early detection and consistent monitoring of this risk.
  7. Mammography Analyzing mammography is technically challenging, even for a trained expert. AI can assist in interpreting the image. For example, AI can identify and characterize microcalcifications. Microcalcifications are tiny deposits of calcium salts that are too small to be felt but can be detected by imaging, and can be an early sign of breast cancer. They can be scattered throughout the mammary gland, or occur in clusters. [4]
  8. Brain imaging AI can help in making diagnostic prediction of brain tumors, which are characterized by abnormal growth of brain tissue.
  9. Radiation oncology Radiation treatment planning can be automated by segmenting tumours for radiation dose optimization. Furthermore, assessing response to treatment by monitoring over time is essential for evaluating the success of radiation therapy efforts. AI is able to perform these assessments, thereby improving accuracy and speed.

AI in Clinical Decision Support

Other than analyzing radiology images, AI can also digest data from blood tests, electrocardiogram (EKG), genomics, and patient medical history do give a better treatment to the patient. AI-enabled clinical decision support includes diagnosis and prognosis, and involves classification or regression algorithms that can predict the probability of a medical outcome or the risk for a certain disease.[5] Here are some examples of how AI helps clinical decision [6]:

  1. Accumulation of medical histories from birth alongside linked maternal electronic health record (HER) information in a healthcare facility, enabled the prediction of high obesity risk children as early as two years after birth, possibly allowing life-altering preventative interventions.
  2. The Advanced Alert Monitoring system developed and deployed by Kaiser Permanente uses Intensive Care Unit (ICU) data to predict fatally deteriorating cases and alert staff to the need of life-saving interventions.

References