Aidoc Staff

AI in Healthcare Examples: 10 Clinical Use Cases

When discussing examples of AI in healthcare, our minds may jump to automated call centers and schedule reminders, chatbots and even robotic surgeries. One crucial example of AI in medicine, however, is its application to clinical workflows and how it can improve patient outcomes. In this blog post, we’re going to dive into 10 use case examples in which AI has drastic impacts in real-time environments.

Examples of AI in Different Healthcare Settings and Service Lines

Brain Aneurysms

Use Case: A 49 year old male is referred for a head CTA following a long period of headaches. In this instance, AI can analyze the CTA image, which reveals a saccular aneurysm located at the bifurcation of the cerebral artery. The aneurysm measured 5 mm in diameter, with no signs of rupture. AI can raise an alert in its designated widget to notify the radiologist of a suspected aneurysm. The radiologist can then confirm the BA and add it to the final report. In this instance, AI has ensured that this subtle finding was triaged for the radiologist in real-time, offering specialists in the health system an opportunity to deliver optimal care.

C-Spine Fractures

Use Case: A 42 year old male fell off a ladder, resulting in severe abdominal pain. The ED team found a limited range of motion of the cervical spine, thus adding a CT of the cervical spine to the abdominal examination. The patient arrived at the scanner without a collar. In this instance, AI would flag the subtle cervical spine fracture, which the radiologist can confirm. This is just one benefit of AI in healthcare, as it opens up the care team to initiate a spinal precaution protocol intervention.

Intracranial Hemorrhage

Use Case: A 75 year old female on anticoagulation arrives at the ED after falling in the bathtub a day earlier. AI would flag a small right parietal subdural hematoma and prioritize the case in the widget and radiology worklist. This example of AI in medicine gives clinicians a leg up on expediting care.

Vessel Occlusions (LVO, MeVO)

Use Case: A 60 year old male with hypertension and smoking history presents to the ED with sudden right-sided weakness and speech difficulty. The ED noted right hemiparesis and facial droop with an NIHSS of 12, suggesting a moderate to severe stroke. A NCCT and CTA were ordered and performed. A huge benefit for AI in healthcare here is its ability to analyze that the NCCT was negative for a hemorrhage, which is confirmed by the radiologist. On the CTA, the algorithm flags a suspected left middle cerebral artery occlusion at the M2 segment, alerting the radiologist. The radiologist then issues a stroke alert through the AI platform. The neurologist and neuro interventional physician both receive notifications. The neurologist then orders an intravenous tPA while the neuro interventional physician reviews the patient images and EHR data, determining the patient should proceed to a mechanical thrombectomy.

Examples of AI for Venous Thromboembolism

Pulmonary Embolism

Use Case:  A 49 year old female, otherwise healthy, arrived after a long flight with atypical chest pain and shortness of breath. In this case, AI can flag a subtle subsegmental PE that drastically reduced waiting and the reading times for the study, also opening opportunities to coordinate care with the Pulmonary Embolism Response Team.

Incidental Pulmonary Embolism

Use Case: A 36 year old male, scanned during a routing restaging following chemotherapy. An incidental pulmonary embolism algorithm can flag a subtle, but clinically significant, pulmonary embolus in the right middle lobe, leading to a significantly reduced turnaround time in diagnosis and in the notification of the downstream care team.

Examples of AI for Aortic Conditions

Aortic Dissection

Use Case: A 53 year old male with hypertension presents to the ER with moderate chest pain. The patient has normal EKG and is waiting for troponin lab results. In the meantime, the patient goes to CT. A benefit of AI in treatment aortic conditions is that it can flag a suspected dissection and prioritize the scan to the radiologist. THe radiologist then confirms the dissection in their PACS and forwards the findings to the intervention team through a mobile app. By the time the patient is returned to the ER, clinicians and radiologists are aware of the dissection and call it out to the surgical staff.

Abdominal Aortic Measurement

Use Case: A 65 year old male, ex-smoker with hypertension and hyperlipidemia, experienced intermittent abdominal pain without gastrointestinal symptoms. An examination found a stable blood pressure and a pulsatile abdominal mass, suggestive of an abdominal aortic aneurysm (AAA). A contrast CT was run, and AI can mark the study, identifying that the measurements (5.2 x 4.4 cm) are above the site configured 3cm threshold. The radiologist then adds the details of a suspected AAA that appears not to have ruptured or have a dissection, but did not mention it to the vascular care team. The AI can then pick up the mention of the AAA in the report and notify the vascular care team to further evaluate and decide if an intervention is required.

Example of AI for Cardiology

Coronary Artery Calcification (CAC)

Use Case: A 45 year old man undergoes a non-contrast CT after a car crash. Though it’s not what the radiologists and ED physicians are looking for, AI could potentially flag the possibility of a high level of coronary artery calcification, an important indicator of cardiovascular health. The radiologist reviews the CAC series in PACS, adds the CAC details to the radiologist report and the patient is referred to a cardiologist for further management.

Example of AI for Bone Fracture

Rib Fractures

Use Case: A 29 year old female, post motor vehicle accident, is admitted with multiple injuries. She is hemodynamically stable with pain localized to the left upper quadrant, working diagnosis was a splenic trauma. Fortunately, AI flagged suspected rib fractures, helping reduce the radiologist turnaround time and prioritizing the patient for additional evaluation with the orthopedic care team.

AI in Healthcare Examples and Continued Growth

The impact of AI in the healthcare domain, in many ways, is only seeing its initial impact. With an ever growing pool of evidence suggesting the clinical efficacy of AI, use cases like the ones outlined above are bound to multiply. AI powered healthcare is an undeniable future.

The above use cases are just some examples of AI in healthcare, with plenty more clinical use cases and benefits yet to be seen. Learn more about enterprise-wide AI and how it effectively helps health systems overcome some of the challenges of AI adoption.

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Aidoc Staff