Artificial intelligence (AI) in healthcare has evolved beyond merely automating call centers and scheduling reminders. It has begun to revolutionize clinical workflows, significantly improving patient outcomes in real-time environments. This write-up explores ten real-life examples of AI’s profound impact across several healthcare settings and service lines.
The first example provides insights into how AI can analyze Computed Tomography Angiography (CTA) images to detect brain aneurysms. For instance, AI was pivotal in identifying a saccular aneurysm in a 49-year-old male patient, thereby enabling the medical personnel to provide optimal care timely.
Next, we discuss AI’s role in detecting C-spine fractures. A 42-year-old male patient fell off a ladder and the AI system helped flag a subtle cervical spine fracture, triggering the initiation of a spinal precaution protocol intervention.
The third case examines AI’s utility in identifying intracranial hemorrhages. In a 75-year-old female patient on anticoagulation, AI’s involvement helped expedite the care process by noting a small right parietal subdural hematoma.
The fourth use case investigated AI’s capabilities in unveiling and managing vessel occlusions. AI analyzed the scans of a 60-year-old male stroke patient and flagged a potential occlusion in the left cerebral artery. This allowed clinicians to order the necessary treatment promptly.
For venous thromboembolism, AI speeds up the detection and treatment process. A subtle subsegmental pulmonary embolism was quickly identified in a 49-year-old female patient, leading to faster care coordination. Another example, an incidental pulmonary embolism in a 36-year-old male, resulted in a significantly reduced diagnosis turnaround time.
Moving to aortic conditions, AI’s prowess in identifying an aortic dissection enhanced patient care for a 53-year-old male. Furthermore, AI flagged measurements suggestive of an abdominal aortic aneurysm (AAA) in a 65-year-old male patient, facilitating the necessary medical intervention.
In cardiology, AI has been instrumental in detecting coronary artery calcification — a crucial cardiovascular health indicator. For instance, in a 45-year-old man undergoing a non-contrast CT, AI flagged high coronary artery calcification levels, facilitating referral to a cardiologist.
Lastly, in orthopedics, AI has been valuable in detecting bone fractures. A suspected rib fracture in a 29-year-old female post-road accident was detected by AI system which assisted in timelier treatment.
These instances only represent the start of AI’s impact on healthcare, and as evidence supporting its clinical efficacy continues to grow, we can expect AI’s application in healthcare to multiply. AI adoption addresses a variety of challenges faced by health systems, offering countless benefits yet to be explored in this sector. AI’s impact guarantees a healthcare future where enterprises can deliver patient care more efficiently.
