What do these AI tools aim to do?
In today’s hospitals and diagnostic centres, AI tools remain a far cry from replacing physicians. But they are actively shaping how clinical judgments are made, prioritized, and later defended. That’s the unsettled legal ground of modern medicine that must be confronted, Marin notes.
Radiology platforms like Aidoc and Zebra Medical Vision interpret CTs and X-rays to flag urgent findings. Aidoc’s algorithm, which is FDA-cleared and used in hospitals across the globe, identifies acute abnormalities such as pulmonary embolism, intracranial hemorrhage, stroke, and cervical spine fractures. Processing in real time, it enables faster intervention and helps healthcare practitioners prioritize cases. Its platform also integrates into radiology and emergency workflows, helping clinicians make faster, more accurate decisions especially in trauma and stroke care.
Zebra focuses on detecting a range of diseases — surfacing findings of coronary artery calcifications, osteoporosis, emphysema, and fatty liver disease — as well as acute issues like aortic aneurysms. The company’s goal is to aid in large-scale screening and proactive care by embedding its AI into radiology workflows.
US-based PathAI assists pathologists in identifying malignancies. It partners with biopharma companies, diagnostic labs, and academic institutions to improve diagnostic precision, support drug development, and reduce diagnostic error. In ophthalmology, DeepMind’s AI, developed with Moorfields Eye Hospital in the UK, focuses on diagnosing and prioritizing eye diseases using deep learning algorithms trained on tens of thousands of retinal OCT (optical coherence tomography) scans. Able to detect over 50 conditions, it also recommends the urgency of referral, helping clinicians triage patients more effectively. It demonstrates performance on par with expert clinicians in detecting eye disease and makes a marked difference in settings with high demand and limited specialist resources.
Meanwhile, predictive analytics tools are experiencing widespread adoption across hospital networks. Epic Systems, leveraging Microsoft Azure, can forecast patient deterioration — such as cardiac arrest — or readmission based on electronic health record (EHR) data. The cloud-based tools’ ability to identify at-risk patients earlier translates to faster, data-driven care decisions. Johns Hopkins’ Sepsis Watch uses machine learning to alert clinicians in real time when patients are trending toward sepsis, based on continuous analysis of vital signs and clinical data — often before symptoms become obvious. It too supports early intervention.