Can AI Diagnose Better Than Humans?

How Diagnostic AI Got Here

AI in medical diagnostics began in the 1950s with foundational AI concepts which transitioned into practical applications by the 1970s. When INTERNIST-1, the first AI program for doctors, debuted in 1971, it could only rank likely diagnoses from a limited rule set. Five years later MYCIN moved the field forward by recommending antibiotics for blood infections, hinting at AI’s potential to save lives.

The 1980s and 1990s refined those early ideas as DXplain (1986) broadened differential lists for general practitioners, whereas CorSage (1989) calculated individual coronary-artery-disease risk—an early taste of personalized medicine.

In the 2000s, deep-learning models began reading images faster and, in some tasks, more accurately than human specialists. By 2017 Arterys earned the first FDA clearance for cloud-based AI cardiac MRI analysis, cutting reporting time from 30-60 minutes to less than a minute.

Fast-forward to 2025: the FDA green-lit 1247 AI-enabled medical devices in total (149 this year).

Tradition vs. Modernity

How useful AI for diagnostics actually is? In the npj Digial Medicine article "Transforming diagnosis through artificial intelligence" L. D’Adderio and D. W. Bates presented findings from a detailed five-year study examining AI adoption for stroke care at three major UK stroke centers. They highlight an AI-caused shift in diagnostic processes, particularly during the critical 'door-to-treatment' phase, which runs from the first scan to the final treatment decision.

Traditionally, diagnosis relies on iterative human assessment, hypothesis generation, and refinement to determine a definitive diagnostic label. However, AI can now provide clinicians with an immediate initial diagnostic label often even before a thorough clinical assessment.

Earlier examples date back to 2017, when IBM’s Watson took 10 minutes to review and provide treatment recommendations based on genetic data of tumor cells. The respective work on the same task done by human experts lasted 160 hours.

From paper: “Transforming diagnosis through artificial intelligence” by Luciana D’Adderio, David W. Bates; license: CC-BY-4.0

D’Adderio and Bates note a key limitation: AI primarily offers binary predictions about diagnoses rather than guiding clinicians through diagnostic reasoning.

Nevertheless, the study observed that clinicians are adapting to AI by transforming their traditional workflow rather than replacing it. Instead of just accepting AI's output, clinicians use AI's suggestions as a starting point, subsequently validating these predictions through multiple verification steps, including patient records, established medical standards, and expert consultations.

Authors further posit that AI’s immediate provision of diagnostic labels accelerates decision-making, changing the clinicians' role from initial diagnosis to verifying AI-generated predictions. AI quickly flags critical conditions like vessel occlusions and brain damage extent through advanced imaging and visual tools, initiating urgent treatment pathways such as thrombectomy referrals.

AI's role in early diagnostic decision-making subtly pushes the balance between human and machine agency toward greater machine influence, changing the diagnostic journey and clinical workflows in a fundamental way (.