International Journal of Healthcare and Hospital Management Studies
The Super Intelligence Paradigm in Clinical Diagnosis: Methodological and Ethical Challenges
By Parhlad Singh Ahluwalia.
Abstract
Background
Claims that artificial intelligence (AI) is approaching, or has already reached, "superhuman" diagnostic competence now shape investment, regulation and clinical expectation. This paper tests those claims against the available evidence.
Methods
I conducted a PRISMA-informed analytical review. The first stream screened a supplied corpus of 117 bibliographic records for thematic relevance. The second stream was a purposive set of landmark diagnostic-accuracy studies, systematic reviews, regulatory analyses and ethics frameworks. Evidence was read against the PROBAST and QUADAS-2 risk-of-bias logic and the TRIPOD+AI and CONSORT-AI reporting standards.
Results
Of the 117 records, 29 met the thematic criteria for AI, diagnosis, health equity, governance, organisational readiness or research integrity. Benchmark results for narrow diagnostic systems are impressive. They are, however, concentrated in retrospective, single-task, imaging-dominated designs, and pooled comparisons with clinicians show parity far more often than superiority. Prospective and randomised evidence is scarce. Documented failure modes include poor external generalisation, shortcut learning, proxy-label bias, underdiagnosis in under-served groups and automation bias. No existing system meets the formal definition of superintelligence.