Artificial intelligence and the scientific method: How to cope with a complete oxymoron.

Journal: Clinics in dermatology
PMID: 38216002

Abstract

Artificial intelligence (AI) can be a powerful tool for data analysis, but it can also mislead investigators, due in part to a fundamental difference between classic data analysis and data analysis using AI. A more or less limited data set is analyzed in classic data analysis, and a hypothesis is generated. That hypothesis is then tested using a separate data set, and the data are examined again. The premise is either accepted or rejected with a value p, indicating that any difference observed is due merely to chance. By contrast, a new hypothesis is generated in AI as each datum is added to the data set. We explore this discrepancy and suggest means to overcome it.

Authors

  • W Clark Lambert
    Departments of Dermatology and of Pathology, Immunology and Laboratory Medicine, Rutgers-New Jersey Medical School, Newark, New Jersey, USA. Electronic address: wclambert3129@gmail.com.
  • Muriel W Lambert
    Departments of Dermatology and of Pathology, Immunology and Laboratory Medicine, Rutgers-New Jersey Medical School, Newark, New Jersey, USA.
  • Mohammad Hassan Emamian
    Ophthalmic Epidemiology Research Center, Shahroud University of Medical Sciences, Shahroud, Iran.
  • Michał Woźniak
    Department of Systems and Computer Networks, Faculty of ICT, Wroclaw University of Science and Technology, Wroclaw, Poland.
  • Andrzej Grzybowski
    Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland.

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