{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T10:36:53Z","timestamp":1781606213373,"version":"3.54.5"},"reference-count":19,"publisher":"Georg Thieme Verlag KG","issue":"05","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Appl Clin Inform"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This study aimed to evaluate and compare the diagnostic responses generated by two artificial intelligence (AI) models developed 54 years apart, and encourage physicians to explore the use of large language models (LLMs) like GPT-4o in clinical practice.<\/jats:p>\n                  <jats:p>A clinical case of metabolic acidosis was presented to GPT-4o, and the model's diagnostic reasoning, data interpretation, and management recommendations were recorded. These outputs were then compared with the responses from Schwartz's 1970 AI model built with a decision-tree algorithm using Conversational Algebraic Language (CAL). Both models were given the same patient data to ensure a fair comparison.<\/jats:p>\n                  <jats:p>GPT-4o generated an advanced analysis of the patient's acid\u2013base disturbance, correctly identifying likely causes and suggesting relevant diagnostic tests and treatments. It provided a detailed, narrative explanation of the metabolic acidosis. The 1970 CAL model, while correctly recognizing the metabolic acidosis and flagging implausible inputs, was constrained by its rule-based design. CAL offered only basic stepwise guidance and required sequential prompts for each data point, reflecting a limited capacity to handle complex or unanticipated information. GPT-4o, by contrast, integrated the data more holistically, although it occasionally ventured beyond the provided information.<\/jats:p>\n                  <jats:p>This comparison illustrates substantial advances in AI capabilities over five decades. GPT-4o's performance demonstrates the transformative potential of modern LLMs in clinical decision-making, showcasing abilities to synthesize complex data and assist diagnosis without specialized training, yet necessitating further validation, rigorous clinical trials, and adaptation to clinical contexts. Although innovative for its era and offering certain advantages over GPT-4o, the rule-based CAL system had technical limitations. Rather than viewing one as simply \u201cbetter,\u201d this study provides perspective on how far AI in medicine has progressed while acknowledging that current AI tools remain supplements to\u2014not replacements for\u2014physician judgment.<\/jats:p>","DOI":"10.1055\/a-2628-8408","type":"journal-article","created":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T19:16:43Z","timestamp":1749237403000},"page":"1627-1636","source":"Crossref","is-referenced-by-count":4,"title":["Comparing the Performances of a 54-Year-Old Computer-Based Consultation to ChatGPT-4o"],"prefix":"10.1055","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0056-3087","authenticated-orcid":false,"given":"Elvan Burak","family":"Verdi","sequence":"additional","affiliation":[{"name":"Department of Chest Diseases, Siirt Training and Research Hospital, Siirt, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oguz","family":"Akbilgic","sequence":"additional","affiliation":[{"name":"Section of Cardiovascular Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"194","published-online":{"date-parts":[[2025,11,7]]},"reference":[{"issue":"06","key":"ref1","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1016\/j.cardfail.2019.04.006","article-title":"The promise of machine learning: When will it be delivered?","volume":"25","author":"O Akbilgic","year":"2019","journal-title":"J Card Fail"},{"issue":"01","key":"ref2","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1007\/s10916-023-01974-9","article-title":"Can artificial intelligence ever develop the human touch and replace a psychiatrist? - A letter to the editor of the Journal of Medical Systems: Regarding \u201cArtificial Intelligence in Medicine & ChatGPT: De-Tether the Physician.\u201d","volume":"47","author":"S Das","year":"2023","journal-title":"J Med Syst"},{"issue":"236","key":"ref3","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1093\/mind\/LIX.236.433","article-title":"Computing machinery and intelligence","volume":"59","author":"A M Turing","year":"1950","journal-title":"Mind"},{"issue":"04","key":"ref4","first-page":"12","article-title":"A proposal for the Dartmouth Summer Research Project on artificial intelligence","volume":"27","author":"J McCarthy","year":"1955","journal-title":"AI Mag"},{"issue":"3366","key":"ref5","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1126\/science.130.3366.9","article-title":"Reasoning foundations of medical diagnosis; symbolic logic, probability, and value theory aid our understanding of how physicians reason","volume":"130","author":"R S Ledley","year":"1959","journal-title":"Science"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1001\/jama.1961.03040290005002","article-title":"A mathematical approach to medical diagnosis. 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