{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T02:51:58Z","timestamp":1781578318410,"version":"3.54.5"},"reference-count":0,"publisher":"University of the Philippines Manila","issue":"11","license":[{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Acta Med Philipp"],"abstract":"<jats:p>Health technology is advancing at breakneck speeds, powered by new discoveries that have disrupted medical practice\u00a0as we know it. Research and development are accelerated, not only intrinsically, but also by external events that provided the\u00a0impetus for change. Last century\u2019s push came from space flight and computers, this millennium\u2019s is triggered by catastrophes\u00a0such as the pandemic. Adoption of such technology is swift, and some say too rapid for comfort.\nThis is seen in areas such as AI and machine learning, robotic surgery and telemedicine, drug discovery and development.\u00a0It is feared that the speed of integration of such technology in healthcare systems appear to overtake our own ability to\u00a0comprehend the science or the implications of such.\nIn the meantime, we have to endeavor to analyze the literature that is currently flooding the publications. Such a\u00a0phenomenon was documented by a bibliometric review by Dalky which discovered that artificial intelligence (AI) and machine\u00a0learning (ML) articles rose dramatically from 694 in 2000 and reached 86,284 in 2021.\u00a0At the Acta Medica Philippina,\u00a0an article on deep learning was published in 2024 on the use of convolutional neural networks in the classification of single\u00a0photon emission computed tomography-myocardial perfusion imaging (SPECT-MPI) results.\u00a0A similar article appeared in\u00a0this year\u2019s issue utilizing back propagation neural network (BPNN) and a recurrent neural network (RNN) in the analysis of\u00a0rural health unit accessibility.\u00a0In this issue, Acilador and Opina describe the construction of an algorithm to predict blood\u00a0donor retention via a machine learning approach.\u00a0For these three papers, the utility of their findings needs to be analyzed\u00a0using a specific framework.\nWe are fortunate that Consolidated Standards of Reporting Trials (CONSORT) came up with a CONSORT\u2013Artificial\u00a0Intelligence (AI) extension\u2014a new reporting guideline for clinical trials evaluating interventions with an AI component.\u00a0While the CONSORT AI was designed to \u201censure transparency, minimize potential biases, and promote the trustworthiness\u00a0of the results,\u201d the subsequent challenge is to determine \u201cthe extent to which (the results) may be generalizable.\u201d The first\u00a0two roles can be performed by a trained peer reviewer, but the latter may require the overview and maturity not only of a\u00a0journal editor but also of a critical reader as well.\nSpeaking of the critical reader, the integration of \u201csolutions\u201d provided by AI is still in its nascent phase in health care.\u00a0While uptake is spreading especially among younger health professionals, illustrated in this issue by a cross-sectional study by\u00a0Lacanlale and co-authors on the use of ChatGPT among student nurses in Baguio City, Philippines as an example of early\u00a0potential adoption, there are obvious barriers to its utilization.\u00a0Among such are algorithmic opacity, insufficient training,\u00a0and ethical challenges. These were the issues discovered in a recent paper by Tun.\u00a0Artificial intelligence should improve in\u00a0explainability and reliability to improve trust and increase assimilation in health care systems.\nDespite such concerns, we continue to innovate and harness new technology for our day-to-day work. We thus look\u00a0forward to the day when a case report in this issue by Jacinto and co-authors on the application of a 3D printed patient-specific\u00a0zygomatic implant for a post-traumatic facial deformity eventually becomes a report on mainstream experience with such\u00a0technology.\nOur publication of these papers is proof that local researchers are in stride with the ongoing revolution in health care\u00a0technology.\n\u00a0<\/jats:p>","DOI":"10.47895\/amp.v60i11.14392","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T01:55:51Z","timestamp":1781574951000},"source":"Crossref","is-referenced-by-count":0,"title":["Critical Adoption of Disruptive Health Technology"],"prefix":"10.47895","volume":"60","author":[{"given":"Angela G.","family":"Sison-Aguilar MD MSc MBA","sequence":"first","affiliation":[{"name":"Department of Obstetrics and Gynecology\u2028College of Medicine and Philippine General Hospital\u2028University of the Philippines Manila"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"27557","published-online":{"date-parts":[[2026,6,15]]},"container-title":["Acta Medica Philippina"],"original-title":[],"link":[{"URL":"https:\/\/actamedicaphilippina.upm.edu.ph\/index.php\/acta\/article\/download\/14392\/7166","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/actamedicaphilippina.upm.edu.ph\/index.php\/acta\/article\/download\/14392\/7166","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T01:55:52Z","timestamp":1781574952000},"score":1,"resource":{"primary":{"URL":"https:\/\/actamedicaphilippina.upm.edu.ph\/index.php\/acta\/article\/view\/14392"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,15]]},"references-count":0,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2026,6,15]]}},"URL":"https:\/\/doi.org\/10.47895\/amp.v60i11.14392","relation":{},"ISSN":["2094-9278","0001-6071"],"issn-type":[{"value":"2094-9278","type":"electronic"},{"value":"0001-6071","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6,15]]}}}