Raymond L. Rosales
Nov 2023 DOI 10.35460/2546-1621.2023-0084
In just about every international medical conference I have been to in the last three years, artificial intelligence (AI) is a trending topic. Leading companies, including healthcare are recognizing the transformative power of AI and are already experimenting on AI tools.
AI as a topic is on its way to ubiquity and before we get overwhelmed by its many capabilities, it is time to engage and learn more, particularly in the healthcare space. We are mostly aware of devices and robotics enhanced by AI in surgery (example, Da Vinci robotics), in smart physical aids for the disabled and elderly. But what about AI that is in virtual reality and dealing with complex data?
Deployment of AI is recognized as beneficial in disease diagnosis, health monitoring and digital patient consultations, clinical training, patient data management, drug development and personalized medicine. Just think of the daily enormity of data gathered by hospitals or clinics from electronic health records, images, biomarkers and genetics, among others. Categorizing and analyzing them for diagnosis or research would be a laborious process without AI tools that could complete the work in minutes. In multidisciplinary field applications, AI natural and large language models could corroborate various medical domains that facilitate consensus and forward management.
In diagnosing patients for example, clinicians may use AI to help identify pulmonary tuberculosis based on chest x-rays. Patients can be monitored through smart wearable devices where data is continually reviewed by AI and can flag any possible adverse event. In clinical sites where clinical trials are conducted, patient recruitment could be more efficient with AI tools to match patients with eligibility requirements based on demographics, disease exposure, genetics and geographic locations, among others. There are more examples but at this exploratory point, we can already acknowledge the advantages of using AI tools.
What about the risks of AI application? Security and privacy of patient records are of paramount concern. Use of anonymized data should be the norm. Advanced technologies to protect personal identifying data are also being developed precisely to mitigate this security risk.
JMUST looks forward to article submission that touches on AI, and we have one in this present issue. It’s time we engage and understand how we can use AI tools for better patient outcomes at the same time being mindful of the risks.
As AI is by far hinged on software, there are also works which are hinged on artistic skills. In this present JMUST cover page, we portray the Thomasian Medicine Class 1981 ( Magilas Class 1981 ) Art group that showcased the doctor artists’ model pieces at the UST Museum on the occasion of the 2022 sesquicentennial FMS anniversary celebration, and recently, at the Philippine Consulate of San Francisco, USA.
Meanwhile, we are on our 13th JMUST issue covering 12 articles, as follows: 2-descriptive research, 3-systematic reviews, 2-case reports, 3-review articles and 2-viewpoint articles.
We will never tire thanking our exemplary editorial team who diligently reviewed all submissions. We are so very grateful!
The state of AI in 2023: Generative AI’s breakout year. August 1, 2023 | Survey. Available from: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year .
Parums DV. Editorial: Artificial Intelligence (AI) in clinical medicine and the 2020 CONSORT-AI study guidelines. Med Sci Monit . 2021 Jun 28;27:e933675
Zhang L, Tashiro S, Mukaino M, Yamada S: Use of artificial intelligence large language models as a clinical tool in rehabilitation medicine: A comparative test case. JRM 2023 Oct;55; DOI: https://doi.org/10.2340/jrm.v55.13373
Lakhani P, Sundaram B. Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology . 2017; 284:574–82.
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