Browsing by Subject "Artificial intelligence (AI)"

Now showing 1 - 9 of 9
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    Artificial Intelligence in Health, Health Care, and Biomedical Science: An AI Code of Conduct Principles and Commitments Discussion Draft
    (National Academy of Sciences, 2024-04-08) Adams, Laura; Fontaine, Elaine; Lin, Steven; Crowell, Trevor; Chung, Vincent C. H.; Gonzalez, Andrew A.; Surgery, School of Medicine
    This commentary presents initial concepts and content that the Steering Committee feel may be important to a draft Code of Conduct framework for use in the development and application of artificial intelligence (AI) in health, health care, and biomedical science.
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    Artificial Intelligence–Generated Research in the Literature: Is It Real or Is It Fraud?
    (Mary Ann Liebert, 2023) Stone, Jennifer A. M.; Anesthesia, School of Medicine
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    Computer-aided detection for colorectal neoplasia in randomized and non-randomized studies
    (Thieme, 2024-04-23) Mori, Yuichi; Patel, Harsh K.; Repici, Alessandro; Rex, Douglas K.; Sharma, Prateek; Hassan, Cesare; Medicine, School of Medicine
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    Federated learning as a catalyst for digital healthcare innovations
    (Elsevier, 2024-07-12) Yang, Guang; Edwards, Brandon; Bakas, Spyridon; Dou, Qi; Xu, Daguang; Li, Xiaoxiao; Wang, Wanying; Pathology and Laboratory Medicine, School of Medicine
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    From marginal gains to clinical utility: machine learning-based percutaneous coronary intervention risk prediction models
    (Oxford University Press, 2025-01-16) Qadir, Muhammad Ibtsaam; Hira, Ravi S.; Kolbinger, Fiona R.; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
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    Harnessing Microbiome, Bacterial Extracellular Vesicle, and Artificial Intelligence for Polycystic Ovary Syndrome Diagnosis and Management
    (MDPI, 2025-06-07) Kushawaha, Bhawna; Rem, Tial T.; Pelosi, Emanuele; Biochemistry and Molecular Biology, School of Medicine
    Polycystic ovary syndrome (PCOS) affects 6-19% of reproductive-age women worldwide, yet diagnosis remains challenging due to heterogeneous presentations and symptoms overlapping with other endocrine disorders. Recent studies have shown that gut dysbiosis plays a significant role in PCOS pathophysiology, with bacterial extracellular vesicles (BEVs) functioning as critical mediators of the gut-ovary axis. BEVs carry distinct cargos in PCOS patients-including specific miRNAs and inflammatory proteins-and show promise for both diagnostic and therapeutic applications. Artificial intelligence (AI) is emerging as a promising significant tool in PCOS research due to improved diagnostic accuracy and the capability to analyze complex datasets combining microbiome, BEV, and clinical parameters. These integrated approaches have the potential to better address PCOS multifactorial nature, enabling improved phenotypic classification and personalized treatment strategies. This review examines recent advances in the last 25 years in microbiome, BEV, and AI applications in PCOS research using PubMed, Web of Science, and Scopus databases. We explore the diagnostic potential of the AI-driven analysis of microbiome and BEV profiles, and address ethical considerations including data privacy and algorithmic bias. As these technologies continue to evolve, they hold increasing potential for the improvement of PCOS diagnosis and management, including the development of safer, more precise, and effective interventions.
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    Machine Learning and Artificial Intelligence in Surgical Fields
    (Springer, 2020-12) Egert, Melissa; Steward, James E.; Sundaram, Chandru P.; Urology, School of Medicine
    Artificial intelligence (AI) and machine learning (ML) have the potential to improve multiple facets of medical practice, including diagnosis of disease, surgical training, clinical outcomes, and access to healthcare. There have been various applications of this technology to surgical fields. AI and ML have been used to evaluate a surgeon's technical skill. These technologies can detect instrument motion, recognize patterns in video recordings, and track the physical motion, eye movements, and cognitive function of the surgeon. These modalities also aid in the advancement of robotic surgical training. The da Vinci Standard Surgical System developed a recording and playback system to help trainees receive tactical feedback to acquire more precision when operating. ML has shown promise in recognizing and classifying complex patterns on diagnostic images and within pathologic tissue analysis. This allows for more accurate and efficient diagnosis and treatment. Artificial neural networks are able to analyze sets of symptoms in conjunction with labs, imaging, and exam findings to determine the likelihood of a diagnosis or outcome. Telemedicine is another use of ML and AI that uses technology such as voice recognition to deliver health care remotely. Limitations include the need for large data sets to program computers to create the algorithms. There is also the potential for misclassification of data points that do not follow the typical patterns learned by the machine. As more applications of AI and ML are developed for the surgical field, further studies are needed to determine feasibility, efficacy, and cost.
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    Opportunities to encourage adoption of a biomarker-enabled care pathway for Alzheimer's in primary care
    (Wiley, 2025-03-11) Borson, Soo; Au, Rhoda; Chodos, Anna H.; Gandy, Sam; Jain, Holly; Alagor, Amy; Cohn, Kristi; Kerwin, Diana R.; Mintzer, Jacobo; Monroe, Stephanie; Robinson, Delecia; Mielke, Michelle M.; Wilcock, Donna M.; Neurology, School of Medicine
    Identification of early-stage Alzheimer's disease (AD) remains a challenge due to limited specialist availability, diagnostic access, disease awareness, and cultural factors. Blood-based biomarkers (BBBM) could play a critical role in the identification and referral of patients suspected of AD to specialty care. A multidisciplinary AD Biomarker Task Force was convened to evaluate current biomarker use cases, define an optimal biomarker-enabled AD diagnostic care pathway, and understand factors impacting adoption. The Task Force identified opportunities to support biomarker-enabled AD diagnostic care pathway adoption, including streamlining risk assessment and screening by leveraging digital tools, activating primary care providers through education, generating data to expand applicability to diverse populations, and advocating for aligned policies and quality measures. Adoption of BBBMs in the primary care setting will be critical to improve early AD detection. However, challenges to pathway adoption persist and will require action from clinicians, payers, policy makers, and patients to address. Highlights: Blood-based biomarkers can streamline the identification of AD in primary care. Future biomarker-enabled diagnostic care pathways will leverage digital assessments. Education, data generation, and policy advocacy are vital to encourage BBBM use. Implementation of AD care pathways requires the activation of diverse stakeholders.
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    The Use of Artificial Intelligence in Writing Scientific Review Articles
    (Springer, 2024) Kacena, Melissa A.; Plotkin, Lilian I.; Fehrenbacher, Jill C.; Orthopaedic Surgery, School of Medicine
    Purpose of review: With the recent explosion in the use of artificial intelligence (AI) and specifically ChatGPT, we sought to determine whether ChatGPT could be used to assist in writing credible, peer-reviewed, scientific review articles. We also sought to assess, in a scientific study, the advantages and limitations of using ChatGPT for this purpose. To accomplish this, 3 topics of importance in musculoskeletal research were selected: (1) the intersection of Alzheimer's disease and bone; (2) the neural regulation of fracture healing; and (3) COVID-19 and musculoskeletal health. For each of these topics, 3 approaches to write manuscript drafts were undertaken: (1) human only; (2) ChatGPT only (AI-only); and (3) combination approach of #1 and #2 (AI-assisted). Articles were extensively fact checked and edited to ensure scientific quality, resulting in final manuscripts that were significantly different from the original drafts. Numerous parameters were measured throughout the process to quantitate advantages and disadvantages of approaches. Recent findings: Overall, use of AI decreased the time spent to write the review article, but required more extensive fact checking. With the AI-only approach, up to 70% of the references cited were found to be inaccurate. Interestingly, the AI-assisted approach resulted in the highest similarity indices suggesting a higher likelihood of plagiarism. Finally, although the technology is rapidly changing, at the time of study, ChatGPT 4.0 had a cutoff date of September 2021 rendering identification of recent articles impossible. Therefore, all literature published past the cutoff date was manually provided to ChatGPT, rendering approaches #2 and #3 identical for contemporary citations. As a result, for the COVID-19 and musculoskeletal health topic, approach #2 was abandoned midstream due to the extensive overlap with approach #3. The main objective of this scientific study was to see whether AI could be used in a scientifically appropriate manner to improve the scientific writing process. Indeed, AI reduced the time for writing but had significant inaccuracies. The latter necessitates that AI cannot currently be used alone but could be used with careful oversight by humans to assist in writing scientific review articles.