Transforming Depression Care with Artificial Intelligence DOI

Jehad Feras AlSamhori,

Abdel Rahman Feras AlSamhori, Diala Ra’Ed Kamal Kakish

et al.

Asian Journal of Psychiatry, Journal Year: 2024, Volume and Issue: 101, P. 104235 - 104235

Published: Sept. 7, 2024

Language: Английский

Artificial intelligence for hearing loss prevention, diagnosis, and management DOI Creative Commons

Jehad Feras AlSamhori,

Abdel Rahman Feras AlSamhori,

Rama Mezyad Amourah

et al.

Journal of Medicine Surgery and Public Health, Journal Year: 2024, Volume and Issue: 3, P. 100133 - 100133

Published: Aug. 1, 2024

Language: Английский

Citations

9
The role of artificial intelligence in enhancing nurses' work-life balance DOI Creative Commons
Moustaq Karim Khan Rony, Daifallah Alrazeeni, Fazila Akter

et al.

Journal of Medicine Surgery and Public Health, Journal Year: 2024, Volume and Issue: 3, P. 100135 - 100135

Published: Aug. 1, 2024

Language: Английский

Citations

9
Transforming Dermatopathology With AI: Addressing Bias, Enhancing Interpretability, and Shaping Future Diagnostics DOI Creative Commons
Diala Ra’Ed Kamal Kakish, Jehad Feras AlSamhori,

Andy Noel Ramirez Fajardo

et al.

Dermatological Reviews, Journal Year: 2025, Volume and Issue: 6(1)

Published: Jan. 17, 2025

ABSTRACT Background Artificial intelligence (AI) is transforming dermatopathology by enhancing diagnostic accuracy, efficiency, and precision medicine. Despite its promise, challenges such as dataset biases, underrepresentation of diverse populations, limited transparency hinder widespread adoption. Addressing these gaps can set a new standard for equitable patient‐centered care. To evaluate how AI mitigates improves interpretability, promotes inclusivity in while highlighting novel technologies like multimodal models explainable (XAI). Results AI‐driven tools demonstrate significant improvements precision, particularly through that integrate histological, genetic, clinical data. Inclusive frameworks, the Monk scale, advanced segmentation methods effectively address biases. However, “black box” nature AI, ethical concerns about data privacy, access to low‐resource settings remain. Conclusion offers transformative potential dermatopathology, enabling equitable, innovative diagnostics. Overcoming persistent will require collaboration among dermatopathologists, developers, policymakers. By prioritizing inclusivity, transparency, interdisciplinary efforts, redefine global standards foster

Language: Английский

Citations

1
Leveraging Artificial Intelligence in Breast Cancer Screening and Diagnosis DOI Open Access
Abdul Haseeb Hasan, Umair Khalid, Muhammad Ali Abid

et al.

Cureus, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

Breast cancer remains the most prevalent malignancy worldwide, posing a significant public health burden due to its high incidence and mortality rates. Early detection through mammography has been instrumental in reducing breast cancer-related deaths; however, traditional screening methods are constrained by human limitations, including variability interpretation resource-intensive workflows. Artificial intelligence (AI) emerged as transformative tool diagnostics, leveraging machine learning (ML) deep (DL) algorithms enhance accuracy, efficiency, accessibility. AI applications digital (DM), tomosynthesis (DBT), ultrasound, magnetic resonance imaging (MRI) have demonstrated improved sensitivity specificity, false positives negatives while optimizing radiologist workload. Despite these advancements, challenges such data accessibility, algorithm biases, regulatory constraints, clinical integration hinder widespread adoption. Addressing limitations requires standardized validation protocols, enhanced interpretability explainable (XAI), clinician patient education. This editorial explores evolving role of diagnosis, emphasizing potential bridge healthcare disparities improve global outcomes.

Language: Английский

Citations

1
Artificial Intelligence for Medicine, Surgery, and Public Health DOI Creative Commons
Jagdish Khubchandani, Srikanta Banerjee, R. Andrew Yockey

et al.

Journal of Medicine Surgery and Public Health, Journal Year: 2024, Volume and Issue: unknown, P. 100141 - 100141

Published: Oct. 1, 2024

Language: Английский

Citations

4
Amelanotic Melanoma: Diagnostic Challenges, Treatment Innovations, and the Emerging Role of AI in Early Detection DOI Creative Commons
Diala Ra’Ed Kamal Kakish, Jehad Feras AlSamhori, Ahmad Ayman

et al.

Journal of Medicine Surgery and Public Health, Journal Year: 2025, Volume and Issue: unknown, P. 100189 - 100189

Published: March 1, 2025

Language: Английский

Citations

0
Application of Fourier Transform Infrared Spectroscopy on Breast Cancer Diagnosis combined with multiple algorithms: A Systematic Review DOI Creative Commons
Yeniewa Kerie Anagaw, Gizachew Kassahun Bizuneh,

Melaku Getahun Feleke

et al.

Photodiagnosis and Photodynamic Therapy, Journal Year: 2025, Volume and Issue: unknown, P. 104579 - 104579

Published: April 1, 2025

Fourier transform infrared (FT-IR) spectroscopy is an innovative diagnostic technique for improving early detection and personalized care breast cancer patients. It allows rapid accurate analysis of biological samples. Therefore, the purpose this study was to assess accuracy FT-IR cancer, based on a comprehensive literature review. An online electronic database systematic search conducted using PubMed/Medline, Cochrane Library, hand databases from March 28, 2024, April 10, 2024. We included peer-reviewed journal articles in which used acquire data cancers manuscripts published English. All eligible studies were assessed Quality Assessment Diagnostic Accuracy Studies (QUADAS) tool. Serum, biopsies, blood plasma, specimen, saliva samples study. This revealed that diagnosis with algorithms had sensitivity specificity 98% 100%, respectively. Almost all have more than one algorithm analyze spectral data. finding showed reported six greater 90%. Employing multivariate coupled has shown promise differentiating between healthy cancerous tissue. review will be next gold standard diagnosis. However, draw definitive conclusions, larger-scale studies, external validation, real-world clinical trials, legislative considerations, alternative methods such as Raman should considered.

Language: Английский

Citations

0
Attitudes of older patients toward artificial intelligence in decision-making in healthcare DOI Creative Commons
Moustaq Karim Khan Rony,

Tuli Rani Deb,

Most. Tahmina Khatun

et al.

Journal of Medicine Surgery and Public Health, Journal Year: 2025, Volume and Issue: unknown, P. 100193 - 100193

Published: April 1, 2025

Language: Английский

Citations

0
Advancing Breast Cancer Diagnosis: Integrating Deep Transfer Learning and U-Net Segmentation for Precise Classification and Delineation of Ultrasound Images DOI Creative Commons
Divine Senanu Ametefe, Dah John,

Abdulmalik Adozuka Aliu

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 105047 - 105047

Published: April 1, 2025

Language: Английский

Citations

0
Evaluation of Artificial Intelligence Models for Nutritional Symptom Management in Breast Cancer Patients Undergoing Chemotherapy DOI
Şenay Burçin Alkan, Elif Didem Örs Demet

Nutrition and Cancer, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 7

Published: May 5, 2025

The purpose of the study is to determine if artificial intelligence (AI) models could provide dietary recommendations manage chemotherapy-induced nutritional symptoms in breast cancer (BC) patients that are on comparable levels with American Cancer Society, National Institute and World Research Fund guidelines which were used as evidence-based recommendation. AI models-ChatGPT, ChatGPT 4.0, Gemini, Gemini Advanced, Copilot, Copilot Pro-were evaluated based their adherence guidelines. Specific queries posed each model, generated responses rated by two experienced dietitians using a 5-point likert scale. provided most adherent for metallic taste mouth, while Advanced excelled managing dehydration. effective addressing heartburn, Pro consistently showed lowest performance across symptoms. Overall, 4.0 attained highest total score, followed indicating general trend where certain better suited specific Various (e.g. 4.0) show potential symptoms, but they do not align recommendations. Improvement necessary

Language: Английский

Citations

0