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Journal articles on the topic 'AI powered patent tools'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Shahzeb Akhtar. "The Role of Artificial Intelligence in Enhancing Patent Lifecycle Management." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 2194–205. https://doi.org/10.32628/cseit23112582.

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This article explores the transformative role of Artificial Intelligence (AI) across the entire patent lifecycle, from initial ideation to post-grant management. It examines how AI technologies are revolutionizing various aspects of patent processes, including brainstorming and invention disclosure, prior art searches, patent drafting, office action responses, and portfolio management. The article discusses the potential of AI to enhance efficiency, accuracy, and strategic decision-making in patent management while also addressing the ethical and legal challenges that arise from integrating AI

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Mr., Mohd Abdul Muneim Farzaan. "Revolutionizing Patent Processes: The Rise of AI-Assisted Patent Prosecution." Annual International Journal on Analysis of Contemporary Legal Affairs (AIJACLA) 5 (May 26, 2025): 220–29. https://doi.org/10.5281/zenodo.15521506.

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<em>The rapid advancement of Artificial intelligence is transforming innovation across industries, including intellectual property (IP). AI generated inventions has become increasingly prevalent worldwide and are reshaping its traditional processes of patent Drafting, examination and enforcement. AI tools predict patentability and automate patent searches with high efficiency. These advancements enable patent professionals enhance their decision making. An AI system generates novel ideas and inventions for intellectual property rights and for patent purposes and it also helps in learning machi

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Deepa P., Badrinath P., Mohanaprasanth N., Pranesh S., and Sriram E. "AI-Powered Doctolib: Revolutionizing Healthcare Appointment Management." Asian Journal of Basic Science & Research 06, no. 04 (2024): 90–102. https://doi.org/10.38177/ajbsr.2024.6407.

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Healthcare systems face persistent challenges in managing appointments and improving accessibility, particularly in underserved regions. This paper explores the development and implementation of Doctolib, an AI-driven healthcare platform designed to address these issues. By utilizing advanced algorithms, real-time scheduling, and predictive analytics, Doctolib enhances the efficiency of appointment management. Key features include an AI chatbot for 24/7 patient support, secure telemedicine integration for remote consultations, and demand optimization tools to better allocate resources. The pla

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Zampatti, Stefania, Juliette Farro, Cristina Peconi, et al. "AI-Powered Neurogenetics: Supporting Patient’s Evaluation with Chatbot." Genes 16, no. 1 (2024): 29. https://doi.org/10.3390/genes16010029.

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Background/Objectives: Artificial intelligence and large language models like ChatGPT and Google’s Gemini are promising tools with remarkable potential to assist healthcare professionals. This study explores ChatGPT and Gemini’s potential utility in assisting clinicians during the first evaluation of patients with suspected neurogenetic disorders. Methods: By analyzing the model’s performance in identifying relevant clinical features, suggesting differential diagnoses, and providing insights into possible genetic testing, this research seeks to determine whether these AI tools could serve as a

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Vidushi, Sharma. "AI-Powered Automation in Healthcare: Improving Patient Care and Operational Efficiency." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 7, no. 4 (2019): 1–8. https://doi.org/10.5281/zenodo.14785680.

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Artificial Intelligence (AI) and automation technologies have become pivotal in transforming the healthcare sector, offering innovative solutions that enhance both patient care and operational efficiency. The integration of AI-powered automation can address several critical challenges faced by modern healthcare systems, including rising patient demand, resource limitations, and the increasing complexity of healthcare management. AI-driven systems provide opportunities to automate routine tasks, improve diagnostic accuracy, optimize clinical workflows, and deliver personalized treatment plans b

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Akter, Sharmin, MD Sheam Arafat, Kirtibhai Desai, Mir Abrar Hossain, and Ayesha Islam Asha. "AI-Powered Computing Racks: Transforming Healthcare IT with Faster Diagnostics and Intelligent Data Processing." American Journal of Applied Sciences 07, no. 05 (2025): 07–30. https://doi.org/10.37547/tajas/volume07issue05-02.

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Healthcare IT underwent a revolution through artificial intelligence (AI) together with high-performance computing which particularly enhances diagnostics along with intelligent data processing operations. The use of AI-powered computing racks delivers exceptional speed alongside efficiency for handling large-scale medical data which leads to faster diagnoses and real-time patient observation and precise medical treatments. This paper studies how AI-powered computing racks redefine healthcare IT operations through their ability to boost computational power and generate more accurate diagnoses

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Basnawi, Abdullah, and Ahmad Koshak. "Application of Artificial Intelligence in Advanced Training and Education of Emergency Medicine Doctors: A Narrative Review." Emergency Care and Medicine 1, no. 3 (2024): 247–59. http://dx.doi.org/10.3390/ecm1030026.

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Emergency medicine (EM) demands continuous adaptation and refinement of training methodologies to equip healthcare professionals with the expertise to effectively manage complex and time-sensitive patient presentations. Artificial intelligence (AI), with its remarkable ability to process vast amounts of data, identify patterns, and make predictions, holds immense promise for enhancing the advanced training and education of EM physicians. This narrative review aims to discuss the potential of AI in transforming EM training and highlight the specific applications of AI in personalized learning,

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A R, SALIM MALIK. "AI Continues to be a Game Changer in the Intellectual Property Management in India." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41289.

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The integration of Artificial Intelligence (AI) into Intellectual Property (IP) management has significantly reshaped the landscape of IP administration in India. With the growing emphasis on innovation and technological advancement, India's IP ecosystem is rapidly adopting AI-driven solutions to streamline processes, enhance accuracy, and reduce human intervention. AI-powered tools for prior art searches, patent classification, and infringement detection have significantly accelerated IP workflows while reducing the backlog of pending applications. This paper explores the transformative role

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Saroj Kumar Rout. "Enhancing Clinical Communication through AI-Powered Recording and Analysis: A Multi-Center Study of Scribr AI Implementation." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 1170–78. https://doi.org/10.32628/cseit241061158.

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This article examines the implementation and effectiveness of Scribr AI, an artificial intelligence-powered system designed to enhance doctor-patient communication through automated recording, transcription, and multilingual support in clinical settings. The article evaluates the platform's impact on clinical documentation, patient comprehension, and longitudinal care management across multiple healthcare facilities. Through a comprehensive article analysis of system usage, patient engagement, and healthcare provider feedback, the article demonstrates significant improvements in communication

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Bajhzer, Rahaf, Mona Alghamdi, and Salma Elhag. "AI Powered Bioinformatics - Expediting Diagnostic Testing: A Survey." International Journal of Computers and Informatics 3, no. 9 (2024): 22–52. http://dx.doi.org/10.59992/ijci.2024.v3n9p2.

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Research has demonstrated the positive impact of artificial intelligence and Bioinformatics in the field of clinical diagnosis. The integration of AI methodologies into bioinformatics has opened new avenues for breakthroughs in genomics, proteomics, and personalized medicine. The document emphasizes the role of AI in early disease detection, improving patient outcomes, and enhancing healthcare systems by avoiding the need for expensive and time-consuming operations as illnesses worsen. The methodology section provides insights into the approach utilized, including the review of 30 articles fro

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Sharma, Dr Mahendra, and Mrs Laveena Sehgal. "The Revolutionary Computer Technology Artificial Intelligence that is Changing Our World & its Impact on Different Jobs." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 286–93. http://dx.doi.org/10.22214/ijraset.2023.50072.

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Abstract: This research paper provides an in-depth analysis of the impact of Artificial Intelligence (AI) on various job sectors. It explores the history of AI and highlights the top AI tools that are being used today. The primary focus of the research is on the effects of AI on different job sectors, including healthcare, finance, and transportation. Additionally, the paper examines the potential benefits and challenges of AI adoption, including the concerns about job displacement and the future of work. Ultimately, the research concludes that a responsible approach to AI adoption is necessar

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Nageswara, Rao Yatam* Chakradhar Chikkala Veeranjaneyulu Donepalli Narayana Raju Padala Bhaskararaju Vatchavai Sravanthi Podila. "Diagnosis And Treatment of Alzheimer's Disease Using Artificial Intelligence." International Journal of Pharmaceutical Sciences 3, no. 2 (2025): 50–62. https://doi.org/10.5281/zenodo.14786653.

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The identification, diagnosis, and treatment of Alzheimer's disease (AD), a neurodegenerative illness marked by memory loss and cognitive decline, are being completely transformed by artificial intelligence (AI). Artificial intelligence (AI)-powered services use natural language processing, deep learning models, and machine learning algorithms to analyse huge data sets from genomics, neuroimaging, and electronic medical records. By capturing minute biomarkers in speech patterns, cerebrospinal fluid, and brain scans that may anticipate clinical symptoms, these technologies allow for early detec

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Lesport, Quentin, Davis Palmie, Gülşen Öztosun, Henry J. Kaminski, and Marc Garbey. "AI-Powered Telemedicine for Automatic Scoring of Neuromuscular Examinations." Bioengineering 11, no. 9 (2024): 942. http://dx.doi.org/10.3390/bioengineering11090942.

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Telemedicine is now being used more frequently to evaluate patients with myasthenia gravis (MG). Assessing this condition involves clinical outcome measures, such as the standardized MG-ADL scale or the more complex MG-CE score obtained during clinical exams. However, human subjectivity limits the reliability of these examinations. We propose a set of AI-powered digital tools to improve scoring efficiency and quality using computer vision, deep learning, and natural language processing. This paper focuses on automating a standard telemedicine video by segmenting it into clips corresponding to

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Awan, Tehreem, Muhammad Ali, Mushahid Hussain, Muhammad Rashid, Sungraiz Ali, and Serosh Karim Noon. "AI-Powered Lung Cancer Detection From CT Imaging." VFAST Transactions on Software Engineering 12, no. 2 (2024): 241–49. http://dx.doi.org/10.21015/vtse.v12i2.1852.

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Lung cancer is one of the deadliest forms of cancer, witnessing thousands of new diagnoses annually. Early detection remains paramount; without it, survival rates plummet drastically. This underscores the critical role of employing artificial intelligence (AI) for early diagnosis, a pivotal step in combating this devastating illness. This study introduces a sophisticated computer-aided system, aiming to revolutionize lung cancer detection through state-of-the-art convolutional neural network (CNN) technology. By harnessing the capabilities of AI and CNN's, enabling precise categorization of pa

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Vedamurthy Gejjegondanahalli Yogeshappa and Swathi Meghana Kolar Rajeev. "The future of healthcare: AI-powered solutions for enhanced access and disease management." World Journal of Advanced Research and Reviews 25, no. 3 (2025): 2152–60. https://doi.org/10.30574/wjarr.2025.25.3.0966.

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Artificial Intelligence (AI) is transforming the healthcare industry by improving access, optimizing cost-efficiency, and enhancing population health management. AI-powered tools facilitate early disease detection, automate administrative processes, and provide predictive insights for medical decision-making. This paper examines recent AI-driven innovations in healthcare, focusing on Medicaid, Medicare, and disease prediction models. The integration of AI with cloud platforms and blockchain enhances healthcare security, accessibility, and scalability. However, ethical challenges and regulatory

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Ogbuagu, Oluchukwu Obinna, Akachukwu Obianuju Mbata, Obe Destiny Balogun, Olajumoke Oladapo, Opeyemi Olaoluawa Ojo, and Muridzo Muonde. "Artificial intelligence in clinical pharmacy: enhancing drug safety, adherence, and patient-centered care." International Journal of Multidisciplinary Research and Growth Evaluation 4, no. 1 (2023): 814–22. https://doi.org/10.54660/.ijmrge.2023.4.1-814-822.

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Artificial Intelligence (AI) rapidly transforms clinical pharmacy by enhancing drug safety, medication adherence, and patient-centered care. This paper explores the application of AI in clinical pharmacy and its potential to revolutionize pharmacy practices by improving patient outcomes. It highlights AI-driven innovations in drug safety through predictive analytics for adverse drug event detection and prevention and advancements in pharmacovigilance. Additionally, AI’s role in optimizing medication adherence through personalized strategies, reminders, and digital tools is discussed, emphasizi

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Crucitti, Davide, Carlos Pérez Míguez, Marta Sonia Gonzalez Perez, et al. "Myeloma and Lymphoma Medical Helpers: AI-Powered Decision Support Tools for Hematologic Malignancies." Blood 144, Supplement 1 (2024): 7478. https://doi.org/10.1182/blood-2024-194597.

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Introduction: Recent advancements in therapeutic options for multiple myeloma (MM) and lymphoma have significantly improved patient prognosis and survival. However, the variety of treatments complicates decision-making for optimal therapy selection, with new drugs often presenting challenging adverse effects, interactions and dose adjustments. To address these challenges, we developed Myeloma Medical Helper and Lymphoma Medical Helper, AI-driven tools to assist hematologists in clinical decision-making based on the latest medical guidelines and scientific evidence. Objectives: Develop AI-based

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Vashistha, Krishna, Jayant Kataria, and Ekta Puri. "Debunking AI and Healthcare: The Connection." Radius: Journal of Science and Technology 2, no. 1 (2025): 251003. https://doi.org/10.5281/zenodo.14945533.

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Artificial intelligence has transformed the healthcare industry so much in terms of diagnosis, treatment, and patient care over the years. This paper outlines how AI was introduced into the healthcare industry from its humble beginnings with early tools that date back to the 20<sup>th</sup> century like decision support systems and various diagnostic programs. Recently, AI has affected the precision medicine area significantly with deep learning models, used for accurate analysis of medical images. AI also improved personalized treatments, particularly cancer therapies and drug development. Sy

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Arefin, Sabira. "Strengthening Healthcare Data Security with Ai-Powered Threat Detection." International Journal of Scientific Research and Management (IJSRM) 12, no. 10 (2024): 1477–83. http://dx.doi.org/10.18535/ijsrm/v12i10.ec02.

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As the healthcare industry undergoes rapid digital transformation, the need for robust cybersecurity measures has never been more critical. AI-driven solutions, such as Machine Learning (ML) and anomaly detection, are proving to be pivotal in securing healthcare data. These technologies enable healthcare organizations to identify cyber threats proactively, automate incident response, and enhance data security. Furthermore, AI's ability to provide continuous network monitoring, predictive analytics, and real-time anomaly detection offers healthcare providers the tools needed to mitigate risks b

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Giorgi, Lucrezia, Domiziana Nardelli, Antonio Moffa, et al. "Advancements in Obstructive Sleep Apnea Diagnosis and Screening Through Artificial Intelligence: A Systematic Review." Healthcare 13, no. 2 (2025): 181. https://doi.org/10.3390/healthcare13020181.

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Background: Obstructive sleep apnea (OSA) is a prevalent yet underdiagnosed condition associated with a major healthcare burden. Current diagnostic tools, such as full-night polysomnography (PSG), pose a limited accessibility to diagnosis due to their elevated costs. Recent advances in Artificial Intelligence (AI), including Machine Learning (ML) and deep learning (DL) algorithms, offer novel potential tools for an accurate OSA screening and diagnosis. This systematic review evaluates articles employing AI-powered models for OSA screening and diagnosis in the last decade. Methods: A comprehens

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Hossain, Mir Abrar, MD Sheam Arafat, Kirtibhai Desai, Sharmin Akter, and Ayesha Islam Asha. "The Economic Impact of AI-Driven Remote Patient Monitoring: A Business Intelligence Perspective on Healthcare Cost Optimization." International Interdisciplinary Business Economics Advancement Journal 06, no. 05 (2025): 39–67. https://doi.org/10.55640/business/volume06issue05-03.

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Healthcare cost optimization has improved through the implementation of Artificial Intelligence in remote patient monitoring because it increases operational efficiency and predictive analytics and allows immediate patient care management. This paper examines AI-powered RPM's economic value by showing its capacity to decrease hospital admissions together with reducing healthcare expenses while directing resources effectively. Business intelligence tools with AI help RPM systems create predictive intervention strategies for better patient results without affecting operational costs. This resear

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Kamal, S. M. Mustofa. "Pioneering Innovations Transforming Medicine and Shaping the Future of Patient Care." Asia Pacific Journal of Medical Innovations 1, no. 1 (2024): 1–4. https://doi.org/10.70818/apjmi.2024.v01i01.01.

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In recent years, the landscape of healthcare has been undergoing a profound transformation driven pioneering innovations [1]. These advancements are not only revolutionizing how medical professionals diagnose, treat, and manage diseases but are also enhancing the quality of patient care in unprecedented ways. From the integration of artificial intelligence (AI) in medical diagnostics to the development of personalized medicine, the future of healthcare is being reshaped a wave of technological breakthroughs. This article explores some of the most significant innovations in modern medicine and

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Kumar, Sahil, Syed Ali Mehsam, Tariq Rafique, et al. "Ai-Powered Predictive Analytics In General Surgery: Improving Patient Safety And Surgical Outcomes." Journal of Neonatal Surgery 14, no. 13S (2025): 937–45. https://doi.org/10.63682/jns.v14i13s.3388.

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Background: The role of AI-based predictive analytics in general surgery. The use of artificial intelligence (AI) tools in surgical environments is influenced by several factors including perceived usefulness, usability, and trust between professionals. This study focused on the core factors of acceptance and use of artificial intelligence-24 predictive analytics in surgery with the use of the technology acceptance model (TAM). Methods: A cross-sectional quantitative survey was conducted on 273 healthcare providers (general surgeons, surgical residents, anesthesiologists, operating room nurses

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Rastogi, Isha, Saif R. Siddiqui, and Fatima Rasheed Khan. "Artificial intelligence up fronting dentistry." Journal of Orofacial and Health Sciences 11, no. 2 (2024): 47–49. http://dx.doi.org/10.18231/j.johs.2024.012.

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Artificial Intelligence (AI) is revolutionizing the field of dentistry by enhancing diagnostic accuracy, treatment planning, and patient care. This article provides an overview of the significant advancements AI has introduced in dental practice. The integration of AI-driven tools, such as machine learning algorithms and computer vision, enables precise analysis of radiographs, 3D imaging, and other diagnostic data, leading to early detection of dental diseases and anomalies. AI-powered systems facilitate personalized treatment plans by predicting outcomes and optimizing procedures based on va

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Chowdhury, Adiba Tabassum, Abdus Salam, Mansura Naznine, et al. "Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Review of Recent Advances." Diagnostics 14, no. 18 (2024): 2059. http://dx.doi.org/10.3390/diagnostics14182059.

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Artificial intelligence (AI) is providing novel answers to long-standing clinical problems, and it is quickly changing pediatric urology. This thorough analysis focuses on current developments in AI technologies that improve pediatric urology diagnosis, treatment planning, and surgery results. Deep learning algorithms help detect problems with previously unheard-of precision in disorders including hydronephrosis, pyeloplasty, and vesicoureteral reflux, where AI-powered prediction models have demonstrated promising outcomes in boosting diagnostic accuracy. AI-enhanced image processing methods h

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Veras, Mirella, Jordi Pardo, Mê-Linh Lê, Cindy Jussup, José Carlos Tatmatsu-Rocha, and Vivian Welch. "A Protocol for AI-Powered Tools to Enhance Mobility and Function in Older Adults: An Evidence and Gap Map." Journal of Personalized Medicine 15, no. 1 (2025): 29. https://doi.org/10.3390/jpm15010029.

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Introduction: Artificial intelligence (AI) is transforming healthcare by enhancing diagnostic accuracy, treatment, and patient monitoring, benefiting older adults by offering personalized care plans. AI-powered tools help manage chronic conditions and maintain independence, making them a valuable asset in addressing aging challenges. Objectives: The objectives are as follows: 1. To identify and describe AI-power-based exercise programs for older adults. 2. To highlight primary evidence gaps in AI interventions for functional improvement and mobility. 3. To evaluate the quality of existing revi

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Gaonkar, Prachi, Imad Mohammed, Mohammed Ribin, D. Chetan Kumar, Pallavi A. Thomas, and Ruchi Saini. "Assessing the Impact of AI-Enhanced Diagnostic Tools on the Treatment Planning of Orthodontic Cases: An RCT." Journal of Pharmacy and Bioallied Sciences 16, Suppl 2 (2024): S1798—S1800. http://dx.doi.org/10.4103/jpbs.jpbs_1147_23.

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ABSTRACT Background: Orthodontic treatment planning involves the precise assessment of dental and skeletal anomalies, which can be facilitated by AI-enhanced diagnostic tools. Materials and Methods: A total of 100 orthodontic cases were included in this RCT. Patients were randomly assigned to two groups: an AI-enhanced diagnostic group and a traditional diagnostic group. The AI-enhanced diagnostic group underwent orthodontic assessment with the aid of AI-powered software, which provided automated cephalometric analysis, 3D model evaluations, and treatment suggestions. The traditional diagnosti

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Lakhani, Amit. "Revolutionizing orthopedic care: The impact of ai in predictive analysis, surgical precision, and personalized rehabilitation." Journal of Community Health Management 11, no. 3 (2024): 120–32. http://dx.doi.org/10.18231/j.jchm.2024.022.

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Artificial intelligence (AI) is transforming the field of orthopedics, significantly impacting predictive analysis, surgical management, and rehabilitation programs. This review explores the multifaceted role of AI in enhancing orthopedic care, focusing on its application in personalized treatment plans, surgical precision, and remote rehabilitation. Predictive analytics in orthopedics, powered by AI, have revolutionized preoperative planning by forecasting surgical outcomes and potential complications, enabling clinicians to tailor surgical strategies to individual patient needs. AI's integra

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Morenikeji, I. Yisa. "Leveraging Artificial Intelligence in healthcare to optimize patient outcomes, with specialized staff training programs." World Journal of Advanced Research and Reviews 24, no. 3 (2024): 3078–93. https://doi.org/10.5281/zenodo.15259577.

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Artificial intelligence (AI) is transforming the healthcare industry by optimizing patient outcomes, enhancing diagnostic accuracy, and streamlining operational efficiency. AI technologies such as machine learning, predictive analytics, and natural language processing are increasingly being integrated into clinical decision-making processes, enabling healthcare providers to deliver personalized, data-driven care. These technologies help in analysing vast amounts of patient data, identifying patterns, and predicting potential health risks, thereby improving clinical decision-making and treatmen

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Kiran, Veernapu. "Saving Lives through Effective Training and Development of Employeesin Healthcare using AI Tools." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 11, no. 2 (2023): 1–8. https://doi.org/10.5281/zenodo.14996422.

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Skilled workforce makes any organization a thriving and a successful organization. Employees need to stay current with the technological advancement and to be effective in the job one need to upgrade the skill set consistently. Especially in healthcare, healthcare professionals need to understand how to use the new tools and techniques and the clinical research outcomes. Investing in employee training, improving the competency, skills, encouraging the talent and innovation leads an organization to be unique among the competitor and bring a better customer service. There are several modes of em

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Aksoy, Serra, and Pritika Dasgupta. "AI-Powered Neuro-Oncology: EfficientNetB0’s Role in Tumor Differentiation." Clinical and Translational Neuroscience 9, no. 1 (2025): 2. https://doi.org/10.3390/ctn9010002.

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Background/Objectives: Brain tumors are among the severe and life-threatening conditions, with timely and accurate detection being crucial for determining the appropriate course of treatment. These tumors can vary widely in their aggressiveness, location, and type, making early diagnosis and precise classification essential for improving patient outcomes and survival rates. The complexity of brain tumors, combined with the detailed nature of MRI scans, presents significant challenges in the diagnostic process, underscoring the need for advanced tools to support clinicians in making informed de

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Lukkahatai, Nada, and Gyumin Han. "Perspectives on Artificial Intelligence in Nursing in Asia." Asian/Pacific Island Nursing Journal 8 (June 19, 2024): e55321. http://dx.doi.org/10.2196/55321.

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Artificial intelligence (AI) is reshaping health care, including nursing, across Asia, presenting opportunities to improve patient care and outcomes. This viewpoint presents our perspective and interpretation of the current AI landscape, acknowledging its evolution driven by enhanced processing capabilities, extensive data sets, and refined algorithms. Notable applications in countries such as Singapore, South Korea, Japan, and China showcase the integration of AI-powered technologies such as chatbots, virtual assistants, data mining, and automated risk assessment systems. This paper further e

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Herrick, Grace, Kelly Frasier, Vivian Li, Haily Fritts, Emily Woolhiser, and Julia Vinagolu-Baur. "Enhancing patient education and engagement through digital intelligence tools in dermatology." International Journal of Research in Dermatology 10, no. 6 (2024): 391–400. http://dx.doi.org/10.18203/issn.2455-4529.intjresdermatol20243342.

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This comprehensive digital health review examines the expanding landscape of digital intelligence tools within dermatology, specifically examining the innovative role of AI-powered applications aimed at educating and engaging patients in managing their skin health. Drawing upon a wide array of existing research, this review scrutinizes the evolution and impact of various digital tools. These include interactive apps that leverage AI algorithms to deliver tailored skincare guidance, considering factors such as skin type, concerns, and environmental factors. Additionally, the review explores the

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Siolos, Pavlos, Saif Pasha, Maria Triantafyllou, et al. "Harnessing AI for Improved Diagnosis and Management of Pediatric Sepsis: Current Advances, Challenges, and Future Directions." Pediatric Emergency Care 41, no. 7 (2025): 576–85. https://doi.org/10.1097/pec.0000000000003397.

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Artificial intelligence (AI) has been applied to early recognition and management of rapidly progressive, community-acquired pediatric sepsis, a leading cause of childhood mortality. The broad adoption of electronic health records combined with rapid advances in digital technologies have enabled the federated training of both knowledge-driven AI, known as expert systems, trained by teams of collaborating clinicians, and data-driven AI, known as machine learning (ML), to derive predictive, clustering algorithms trained on “big data.” An important subset of ML is “deep learning,” which includes

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Giridhar Raj Singh Chowhan. "The role of AI-powered CRM in personalized healthcare and patient engagement." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 168–77. https://doi.org/10.30574/wjarr.2025.26.2.1574.

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This article examines the transformative impact of artificial intelligence-powered Customer Relationship Management (CRM) systems on healthcare delivery and patient engagement. As the healthcare sector undergoes digital transformation, AI-enhanced CRM platforms have evolved from basic administrative tools into sophisticated systems that enable personalized care and proactive health management. The integration of machine learning algorithms, natural language processing, and predictive analytics has revolutionized patient profiling, risk assessment, and care management. These systems excel at ag

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P, Ashwini, and Prabir Chandra Padhy. "Impact of AI on Healthcare with Specific Reference to Nurses' Education." Journal of Advanced Zoology 44, S-5 (2023): 818–29. http://dx.doi.org/10.17762/jaz.v44is-5.1002.

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Integrating Artificial Intelligence (AI) in healthcare has revolutionized the industry by enhancing patient care, optimizing clinical workflows, and improving healthcare professionals' education and training. This paper explores the multifaceted impact of AI on healthcare delivery, specifically focusing on its influence on nurse education. AI-driven technologies have ushered in a new era of personalized learning, enabling nurses to acquire essential skills, stay current with medical advancements, and adapt to dynamic healthcare environments. Additionally, AI-powered tools have streamlined admi

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Dr, Nisha Pandey, and Medha Lakhanam Dr. "Artificial Intelligence in Dentistry: The New Opportunity to Discover." INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH AND ANALYSIS 07, no. 12 (2024): 5596–99. https://doi.org/10.5281/zenodo.14523941.

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Artificial intelligence (AI) is revolutionizing the field of dentistry by enabling advanced diagnostic, treatment planning, and procedural precision. From automated radiographic analysis to robotic-assisted surgeries, AI technologies enhance efficiency and accuracy in patient care. AI-powered imaging tools improve early detection of oral diseases, such as caries, periodontal conditions, and malignancies, by analyzing patterns beyond human capability. In restorative dentistry, AI aids in designing prosthetics and optimizing implant placement with unprecedented precision, reducing complications

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Maram Khalid Ahmad Harbi. "The Role of Nurses and Optical Technicians in Using Ai for Early Vision Problem Detection." Power System Technology 48, no. 4 (2024): 2800–2819. https://doi.org/10.52783/pst.1167.

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The integration of Artificial Intelligence (AI) in healthcare has revolutionized diagnostic approaches, particularly in the field of ophthalmology and vision care. Nurses and optical technicians play a crucial role in the early detection of vision problems, especially with the implementation of AI-powered tools and systems. AI has enhanced the efficiency, accuracy, and speed of diagnosing vision-related issues by analyzing vast amounts of imaging data, medical histories, and patient symptoms. This article explores the collaborative role of nurses and optical technicians in utilizing AI for ear

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Mahor, Harshita, and Surbhi Gupta. "Design a AI Healthcare Chatbot." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 890–95. https://doi.org/10.22214/ijraset.2025.70067.

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Abstract : The Artificial Intelligence (AI) chatbots have emerged as transformative tools in the healthcare sector, enhancing patient engagement, automating routine tasks, and improving healthcare accessibility. This paper explores the development of AI-driven chatbots for healthcare, analyzing their significance, methodologies, and impact on patient care. Through a comprehensive literature review and experimental evaluation, this study presents insights into the effectiveness of healthcare chatbots and the challenges they face, including ethical concerns and data security. The results indicat

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Thekkumbad, Vishnu Govind, Mounica Pinnamaneni, Sruthi Kalakota, Krishma Pandoh, Syeda Hussain, and Ridhi Bhola Sandeep Singh. "Artificial Intelligence: A Paradigm Shift in General Dental Practice." Saudi Journal of Oral and Dental Research 10, no. 05 (2025): 233–40. https://doi.org/10.36348/sjodr.2025.v10i05.002.

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AI is rapidly transforming the landscape of general dental practice by enhancing diagnostic accuracy, offering innovative treatment strategies, and streamlining administrative workflows. This descriptive review explores the latest advancements in AI within dentistry. Machine learning models, trained on extensive datasets of dental images, have shown remarkable accuracy in the early detection of dental conditions such as caries, periodontal diseases, and tooth structure anomalies. These tools enable timely interventions, improving clinical outcomes while reducing the burden on dental healthcare

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Ayyappan, Vithyash, and Vikash Ayyappan. "Artificial Intelligence in Healthcare: A New Frontier in Medical Innovation." Healthcare Administration Leadership & Management Journal 3, no. 3 (2025): 140–43. https://doi.org/10.55834/halmj.8737939725.

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Artificial intelligence (AI) is revolutionizing healthcare by enhancing diagnostics, treatment, and patient care through its ability to process vast amounts of data quickly, enabling early disease detection, accurate treatments, and personalized care plans. AI-powered tools, such as advanced imaging systems, improve diagnostic accuracy while reducing unnecessary tests, and in mental health, AI aids in early disorder detection and provides accessible support through virtual therapists and chatbots. By simplifying complex medical information, AI empowers patients to better understand their healt

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Hudda, Sahil, Dr Raj Kumar, and Dr Neerja Negi. "The Changing Landscape of Healthcare with State of the Art AI Technology." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 1700–1705. http://dx.doi.org/10.22214/ijraset.2024.61723.

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Abstract: Artificial Intelligence (AI) has emerged as a transformative force in healthcare, offering unparalleled opportunities to enhance patient outcomes, streamline processes, and reduce costs. This abstract provides an overview of the diverse applications of AI in healthcare, including medical imaging analysis, predictive analytics, personalized treatment plans, and administrative tasks automation. By leveraging machine learning algorithms and big data analytics, AI enables healthcare professionals to make more accurate diagnoses, predict disease progression, and customize treatment regime

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Onyekachukwu Victor Unanah and Esi Mansa Aidoo. "The potential of AI technologies to address and reduce disparities within the healthcare system by enabling more personalized and efficient patient engagement and care management." World Journal of Advanced Research and Reviews 25, no. 2 (2025): 2643–64. https://doi.org/10.30574/wjarr.2025.25.2.0641.

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Healthcare disparities remain a significant challenge worldwide, with socioeconomic, racial, and geographic factors limiting access to quality care. Traditional healthcare systems often struggle to provide equitable services, leading to disparities in disease prevention, early diagnosis, and treatment outcomes. Artificial intelligence (AI) technologies have emerged as a transformative force in healthcare, offering the potential to bridge these gaps by enabling more personalized, efficient, and accessible patient engagement and care management. AI-driven solutions can enhance early detection of

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CHIDOLUO VITUS, OKECHUKWU. "ARTIFICIAL INTELLIGENCE IN HEALTHCARE: REVOLUTIONIZING DIAGNOSIS AND TREATMENT THROUGH MACHINE LEARNING." New Healthcare Advancements and Explorations 01, no. 03 (2024): 01–03. https://doi.org/10.64347/3066-2591/nhae.012.

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Artificial intelligence (AI), particularly machine learning (ML), is rapidly transforming the healthcare landscape. This paper explores the profound impact of AI in healthcare, focusing on its application in revolutionizing diagnosis and treatment. We delve into the core principles of ML and its various subfields relevant to healthcare, including supervised, unsupervised, and reinforcement learning. Further, we examine specific examples of how AI-powered tools are being implemented to enhance diagnostic accuracy, personalize treatment plans, predict disease outbreaks, and improve patient outco

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Pham, Tuan D., Muy-Teck Teh, Domniki Chatzopoulou, Simon Holmes, and Paul Coulthard. "Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions." Current Oncology 31, no. 9 (2024): 5255–90. http://dx.doi.org/10.3390/curroncol31090389.

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Artificial intelligence (AI) is revolutionizing head and neck cancer (HNC) care by providing innovative tools that enhance diagnostic accuracy and personalize treatment strategies. This review highlights the advancements in AI technologies, including deep learning and natural language processing, and their applications in HNC. The integration of AI with imaging techniques, genomics, and electronic health records is explored, emphasizing its role in early detection, biomarker discovery, and treatment planning. Despite noticeable progress, challenges such as data quality, algorithmic bias, and t

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Rauf, Md Abdur, Md Majadul Islam Jim, Md Mahfuzur Rahman, and Md Tariquzzaman. "AI-POWERED PREDICTIVE ANALYTICS FOR INTELLECTUAL PROPERTY RISK MANAGEMENT IN SUPPLY CHAIN OPERATIONS: A BIG DATA APPROACH." Global Mainstream Journal 1, no. 4 (2024): 32–46. http://dx.doi.org/10.62304/ijse.v1i04.184.

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The rapid advancement of technology and the increasing complexity of global supply chains have heightened the need for robust intellectual property (IP) risk management strategies. This study explores the application of artificial intelligence (AI) and big data analytics in enhancing IP risk management within supply chains. A comprehensive literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, identifying 578 records through database searches and an additional 90 records through other sources. After removing duplicates

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Morenikeji I Yisa. "Leveraging Artificial Intelligence in healthcare to optimize patient outcomes, with specialized staff training programs." World Journal of Advanced Research and Reviews 24, no. 3 (2024): 3078–93. https://doi.org/10.30574/wjarr.2024.24.3.4041.

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Artificial intelligence (AI) is transforming the healthcare industry by optimizing patient outcomes, enhancing diagnostic accuracy, and streamlining operational efficiency. AI technologies such as machine learning, predictive analytics, and natural language processing are increasingly being integrated into clinical decision-making processes, enabling healthcare providers to deliver personalized, data-driven care. These technologies help in analysing vast amounts of patient data, identifying patterns, and predicting potential health risks, thereby improving clinical decision-making and treatmen

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Singh, Rishabh. "CelInsight:AI-Powered Web Solution for Cervical Cancer Classification." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41313.

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This paper introduces the system, a Python-based web platform developed for the secure management, processing, and intelligent analysis of medical images and video streams. The system leverages the great libraries and frameworks of Python, especially Flask, in building the backend.Through the emphasis on AI model integration of TensorFlow/PyTorch for better detection accuracy and precision.For the healthcare pro- fessionals.The report provides information about the architecture of the platform, the key features of the system, and how integrating AI transforms diagnostic workflows. Results indi

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Lal Din, Afza, Jehanzeb Khan, Iqra Javed, et al. "AI-Powered Nursing Assistants: Enhancing Efficiency and Reducing Workload in Healthcare." Journal of Neonatal Surgery 14, no. 7S (2025): 499–507. https://doi.org/10.63682/jns.v14i7s.2439.

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Background: The use of AI-powered nursing aides in the healthcare sector promises to improve efficiency, reduce staff workload, and increase patient outcomes. Still, how healthcare workers are utilizing and benefiting from AI solutions remains an area of study. This research seeks to understand the effectiveness, perceptions, and challenges of AI-powered nursing aides in the clinical context. Methods: This study utilized a quantitative approach in the form of a descriptive cross-sectional survey targeting 250 healthcare workers, including doctors, nurses, and hospital managers. A pretested str

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Hou, Wenzhuo. "Artificial Intelligence Algorithms and Applications in Medical Robotics." Theoretical and Natural Science 113, no. 1 (2025): 41–48. https://doi.org/10.54254/2753-8818/2025.au24056.

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AI has changed the modern healthcare landscape, especially its use in medical robots. The paper explores the core AI algorithms (machine learning, deep learning, reinforcement learning, computer vision, and natural language processing). It applies to medical robotics, including surgical assistance, diagnostics, rehabilitation, disaster response, and telepresence. Although these technologies have many advantages, like precision, speed, and better patient outcomes, there are still issues of data privacy, ethical queries, and disparities in access. The study goes on to identify limitations, techn

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