Revolutionizing healthcare: the role of artificial intelligence in clinical practice

Dhoha Al Muhanna Fahad A Al-Muhanna Ahmed Al Kuwaiti + 5 lainnya

1 Juni 2023

Artificial intelligence (AI) applications have transformed healthcare. This study is based on a general literature review uncovering the role of AI in healthcare and focuses on the following key aspects: (i) medical imaging and diagnostics, (ii) virtual patient care, (iii) medical research and drug discovery, (iv) patient engagement and compliance, (v) rehabilitation, and (vi) other administrative applications. The impact of AI is observed in detecting clinical conditions in medical imaging and diagnostic services, controlling the outbreak of coronavirus disease 2019 (COVID-19) with early diagnosis, providing virtual patient care using AI-powered tools, managing electronic health records, augmenting patient engagement and compliance with the treatment plan, reducing the administrative workload of healthcare professionals (HCPs), discovering new drugs and vaccines, spotting medical prescription errors, extensive data storage and analysis, and technology-assisted rehabilitation. Nevertheless, this science pitch meets several technical, ethical, and social challenges, including privacy, safety, the right to decide and try, costs, information and consent, access, and efficacy, while integrating AI into healthcare. The governance of AI applications is crucial for patient safety and accountability and for raising HCPs’ belief in enhancing acceptance and boosting significant health consequences. Effective governance is a prerequisite to precisely address regulatory, ethical, and trust issues while advancing the acceptance and implementation of AI. Since COVID-19 hit the global health system, the concept of AI has created a revolution in healthcare, and such an uprising could be another step forward to meet future healthcare needs.

Jayashree Mayuren Priyanka Banerjee Mayuren Candasamy + 3 lainnya

11 Januari 2023

Artificial intelligence (AI) is a branch of computer science that allows machines to work efficiently, can analyze complex data. The research focused on AI has increased tremendously, and its role in healthcare service and research is emerging at a greater pace. This review elaborates on the opportunities and challenges of AI in healthcare and pharmaceutical research. The literature was collected from domains such as PubMed, Science Direct and Google scholar using specific keywords and phrases such as ‘Artificial intelligence’, ‘Pharmaceutical research’, ‘drug discovery’, ‘clinical trial’, ‘disease diagnosis’, etc. to select the research and review articles published within the last five years. The application of AI in disease diagnosis, digital therapy, personalized treatment, drug discovery and forecasting epidemics or pandemics was extensively reviewed in this article. Deep learning and neural networks are the most used AI technologies; Bayesian nonparametric models are the potential technologies for clinical trial design; natural language processing and wearable devices are used in patient identification and clinical trial monitoring. Deep learning and neural networks were applied in predicting the outbreak of seasonal influenza, Zika, Ebola, Tuberculosis and COVID-19. With the advancement of AI technologies, the scientific community may witness rapid and cost-effective healthcare and pharmaceutical research as well as provide improved service to the general public.

Andrew Cupples

1 September 2023

Artificial Intelligence in Medicine is looking for novelty in the methodological and/or theoretical content of submitted papers. Such kind of novelty has to be mainly acknowledged in the area of AI and Computer Science. Methodological papers deal with the proposal of some strategy and related methods to solve some scientific issues in specific domains. They must show, usually through an experimental evaluation, how the proposed methodology can be applied to medicine, medicallyoriented human biology, and health care, respectively. They have also to provide a comparison with other proposals, and explicitly discuss elements of novelty. Theoretical papers focus on more fundamental, general and formal topics of AI and must show the novel expected effects of the proposed solution in some medical or healthcare field.

Tahereh Mahmoudi Alireza Mehdizadeh

1 Desember 2022

Received: 27 November 2022 Accepted: 28 November 2022 Artificial intelligence (AI) as a branch of computer science focuses on creating intelligent computers to do activities, which generally require human intelligence. AI is considered in many aspects of the routine day, including personal assistants, aircraft, video games, and automated public transit. Moreover, AI is recently considered mostly in healthcare and medicine: disease diagnosis, referral management, service delivery, drug development, predictive modeling, and decision support, drastically leading to altering some processes of patient care [1-5]. The number of manuscripts related to AI in Medicine (AIM) submitted to the Journal of Biomedical Physics and Engineering (JBPE) has been significantly increasing since the past years, resulting in assigning this issue only to selected AIM manuscripts as a special opportunity for AIM researchers. This special issue aimed to show the most recent advances and innovative uses of AI in the medical field. Therefore, scholars and researchers are invited to submit their original research articles based on AI techniques to address health challenges. Furthermore, some of the articles were selected from the manuscripts presented at the first international congress on “Advanced Health Technologies-Artificial Intelligence in Medicine” [6], leading to the selection of 11 papers that cover a wide range of AIM: diagnosis, prediction, classification, and cancer detection using AI techniques, such as machine learning, neural network, and deep learning. This study also aimed to provide historical perspective and an up-to-date review of AIM publications. This review briefly discusses the number of publications, research trends, and hot medical topics related to AIM. From the WOS and PubMed databases, Papers were selected based on keywords like 1) AI type, 2) medicine/health from 1971 to November 25th 2022. The conference proceedings, abstract meetings, book chapters, and articles published in languages other than English were excluded. The AI types were set as “Artificial intelligence”, “Deep learning”, “Machine learning”, “Neural network”, “Artificial neural network”, “Natural language processing”, “Fuzzy expert system” and “Robotics”. Meanwhile, the search terms “medicine”, “health*”, “diagnosis”, “treatment”, “diseases” and “illness” were considered the medicine/health items in the research. Finally, these two queries were connected with “AND” together. Figure 1 shows the growth of some publications in WOS and PubMed, which is a significant increase in AIM publications since 2018. Figure 2 demonstrates the different hot-topic medical fields, using AI approaches, and surgery first ranks in AI usage. The number of published papers in different branches of medical sciences is compared from 2019 to 2022 (Figure 2) to illustrate the explosive growth of published papers in the last four years.

T. Tsuboyama Y. Fushimi T. Nakaura + 15 lainnya

4 Agustus 2023

In this review, we address the issue of fairness in the clinical integration of artificial intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a subfield of AI, progresses, concerns have arisen regarding the impact of AI biases and discrimination on patient health. This review aims to provide a comprehensive overview of concerns associated with AI fairness; discuss strategies to mitigate AI biases; and emphasize the need for cooperation among physicians, AI researchers, AI developers, policymakers, and patients to ensure equitable AI integration. First, we define and introduce the concept of fairness in AI applications in healthcare and radiology, emphasizing the benefits and challenges of incorporating AI into clinical practice. Next, we delve into concerns regarding fairness in healthcare, addressing the various causes of biases in AI and potential concerns such as misdiagnosis, unequal access to treatment, and ethical considerations. We then outline strategies for addressing fairness, such as the importance of diverse and representative data and algorithm audits. Additionally, we discuss ethical and legal considerations such as data privacy, responsibility, accountability, transparency, and explainability in AI. Finally, we present the Fairness of Artificial Intelligence Recommendations in healthcare (FAIR) statement to offer best practices. Through these efforts, we aim to provide a foundation for discussing the responsible and equitable implementation and deployment of AI in healthcare.