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Yearbook of Medical Informatics Aug 2019Artificial intelligence (AI) is heralded as an approach that might augment or substitute for the limited processing power of the human brain of primary health care (PHC)...
BACKGROUND
Artificial intelligence (AI) is heralded as an approach that might augment or substitute for the limited processing power of the human brain of primary health care (PHC) professionals. However, there are concerns that AI-mediated decisions may be hard to validate and challenge, or may result in rogue decisions.
OBJECTIVE
To form consensus about perceptions, issues, and challenges of AI in primary care.
METHOD
A three-round Delphi study was conducted. Round 1 explored experts' viewpoints on AI in PHC (n=20). Round 2 rated the appropriateness of statements arising from round one (n=12). The third round was an online panel discussion of findings (n=8) with the members of both the International Medical Informatics Association and the European Federation of Medical Informatics Primary Health Care Informatics Working Groups.
RESULTS
PHC and informatics experts reported AI has potential to improve managerial and clinical decisions and processes, and this would be facilitated by common data standards. The respondents did not agree that AI applications should learn and adapt to clinician preferences or behaviour and they did not agree on the extent of AI potential for harm to patients. It was more difficult to assess the impact of AI-based applications on continuity and coordination of care.
CONCLUSION
While the use of AI in medicine should enhance healthcare delivery, we need to ensure meticulous design and evaluation of AI applications. The primary care informatics community needs to be proactive and to guide the ethical and rigorous development of AI applications so that they will be safe and effective.
Topics: Artificial Intelligence; Delphi Technique; Learning Health System; Primary Health Care
PubMed: 31022751
DOI: 10.1055/s-0039-1677901 -
Indian Journal of Cancer 2021Artificial intelligence (AI) has found its way into every sphere of human life including the field of medicine. Detection of cancer might be AI's most altruistic and... (Review)
Review
Artificial intelligence (AI) has found its way into every sphere of human life including the field of medicine. Detection of cancer might be AI's most altruistic and convoluted challenge to date in the field of medicine. Embedding AI into various aspects of cancer diagnostics would be of immense use in dealing with the tedious, repetitive, time-consuming job of lesion detection, remove opportunities for human error, and cut costs and time. This would be of great value in cancer screening programs. By using AI algorithms, data from digital images from radiology and pathology that are imperceptible to the human eye can be identified (radiomics and pathomics). Correlating radiomics and pathomics with clinico-demographic-therapy-morbidity-mortality profiles will lead to a greater understanding of cancers. Specific imaging phenotypes have been found to be associated with specific gene-determined molecular pathways involved in cancer pathogenesis (radiogenomics). All these developments would not only help to personalize oncologic practice but also lead to the development of new imaging biomarkers. AI algorithms in oncoimaging and oncopathology will broadly have the following uses: cancer screening (detection of lesions), characterization and grading of tumors, and clinical decision-making and prognostication. However, AI cannot be a foolproof panacea nor can it supplant the role of humans. It can however be a powerful and useful complement to human insights and deeper understanding. Multiple issues like standardization, validity, ethics, privacy, finances, legal liability, training, accreditation, etc., need to be overcome before the vast potential of AI in diagnostic oncology can be fully harnessed.
Topics: Artificial Intelligence; Deep Learning; Humans; Machine Learning; Neoplasms
PubMed: 34975094
DOI: 10.4103/ijc.IJC_399_20 -
Yearbook of Medical Informatics Aug 2019This paper explores the implications of artificial intelligence (AI) on the management of healthcare data and information and how AI technologies will affect the... (Review)
Review
OBJECTIVE
This paper explores the implications of artificial intelligence (AI) on the management of healthcare data and information and how AI technologies will affect the responsibilities and work of health information management (HIM) professionals.
METHODS
A literature review was conducted of both peer-reviewed literature and published opinions on current and future use of AI technology to collect, store, and use healthcare data. The authors also sought insights from key HIM leaders via semi-structured interviews conducted both on the phone and by email.
RESULTS
The following HIM practices are impacted by AI technologies: 1) Automated medical coding and capturing AI-based information; 2) Healthcare data management and data governance; 3) Fbtient privacy and confidentiality; and 4) HIM workforce training and education.
DISCUSSION
HIM professionals must focus on improving the quality of coded data that is being used to develop AI applications. HIM professional's ability to identify data patterns will be an important skill as automation advances, though additional skills in data analysis tools and techniques are needed. In addition, HIM professionals should consider how current patient privacy practices apply to AI application, development, and use.
CONCLUSIONS
AI technology will continue to evolve as will the role of HIM professionals who are in a unique position to take on emerging roles with their depth of knowledge on the sources and origins of healthcare data. The challenge for HIM professionals is to identify leading practices for the management of healthcare data and information in an AI-enabled world.
Topics: Artificial Intelligence; Delivery of Health Care; Health Information Management; Health Workforce; Medical Informatics; Professional Role
PubMed: 31419816
DOI: 10.1055/s-0039-1677913 -
Cancer Communications (London, England) Apr 2020The development of digital pathology and progression of state-of-the-art algorithms for computer vision have led to increasing interest in the use of artificial... (Review)
Review
The development of digital pathology and progression of state-of-the-art algorithms for computer vision have led to increasing interest in the use of artificial intelligence (AI), especially deep learning (DL)-based AI, in tumor pathology. The DL-based algorithms have been developed to conduct all kinds of work involved in tumor pathology, including tumor diagnosis, subtyping, grading, staging, and prognostic prediction, as well as the identification of pathological features, biomarkers and genetic changes. The applications of AI in pathology not only contribute to improve diagnostic accuracy and objectivity but also reduce the workload of pathologists and subsequently enable them to spend additional time on high-level decision-making tasks. In addition, AI is useful for pathologists to meet the requirements of precision oncology. However, there are still some challenges relating to the implementation of AI, including the issues of algorithm validation and interpretability, computing systems, the unbelieving attitude of pathologists, clinicians and patients, as well as regulators and reimbursements. Herein, we present an overview on how AI-based approaches could be integrated into the workflow of pathologists and discuss the challenges and perspectives of the implementation of AI in tumor pathology.
Topics: Artificial Intelligence; Deep Learning; Humans; Neoplasms; Precision Medicine
PubMed: 32277744
DOI: 10.1002/cac2.12012 -
International Journal of Molecular... Feb 2020A growing body of evidence now suggests that precision psychiatry, an interdisciplinary field of psychiatry, precision medicine, and pharmacogenomics, serves as an... (Review)
Review
A growing body of evidence now suggests that precision psychiatry, an interdisciplinary field of psychiatry, precision medicine, and pharmacogenomics, serves as an indispensable foundation of medical practices by offering the accurate medication with the accurate dose at the accurate time to patients with psychiatric disorders. In light of the latest advancements in artificial intelligence and machine learning techniques, numerous biomarkers and genetic loci associated with psychiatric diseases and relevant treatments are being discovered in precision psychiatry research by employing neuroimaging and multi-omics. In this review, we focus on the latest developments for precision psychiatry research using artificial intelligence and machine learning approaches, such as deep learning and neural network algorithms, together with multi-omics and neuroimaging data. Firstly, we review precision psychiatry and pharmacogenomics studies that leverage various artificial intelligence and machine learning techniques to assess treatment prediction, prognosis prediction, diagnosis prediction, and the detection of potential biomarkers. In addition, we describe potential biomarkers and genetic loci that have been discovered to be associated with psychiatric diseases and relevant treatments. Moreover, we outline the limitations in regard to the previous precision psychiatry and pharmacogenomics studies. Finally, we present a discussion of directions and challenges for future research.
Topics: Algorithms; Artificial Intelligence; Humans; Interdisciplinary Studies; Machine Learning; Mental Disorders; Neural Networks, Computer; Pharmacogenetics; Precision Medicine
PubMed: 32024055
DOI: 10.3390/ijms21030969 -
European Journal of Pharmaceutical... Feb 2023Computer-aided drug design (CADD) is an emerging field that has drawn a lot of interest because of its potential to expedite and lower the cost of the drug development... (Review)
Review
Computer-aided drug design (CADD) is an emerging field that has drawn a lot of interest because of its potential to expedite and lower the cost of the drug development process. Drug discovery research is expensive and time-consuming, and it frequently took 10-15 years for a drug to be commercially available. CADD has significantly impacted this area of research. Further, the combination of CADD with Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies to handle enormous amounts of biological data has reduced the time and cost associated with the drug development process. This review will discuss how CADD, AI, ML, and DL approaches help identify drug candidates and various other steps of the drug discovery process. It will also provide a detailed overview of the different in silico tools used and how these approaches interact.
Topics: Artificial Intelligence; Drug Design; Drug Discovery; Machine Learning
PubMed: 36347444
DOI: 10.1016/j.ejps.2022.106324 -
The Journal of Infection Oct 2023Artificial intelligence (AI), machine learning and deep learning (including generative AI) are increasingly being investigated in the context of research and management... (Review)
Review
BACKGROUND
Artificial intelligence (AI), machine learning and deep learning (including generative AI) are increasingly being investigated in the context of research and management of human infection.
OBJECTIVES
We summarise recent and potential future applications of AI and its relevance to clinical infection practice.
METHODS
1617 PubMed results were screened, with priority given to clinical trials, systematic reviews and meta-analyses. This narrative review focusses on studies using prospectively collected real-world data with clinical validation, and on research with translational potential, such as novel drug discovery and microbiome-based interventions.
RESULTS
There is some evidence of clinical utility of AI applied to laboratory diagnostics (e.g. digital culture plate reading, malaria diagnosis, antimicrobial resistance profiling), clinical imaging analysis (e.g. pulmonary tuberculosis diagnosis), clinical decision support tools (e.g. sepsis prediction, antimicrobial prescribing) and public health outbreak management (e.g. COVID-19). Most studies to date lack any real-world validation or clinical utility metrics. Significant heterogeneity in study design and reporting limits comparability. Many practical and ethical issues exist, including algorithm transparency and risk of bias.
CONCLUSIONS
Interest in and development of AI-based tools for infection research and management are undoubtedly gaining pace, although the real-world clinical utility to date appears much more modest.
Topics: Humans; Artificial Intelligence; Deep Learning; COVID-19; Machine Learning; Algorithms
PubMed: 37468046
DOI: 10.1016/j.jinf.2023.07.006 -
Nursing & Health Sciences Sep 2023This article is a theoretical discourse about technological machines and artificial intelligence, highlighting their effective interactive outcomes in nursing. One...
This article is a theoretical discourse about technological machines and artificial intelligence, highlighting their effective interactive outcomes in nursing. One significant influence is technological efficiency which positively affects nursing care time, enabling nurses to focus more on their patients as the core of nursing. The article examines the impact of technology and artificial intelligence on nursing practice in this era of rapid technological advancements and technological dependence. Strategic opportunities in nursing are advanced, exemplified by robotics technology and artificial intelligence. A survey of recent literature focused on what is known about the influence of technology, healthcare robotics, and artificial intelligence on nursing in the contexts of industrialization, societal milieu, and human living environments. Efficient, precision-driven machines with artificial intelligence support a technology-centered society in which hospitals and healthcare systems become increasingly technology-dependent, impacting healthcare quality and patient care satisfaction. As a result, higher levels of knowledge, intelligence, and recognition of technologies and artificial intelligence are required for nurses to render quality nursing care. Designers of health facilities should be particularly aware of nursing's increasing dependence on technological advancements in their practice.
Topics: Humans; Artificial Intelligence; Robotics; Nursing Care; Technology; Nurses
PubMed: 37332058
DOI: 10.1111/nhs.13029 -
Dento Maxillo Facial Radiology Mar 2021Artificial intelligence, which has been actively applied in a broad range of industries in recent years, is an active area of interest for many researchers. Dentistry is...
Artificial intelligence, which has been actively applied in a broad range of industries in recent years, is an active area of interest for many researchers. Dentistry is no exception to this trend, and the applications of artificial intelligence are particularly promising in the field of oral and maxillofacial (OMF) radiology. Recent researches on artificial intelligence in OMF radiology have mainly used convolutional neural networks, which can perform image classification, detection, segmentation, registration, generation, and refinement. Artificial intelligence systems in this field have been developed for the purposes of radiographic diagnosis, image analysis, forensic dentistry, and image quality improvement. Tremendous amounts of data are needed to achieve good results, and involvement of OMF radiologist is essential for making accurate and consistent data sets, which is a time-consuming task. In order to widely use artificial intelligence in actual clinical practice in the future, there are lots of problems to be solved, such as building up a huge amount of fine-labeled open data set, understanding of the judgment criteria of artificial intelligence, and DICOM hacking threats using artificial intelligence. If solutions to these problems are presented with the development of artificial intelligence, artificial intelligence will develop further in the future and is expected to play an important role in the development of automatic diagnosis systems, the establishment of treatment plans, and the fabrication of treatment tools. OMF radiologists, as professionals who thoroughly understand the characteristics of radiographic images, will play a very important role in the development of artificial intelligence applications in this field.
Topics: Artificial Intelligence; Humans; Neural Networks, Computer; Radiography; Radiologists; Radiology
PubMed: 33197209
DOI: 10.1259/dmfr.20200375 -
Medical Image Analysis Jul 2022With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image... (Review)
Review
With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of explainable artificial intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.
Topics: Artificial Intelligence; Deep Learning; Humans
PubMed: 35576821
DOI: 10.1016/j.media.2022.102470