Artificial Intelligence In Clinical Practice Research Articles

Optometrist's perspectives of Artificial Intelligence in eye care

PurposeThe application of artificial intelligence (AI) in diagnosing and managing ocular disease has gained popularity as research highlights the utilization of AI to improve personalized medicine and healthcare outcomes. The objective of this study is to describe current optometric perspectives of AI in eye care. MethodsMembers of the American Academy of Optometry were sent an electronic invitation to complete a 17-item survey. Survey items assessed perceived advantages and concerns regarding AI using a 5-point Likert scale ranging from “strongly agree” to “strongly disagree.” ResultsA total of 400 optometrists completed the survey. The mean number of years since optometry school completion was 25 ± 15.1. Most respondents reported familiarity with AI (66.8%). Though half of optometrists had concerns about the diagnostic accuracy of AI (53.0%), most believed it would improve the practice of optometry (72.0%). Optometrists reported their willingness to incorporate AI into practice increased from 53.3% before the COVID-19 pandemic to 65.5% after onset of the pandemic (p<0.001). ConclusionIn this study, optometrists are optimistic about the use of AI in eye care, and willingness to incorporate AI in clinical practice also increased after the onset of the COVID-19 pandemic.

Application and observation of artificial intelligence in clinical practice of fundus screening for diabetic retinopathy with non-mydriatic fundus photography: a retrospective observational study of T2DM patients in Tianjin, China.

Objective:To observe the consistency of a preliminary report of artificial intelligence (AI) in the clinical practice of fundus screening for diabetic retinopathy (DR) using non-mydriatic fundus photography.Methods:Patients who underwent DR screening in the Metabolic Disease Management Center (MMC) of our hospital were selected as research participants. The degree of coincidence of the AI preliminary report and the ophthalmic diagnosis was compared and analyzed, and the kappa value was calculated. Fundus fluorescein angiography (FFA) was performed in patients referred to the out-of-hospital ophthalmology department, and the consistency between fluorescein angiography and AI diagnosis was evaluated.Results:In total, 6146 patients (12,263 eyes) completed the non-mydriasis fundus examination. The positive DR screening rate was 24.3%. When considering moderate nonproliferative retinopathy as the cut-off point, the kappa coefficient was 0.75 (p < 0.001), the sensitivity was 0.973, and the precision was 0.642, which was shown in the precision–recall curve. Fifty-nine patients referred to receive FFA were compared with non-mydriatic AI diagnoses. The kappa coefficient was 0.53, and the coincidence rate was 66.9%.Conclusion:Non-mydriasis fundus examination combined with AI has a medium-high consistency with ophthalmologists in DR diagnosis, conducive to early DR screening. Combining diagnosis and treatment modes with the Internet can promote the development of telemedicine, alleviate the shortage of ophthalmology resources, and promote the process of blindness prevention and treatment projects.

Trust in AI: why we should be designing for APPROPRIATE reliance.

Use of artificial intelligence in healthcare, such as machine learning-based predictive algorithms, holds promise for advancing outcomes, but few systems are used in routine clinical practice. Trust has been cited as an important challenge to meaningful use of artificial intelligence in clinical practice. Artificial intelligence systems often involve automating cognitively challenging tasks. Therefore, previous literature on trust in automation may hold important lessons for artificial intelligence applications in healthcare. In this perspective, we argue that informatics should take lessons from literature on trust in automation such that the goal should be to foster appropriate trust in artificial intelligence based on the purpose of the tool, its process for making recommendations, and its performance in the given context. We adapt a conceptual model to support this argument and present recommendations for future work.

Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy

ObjectiveTo examine the accuracy of artificial intelligence (AI) for the detection of breast cancer in mammography screening practice.DesignSystematic review of test accuracy studies.Data sourcesMedline, Embase, Web of Science, and Cochrane...

Clinical Validation Is the Key to Adopting AI in Clinical Practice.

Clinical Validation Is the Key to Adopting AI in Clinical Practice.

An international survey on AI in radiology in 1041 radiologists and radiology residents part 2: expectations, hurdles to implementation, and education

ObjectivesCurrently, hurdles to implementation of artificial intelligence (AI) in radiology are a much-debated topic but have not been investigated in the community at large. Also, controversy exists if and to what extent AI should be incorporated into radiology residency programs.MethodsBetween April and July 2019, an international survey took place on AI regarding its impact on the profession and training. The survey was accessible for radiologists and residents and distributed through several radiological societies. Relationships of independent variables with opinions, hurdles, and education were assessed using multivariable logistic regression.ResultsThe survey was completed by 1041 respondents from 54 countries. A majority (n = 855, 82%) expects that AI will cause a change to the radiology field within 10 years. Most frequently, expected roles of AI in clinical practice were second reader (n = 829, 78%) and work-flow optimization (n = 802, 77%). Ethical and legal issues (n = 630, 62%) and lack of knowledge (n = 584, 57%) were mentioned most often as hurdles to implementation. Expert respondents added lack of labelled images and generalizability issues. A majority (n = 819, 79%) indicated that AI should be incorporated in residency programs, while less support for imaging informatics and AI as a subspecialty was found (n = 241, 23%).ConclusionsBroad community demand exists for incorporation of AI into residency programs. Based on the results of the current study, integration of AI education seems advisable for radiology residents, including issues related to data management, ethics, and legislation.Key Points• There is broad demand from the radiological community to incorporate AI into residency programs, but there is less support to recognize imaging informatics as a radiological subspecialty.• Ethical and legal issues and lack of knowledge are recognized as major bottlenecks for AI implementation by the radiological community, while the shortage in labeled data and IT-infrastructure issues are less often recognized as hurdles.• Integrating AI education in radiology curricula including technical aspects of data management, risk of bias, and ethical and legal issues may aid successful integration of AI into diagnostic radiology.

Applications of artificial intelligence in prostate cancer imaging.

The purpose of this review was to identify the most recent lines of research focusing on the application of artificial intelligence (AI) in the diagnosis and staging of prostate cancer (PCa) with imaging. The majority of studies focused on the improvement in the interpretation of bi-parametric and multiparametric magnetic resonance imaging, and in the planning of image guided biopsy. These initial studies showed that AI methods based on convolutional neural networks could achieve a diagnostic performance close to that of radiologists. In addition, these methods could improve segmentation and reduce inter-reader variability. Methods based on both clinical and imaging findings could help in the identification of high-grade PCa and more aggressive disease, thus guiding treatment decisions. Though these initial results are promising, only few studies addressed the repeatability and reproducibility of the investigated AI tools. Further, large-scale validation studies are missing and no diagnostic phase III or higher studies proving improved outcomes regarding clinical decision making have been conducted. AI techniques have the potential to significantly improve and simplify diagnosis, risk stratification and staging of PCa. Larger studies with a focus on quality standards are needed to allow a widespread introduction of AI in clinical practice.

2020 ACR Data Science Institute Artificial Intelligence Survey.

The ACR Data Science Institute conducted its first annual survey of ACR members to understand how radiologists are using artificial intelligence (AI) in clinical practice and to provide a baseline for monitoring trends in AI use over time. The ACR Data Science Institute sent a brief electronic survey to all ACR members via email. Invitees were asked for demographic information about their practice and if and how they were currently using AI as part of their clinical work. They were also asked to evaluate the performance of AI models in their practices and to assess future needs. Approximately 30% of radiologists are currently using AI as part of their practice. Large practices were more likely to use AI than smaller ones, and of those using AI in clinical practice, most were using AI to enhance interpretation, most commonly detection of intracranial hemorrhage, pulmonary emboli, and mammographic abnormalities. Of practices not currently using AI, 20% plan to purchase AI tools in the next 1 to 5 years. The survey results indicate a modest penetrance of AI in clinical practice. Information from the survey will help researchers and industry develop AI tools that will enhance radiological practice and improve quality and efficiency in patient care.

An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude

ObjectivesRadiologists’ perception is likely to influence the adoption of artificial intelligence (AI) into clinical practice. We investigated knowledge and attitude towards AI by radiologists and residents in Europe and beyond.MethodsBetween April and July 2019, a survey on fear of replacement, knowledge, and attitude towards AI was accessible to radiologists and residents. The survey was distributed through several radiological societies, author networks, and social media. Independent predictors of fear of replacement and a positive attitude towards AI were assessed using multivariable logistic regression.ResultsThe survey was completed by 1,041 respondents from 54 mostly European countries. Most respondents were male (n = 670, 65%), median age was 38 (24–74) years, n = 142 (35%) residents, and n = 471 (45%) worked in an academic center. Basic AI-specific knowledge was associated with fear (adjusted OR 1.56, 95% CI 1.10–2.21, p = 0.01), while intermediate AI-specific knowledge (adjusted OR 0.40, 95% CI 0.20–0.80, p = 0.01) or advanced AI-specific knowledge (adjusted OR 0.43, 95% CI 0.21–0.90, p = 0.03) was inversely associated with fear. A positive attitude towards AI was observed in 48% (n = 501) and was associated with only having heard of AI, intermediate (adjusted OR 11.65, 95% CI 4.25–31.92, p < 0.001), or advanced AI-specific knowledge (adjusted OR 17.65, 95% CI 6.16–50.54, p < 0.001).ConclusionsLimited AI-specific knowledge levels among radiology residents and radiologists are associated with fear, while intermediate to advanced AI-specific knowledge levels are associated with a positive attitude towards AI. Additional training may therefore improve clinical adoption.Key Points• Forty-eight percent of radiologists and residents have an open and proactive attitude towards artificial intelligence (AI), while 38% fear of replacement by AI.• Intermediate and advanced AI-specific knowledge levels may enhance adoption of AI in clinical practice, while rudimentary knowledge levels appear to be inhibitive.• AI should be incorporated in radiology training curricula to help facilitate its clinical adoption.

Artificial intelligence in advanced pharmacy

Artificial intelligence (AI) is Pharma's next frontier in life sciences. This article analyze about the recently techniques of AI that aims to imitate human intelligence functions i.e. with the help of Artificial intelligence &amp; Robots ‘Automation become the result of Industrialization’, driven by the need to increase productivity, to achieve consistent quality products &amp; to remove hazardous and heavy work from workers. Recent trends of AI in pharmacy are PAT, CFD, Pharmaceutical automation in research &amp; development which give detailed information about techniques that has already been used in healthcare such as inhaler designs, drug absorption &amp; dissolution and is disease focused. A robot for pharmaceutical applications has a bright future but with the rapidly aging population that urgently requires sophisticated medical devices &amp; newer drugs, robotics systems are increasingly adopted for improved productivity and efficiency to meet this growing demand. However, robots manufacturers face several challenges in their effort to establish themselves in pharmaceutical applications. AI with robotics in the life of mankind has several advantages &amp; disadvantages. Despite the increasingly rich AI literature from the drug discovery to care options AI techniques are used such as in ANN [artificial neural network], machine learning, AI in healthcare, AI in clinical practice. This research mainly concentrates around a few disease types: Cancer, Nervous system and cardiovascular diseases as they are life threatening. The future is always hard to predict, but it will be determined by AI as it would become the next frontier in pharmacy.

Artificial Intelligence (AI) in Pathology – A Summary and Challenges

This bibliographic study covers Artificial Intelligence (AI)theory and its applications from the healthcare field and in particular from the discipline of pathology. This review includes basics of AI, supervised and unsupervised machine learning (ML), various supervised ML algorithms, and their applications in healthcare and pathology. Digital Pathology with Deep Machine Learning is more advantageous over traditional pathology that is based on ‘physical slide on a physical microscope’. However, various implementation challenges of cost, data quality, multi-center validation, bias, and regulatory approval issues for AI in clinical practice still remain, which are also described in this study.

How does the radiology community discuss the benefits and limitations of artificial intelligence for their work? A systematic discourse analysis

PurposeWe aimed to systematically analyse how the radiology community discusses the concept of artificial intelligence (AI), perceives its benefits, and reflects on its limitations. MethodsWe conducted a qualitative, systematic discourse analysis on 200 social-media posts collected over a period of five months (April–August 2020). ResultsThe discourse on AI is active, albeit often referring to AI as an umbrella term and lacking precision on the context (e.g. research, clinical) and the temporal focus (e.g. current AI, future AI). The discourse is also somewhat split between optimism and pessimism. The latter considers a wider range of social, ethical and legal factors than the former, which tends to focus on concrete technologies and their functionalities. ConclusionsFurther precision in the discourse could lead to more constructive conversations around AI. The split between optimism and pessimism calls for a constant exchange and synthesis between the two perspectives. Practical conversations (e.g. business models) remain rare, but may be crucial for an effective implementation of AI in clinical practice.

Medical Machines: The Expanding Role of Ethics in Technology-Driven Healthcare

Emerging technologies such as artificial intelligence are actively revolutionizing the healthcare industry. While there is widespread concern that these advances will displace human practitioners within the healthcare sector, there are several tasks – including original and nuanced ethical decision making – that they cannot replace. Further, the implementation of artificial intelligence in clinical practice can be anticipated to drive the production of novel ethical tensions surrounding its use, even while eliminating some of the technical tasks which currently compete with ethical deliberation for clinicians’ limited time. A new argument therefore arises to suggest that although these disruptive technologies will change the face of medicine, they may also foster a revival of several fundamental components inherent to the role of healthcare professionals, chiefly, the principal activities of moral philosophy. Accordingly, “machine medicine” presents a vital opportunity to reinvigorate the field of bioethics, rather than withdraw from it.

Evolution of Clinical Medicine: From Expert Opinion to Artificial Intelligence

Artificial intelligence provides a vast opportunity and conquest of the science of knowledge. Twenty-first-century medicine will be characterized by an extraordinary ability to access and process medical information to provide patient-specific, timely, and effective clinical decision support. The knowledge gained by patient care experience and clinicians' expertise has led to many clinical care advances. Access to a large volume of data, along with ever-growing information and knowledge of diseases, can allow us to optimize diagnoses and management strategies by using advances in machine learning and artificial intelligence. Changing the medical culture from only relying on the experts to use medical informatics advances to improve the experts' clinical judgment would be an uphill battle. It is necessary to overcome the clinicians' traditional training to empower them into moving in the data science, statistics, and artificial intelligence era. As the incorporation of artificial intelligence in clinical practice seems inevitable, a thorough understanding of its capacities and flaws is essential to the emergence of a new clinical practice world. This review paper describes some of the nuances of past, current, and future clinical decision support systems and artificial intelligence's impact on this process.

Classification of femur fracture in pelvic X-ray images using meta-learned deep neural network

In the medical field, various studies using artificial intelligence (AI) techniques have been attempted. Numerous attempts have been made to diagnose and classify diseases using image data. However, different forms of fracture exist, and inaccurate results have been confirmed depending on condition at the time of imaging, which is problematic. To overcome this limitation, we present an encoder-decoder structured neural network that utilizes radiology reports as ancillary information at training. This is a type of meta-learning method used to generate sufficiently adequate features for classification. The proposed model learns representation for classification from X-ray images and radiology reports simultaneously. When using a dataset of only 459 cases for algorithm training, the model achieved a favorable performance in a test dataset containing 227 cases (classification accuracy of 86.78% and classification F1 score of 0.867 for fracture or normal classification). This finding demonstrates the potential for deep learning to improve performance and accelerate application of AI in clinical practice.

Automatic detection of perforators for microsurgical reconstruction

The deep inferior epigastric perforator (DIEP) is the most commonly used free flap in mastectomy reconstruction.Preoperative imaging techniques are routinely used to detect location, diameter and course of perforators, with direct intervention from the imaging team, who subsequently draw a chart that will help surgeons choosing the best vascular support for the reconstruction.In this work, the feasibility of using a computer software to support the preoperative planning of 40 patients proposed for breast reconstruction with a DIEP flap is evaluated for the first time. Blood vessel centreline extraction and local characterization algorithms are applied to identify perforators and compared with the manual mapping, aiming to reduce the time spent by the imaging team, as well as the inherent subjectivity to the task.Comparing with the measures taken during surgery, the software calibre estimates were worse for vessels smaller than 1.5 mm (P = 6e-4) but better for the remaining ones (P = 2e-3). Regarding vessel location, the vertical component of the software output was significantly different from the manual measure (P = 0.02), nonetheless that was irrelevant during surgery as errors in the order of 2–3 mm do not have impact in the dissection step.Our trials support that a reduction of the time spent is achievable using the automatic tool (about 2 h/case).The introduction of artificial intelligence in clinical practice intends to simplify the work of health professionals and to provide better outcomes to patients. This pilot study paves the way for a success story.

Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement

This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine. AI has great potential to increase efficiency and accuracy throughout radiology, but it also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice. This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future. The radiology community should start now to develop codes of ethics and practice for AI that promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes.

Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement

This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine. AI has great potential to increase efficiency and accuracy throughout radiology, but it also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice. This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future. The radiology community should start now to develop codes of ethics and practice for AI that promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes.

Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement.

This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine. AI has great potential to increase efficiency and accuracy throughout radiology, but it also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice. This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future. The radiology community should start now to develop codes of ethics and practice for AI that promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes. This article is a simultaneous joint publication in Radiology, Journal of the American College of Radiology, Canadian Association of Radiologists Journal, and Insights into Imaging. Published under a CC BY-NC-ND 4.0 license. Online supplemental material is available for this article.

Artificial Intelligence: Practical Primer for Clinical Research in Cardiovascular Disease.

Artificial intelligence (AI) has begun to permeate and reform the field of medicine and cardiovascular medicine. Impacting about 100 million patients in the United States, the burden of cardiovascular disease is felt in a diverse array of demographics.1, 2 Meanwhile, routine mediums such as multimodality images, electronic health records (EHR), and mobile health devices store troves of underutilized data for each patient. AI has the potential to improve and influence the status quo, with capacity to learn from these massive data and apply knowledge from them to distinct circumstances.3, 4 With considerable information in each heart beat, cardiovascular medicine will definitely be one of the fields that embrace AI to move toward personalized and precise care.5 AI has already been woven into the fabric of everyday life. From an internet search engine, email spam and malware filtering, to uncovering fraudulent credit card purchases, AI addresses an individual's needs in the realms of business, entertainment, and technology. Unfortunately, medicine, including cardiology, has not fully embraced this revolution, with only a limited number of AI‐based clinical applications being available. Nevertheless, there is promise towards routine implementation; machine learning and deep learning have seen an exponential surge of cardiovascular publications in the past decade.6, 7 These methods have proven beneficial in a variety of complex areas including echocardiogram interpretation and diastolic dysfunction grade stratification.8, 9 The US Food and Drug Administration has already approved several devices that utilize AI features.10 Imagine coming to work finding that your system has analyzed all your patients while you were sleeping: their laboratory data, imaging results, symptoms, and mobile device data to calculate their risk of cardiovascular events, death, hospitalization, whether medications should be adjusted/added/removed, or whether they should be referred for an examination. The system presents you with the reasons for its recommendations, and you are confident that they are as good as those given by the most experienced physicians. This may allow you to spend time in shared decision making with your patients, both objectively and compassionately. Although there are currently several barriers/challenges to adoption of AI in clinical practice, undoubtedly, AI will drive current healthcare practice towards a more individualized and precision‐based approach over the next several years. Therefore, general understanding of AI techniques by clinicians and researchers in cardiovascular medicine is paramount. In this review article, we describe the fundamentals of AI that clinicians and researchers should understand, its definition and principles, how to interpret and apply AI in cardiovascular research, limitations, and future perspectives.