Applications of artificial intelligence in drug development using real-world data.
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ABSTRACT:
SUBMITTER: Chen Z
PROVIDER: S-EPMC8626864 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveTo conduct a systematic scoping review of explainable artificial intelligence (XAI) models that use real-world electronic health record data, categorize these techniques according to different biomedical applications, identify gaps of current studies, and suggest future research directions.Materials and methodsWe searched MEDLINE, IEEE Xplore, and the Association for Computing Machinery (ACM) Digital Library to identify relevant papers published between January 1, 2009 and May 1, 2019. We summarized these studies based on the year of publication, prediction tasks, machine learning algorithm, dataset(s) used to build the models, the scope, category, and evaluation of the XAI methods. We further assessed the reproducibility of the studies in terms of the availability of data and code and discussed open issues and challenges.ResultsForty-two articles were included in this review. We reported the research trend and most-studied diseases. We grouped XAI methods into 5 categories: knowledge distillation and rule extraction (N = 13), intrinsically interpretable models (N = 9), data dimensionality reduction (N = 8), attention mechanism (N = 7), and feature interaction and importance (N = 5).DiscussionXAI evaluation is an open issue that requires a deeper focus in the case of medical applications. We also discuss the importance of reproducibility of research work in this field, as well as the challenges and opportunities of XAI from 2 medical professionals' point of view.ConclusionBased on our review, we found that XAI evaluation in medicine has not been adequately and formally practiced. Reproducibility remains a critical concern. Ample opportunities exist to advance XAI research in medicine.
| S-EPMC7647281 | biostudies-literature
Project description:BackgroundDevelopment of artificial intelligence (AI)-based technologies in health care is proceeding rapidly. The sharing and release of real-world data are key practical issues surrounding the implementation of AI solutions into existing clinical practice. However, data derived from daily patient care are necessary for initial training, and continued data supply is needed for the ongoing training, validation, and improvement of AI-based solutions. Data may need to be shared across multiple institutions and settings for the widespread implementation and high-quality use of these solutions. To date, solutions have not been widely implemented in Germany to meet the challenge of providing a sufficient data volume for the development of AI-based technologies for research and third-party entities. The Protected Artificial Intelligence Innovation Environment for Patient-Oriented Digital Health Solutions (pAItient) project aims to meet this challenge by creating a large data pool that feeds on the donation of data derived from daily patient care. Prior to building this data pool, physician perspectives regarding data donation for AI-based solutions should be studied.ObjectiveThis study explores physician perspectives on providing and using real-world care data for the development of AI-based solutions in health care in Germany.MethodsAs a part of the requirements analysis preceding the pAItient project, this qualitative study explored physician perspectives and expectations regarding the use of data derived from daily patient care in AI-based solutions. Semistructured, guide-based, and problem-centered interviews were audiorecorded, deidentified, transcribed verbatim, and analyzed inductively in a thematically structured approach.ResultsInterviews (N=8) with a mean duration of 24 (SD 7.8) minutes were conducted with 6 general practitioners and 2 hospital-based physicians. The mean participant age was 54 (SD 14.1; range 30-74) years, with an average experience as a physician of 25 (SD 13.9; range 1-45) years. Self-rated affinity toward modern information technology varied from very high to low (5-point Likert scale: mean 3.75, SD 1.1). All participants reported they would support the development of AI-based solutions in research contexts by donating deidentified data derived from daily patient care if subsequent data use was made transparent to them and their patients and the benefits for patient care were clear. Contributing to care optimization and efficiency were cited as motivation for potential data donation. Concerns regarding workflow integration (time and effort), appropriate deidentification, and the involvement of third-party entities with economic interests were discussed. The donation of data in reference to psychosomatic treatment needs was viewed critically.ConclusionsThe interviewed physicians reported they would agree to use real-world care data to support the development of AI-based solutions with a clear benefit for daily patient care. Joint ventures with third-party entities were viewed critically and should focus on care optimization and patient benefits rather than financial interests.
| S-EPMC9161099 | biostudies-literature
Project description:BackgroundCoronary heart disease (CHD) has become the leading cause of death and one of the most serious epidemic diseases worldwide. CHD is characterized by urgency, danger and severity, and dynamic treatment strategies for CHD patients are needed. We aimed to build and validate an AI model for dynamic treatment recommendations for CHD patients with the goal of improving patient outcomes and learning best practices from clinicians to help clinical decision support for treating CHD patients.MethodsWe formed the treatment strategy as a sequential decision problem, and applied an AI supervised reinforcement learning-long short-term memory (SRL-LSTM) framework that combined supervised learning (SL) and reinforcement learning (RL) with an LSTM network to track patients' states to learn a recommendation model that took a patient's diagnosis and evolving health status as input and provided a treatment recommendation in the form of whether to take specific drugs. The experiments were conducted by leveraging a real-world intensive care unit (ICU) database with 13,762 admitted patients diagnosed with CHD. We compared the performance of the applied SRL-LSTM model and several state-of-the-art SL and RL models in reducing the estimated in-hospital mortality and the Jaccard similarity with clinicians' decisions. We used a random forest algorithm to calculate the feature importance of both the clinician policy and the AI policy to illustrate the interpretability of the AI model.ResultsOur experimental study demonstrated that the AI model could help reduce the estimated in-hospital mortality through its RL function and learn the best practice from clinicians through its SL function. The similarity between the clinician policy and the AI policy regarding the surviving patients was high, while for the expired patients, it was much lower. The dynamic treatment strategies made by the AI model were clinically interpretable and relied on sensible clinical features extracted according to monitoring indexes and risk factors for CHD patients.ConclusionsWe proposed a pipeline for constructing an AI model to learn dynamic treatment strategies for CHD patients that could improve patient outcomes and mimic the best practices of clinicians. And a lot of further studies and efforts are needed to make it practical.
| S-EPMC8845235 | biostudies-literature
Project description:BackgroundPsoriasis is one of the most frequent inflammatory skin conditions and could be treated via tele-dermatology, provided that the current lack of reliable tools for objective severity assessments is overcome. Psoriasis Area and Severity Index (PASI) has a prominent level of subjectivity and is rarely used in real practice, although it is the most widely accepted metric for measuring psoriasis severity currently.ObjectiveThis study aimed to develop an image-artificial intelligence (AI)-based validated system for severity assessment with the explicit intention of facilitating long-term management of patients with psoriasis.MethodsA deep learning system was trained to estimate the PASI score by using 14,096 images from 2367 patients with psoriasis. We used 1962 patients from January 2015 to April 2021 to train the model and the other 405 patients from May 2021 to July 2021 to validate it. A multiview feature enhancement block was designed to combine vision features from different perspectives to better simulate the visual diagnostic method in clinical practice. A classification header along with a regression header was simultaneously applied to generate PASI scores, and an extra cross-teacher header after these 2 headers was designed to revise their output. The mean average error (MAE) was used as the metric to evaluate the accuracy of the predicted PASI score. By making the model minimize the MAE value, the model becomes closer to the target value. Then, the proposed model was compared with 43 experienced dermatologists. Finally, the proposed model was deployed into an app named SkinTeller on the WeChat platform.ResultsThe proposed image-AI-based PASI-estimating model outperformed the average performance of 43 experienced dermatologists with a 33.2% performance gain in the overall PASI score. The model achieved the smallest MAE of 2.05 at 3 input images by the ablation experiment. In other words, for the task of psoriasis severity assessment, the severity score predicted by our model was close to the PASI score diagnosed by experienced dermatologists. The SkinTeller app has been used 3369 times for PASI scoring in 1497 patients from 18 hospitals, and its excellent performance was confirmed by a feedback survey of 43 dermatologist users.ConclusionsAn image-AI-based psoriasis severity assessment model has been proposed to automatically calculate PASI scores in an efficient, objective, and accurate manner. The SkinTeller app may be a promising alternative for dermatologists' accurate assessment in the real world and chronic disease self-management in patients with psoriasis.
| S-EPMC10131673 | biostudies-literature
Project description:Recent progress in digital health data recording, advances in computing power, and methodological approaches that extract information from data as artificial intelligence are expected to have a disruptive impact on technology in medicine. One of the potential benefits is the ability to extract new and essential insights from the vast amount of data generated during health care delivery every day. Cardiovascular imaging is boosted by new intelligent automatic methods to manage, process, segment, and analyze petabytes of image data exceeding historical manual capacities. Algorithms that learn from data raise new challenges for regulatory bodies. Partially autonomous behavior and adaptive modifications and a lack of transparency in deriving evidence from complex data pose considerable problems. Controlling new technologies requires new controlling techniques and ongoing regulatory research. All stakeholders must participate in the quest to find a fair balance between innovation and regulation. The regulatory approach to artificial intelligence must be risk-based and resilient. A focus on unknown emerging risks demands continuous surveillance and clinical evaluation during the total product life cycle. Since learning algorithms are data-driven, high-quality data is fundamental for good machine learning practice. Mining, processing, validation, governance, and data control must account for bias, error, inappropriate use, drifts, and shifts, particularly in real-world data. Regulators worldwide are tackling twenty-first century challenges raised by “learning” medical devices. Ethical concerns and regulatory approaches are presented. The paper concludes with a discussion on the future of responsible artificial intelligence.
| S-EPMC9354141 | biostudies-literature
Project description:BackgroundArtificial intelligence (AI) applications are growing at an unprecedented pace in health care, including disease diagnosis, triage or screening, risk analysis, surgical operations, and so forth. Despite a great deal of research in the development and validation of health care AI, only few applications have been actually implemented at the frontlines of clinical practice.ObjectiveThe objective of this study was to systematically review AI applications that have been implemented in real-life clinical practice.MethodsWe conducted a literature search in PubMed, Embase, Cochrane Central, and CINAHL to identify relevant articles published between January 2010 and May 2020. We also hand searched premier computer science journals and conferences as well as registered clinical trials. Studies were included if they reported AI applications that had been implemented in real-world clinical settings.ResultsWe identified 51 relevant studies that reported the implementation and evaluation of AI applications in clinical practice, of which 13 adopted a randomized controlled trial design and eight adopted an experimental design. The AI applications targeted various clinical tasks, such as screening or triage (n=16), disease diagnosis (n=16), risk analysis (n=14), and treatment (n=7). The most commonly addressed diseases and conditions were sepsis (n=6), breast cancer (n=5), diabetic retinopathy (n=4), and polyp and adenoma (n=4). Regarding the evaluation outcomes, we found that 26 studies examined the performance of AI applications in clinical settings, 33 studies examined the effect of AI applications on clinician outcomes, 14 studies examined the effect on patient outcomes, and one study examined the economic impact associated with AI implementation.ConclusionsThis review indicates that research on the clinical implementation of AI applications is still at an early stage despite the great potential. More research needs to assess the benefits and challenges associated with clinical AI applications through a more rigorous methodology.
| S-EPMC8103304 | biostudies-literature
Project description:The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that led to the COVID-19 (Coronavirus Disease 2019) pandemic, has resulted in substantial overburdening of healthcare systems as well as an economic crisis on a global scale. This has in turn resulted in widespread efforts to identify suitable therapies to address this aggressive pathogen. Therapeutic antibody and vaccine development are being actively explored, and a phase I clinical trial of mRNA-1273 which is developed in collaboration between the National Institute of Allergy and Infectious Diseases and Moderna, Inc. is currently underway. Timelines for the broad deployment of a vaccine and antibody therapies have been estimated to be 12-18 months or longer. These are promising approaches that may lead to sustained efficacy in treating COVID-19. However, its emergence has also led to a large number of clinical trials evaluating drug combinations composed of repurposed therapies. As study results of these combinations continue to be evaluated, there is a need to move beyond traditional drug screening and repurposing by harnessing artificial intelligence (AI) to optimize combination therapy design. This may lead to the rapid identification of regimens that mediate unexpected and markedly enhanced treatment outcomes.
| S-EPMC7235485 | biostudies-literature
Project description:BackgroundLaryngopharyngeal cancer (LPC) includes laryngeal and hypopharyngeal cancer, whose early diagnosis can significantly improve the prognosis and quality of life of patients. Pathological biopsy of suspicious cancerous tissue under the guidance of laryngoscopy is the gold standard for diagnosing LPC. However, this subjective examination largely depends on the skills and experience of laryngologists, which increases the possibility of missed diagnoses and repeated unnecessary biopsies. We aimed to develop and validate a deep convolutional neural network-based Laryngopharyngeal Artificial Intelligence Diagnostic System (LPAIDS) for real-time automatically identifying LPC in both laryngoscopy white-light imaging (WLI) and narrow-band imaging (NBI) images to improve the diagnostic accuracy of LPC by reducing diagnostic variation among on-expert laryngologists.MethodsAll 31,543 laryngoscopic images from 2382 patients were categorised into training, verification, and test sets to develop, validate, and internal test LPAIDS. Another 25,063 images from five other hospitals were used as external tests. Overall, 551 videos were used to evaluate the real-time performance of the system, and 200 randomly selected videos were used to compare the diagnostic performance of the LPAIDS with that of laryngologists. Two deep-learning models using either WLI (model W) or NBI (model N) images were constructed to compare with LPAIDS.ResultsLPAIDS had a higher diagnostic performance than models W and N, with accuracies of 0·956 and 0·949 in the internal image and video tests, respectively. The robustness and stability of LPAIDS were validated in external sets with the area under the receiver operating characteristic curve values of 0·965-0·987. In the laryngologist-machine competition, LPAIDS achieved an accuracy of 0·940, which was comparable to expert laryngologists and outperformed other laryngologists with varying qualifications.ConclusionsLPAIDS provided high accuracy and stability in detecting LPC in real-time, which showed great potential for using LPAIDS to improve the diagnostic accuracy of LPC by reducing diagnostic variation among on-expert laryngologists.
| S-EPMC10559609 | biostudies-literature
Project description:Patients' increasing digital participation provides an opportunity to pursue patient-centric research and drug development by understanding their needs. Social media has proven to be one of the most useful data sources when it comes to understanding a company's potential audience to drive more targeted impact. Navigating through an ocean of information is a tedious task where techniques such as artificial intelligence and text analytics have proven effective in identifying relevant posts for healthcare business questions. Here, we present an enterprise-ready, scalable solution demonstrating the feasibility and utility of social media-based patient experience data for use in research and development through capturing and assessing patient experiences and expectations on disease, treatment options, and unmet needs while creating a playbook for roll-out to other indications and therapeutic areas.
| S-EPMC10601646 | biostudies-literature
Project description:Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) plays a pivotal role in preventing autoimmunity and fostering anticancer immunity by interacting with B7 proteins CD80 and CD86. CTLA-4 is the first immune checkpoint targeted with a monoclonal antibody inhibitor. Checkpoint inhibitors have generated durable responses in many cancer patients, representing a revolutionary milestone in cancer immunotherapy. However, therapeutic efficacy is limited to a small portion of patients, and immune-related adverse events are noteworthy, especially for monoclonal antibodies directed against CTLA-4. Previously, small molecules have been developed to impair the CTLA-4: CD80 interaction; however, they directly targeted CD80 and not CTLA-4. In this study, we performed artificial intelligence (AI)-powered virtual screening of approximately ten million compounds to target CTLA-4. We validated primary hits with biochemical, biophysical, immunological, and experimental animal assays. We then optimized lead compounds and obtained inhibitors with an inhibitory concentration of 1 micromole in disrupting the interaction between CTLA-4 and CD80. Unlike ipilimumab, these small molecules did not degrade CTLA-4. Several compounds inhibited tumor development prophylactically and therapeutically in syngeneic and CTLA-4-humanized mice. This project supports an AI-based framework in designing small molecules targeting immune checkpoints for cancer therapy.
2024-02-15 | GSE228560 | GEO