The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.

High-performance medicine: the convergence of human and artificial intelligence

Artificial intelligence (AI) is gradually changing medical practice. With recent progress in digitized data acquisition, machine learning and computing infrastructure, AI applications are expanding into areas that were previously thought to be only the province of human experts. In this Review Article, we outline recent breakthroughs in AI technologies and their biomedical applications, identify the challenges for further progress in medical AI systems, and summarize the economic, legal and social implications of AI in healthcare.

Artificial Intelligence in Healthcare

Artificial intelligence (AI) has been in development for decades and become a productive tool in many industries. While AI has received a tremendous amount of hype and hyperbole, advances in the availability of data for training, dramatically faster hardware, and maturation of the tools and algorithms are delivering radically better predictive models that are producing real, quantifiable impact within health care organizations. In particular, machine learning, a subset of AI, is becoming an essential part of advanced health system analytics programs. AI-enhanced processes can provide advantages in speed, cost, capacity, quality, and consistency, allowing human decision makers to focus on higher-value “top of license” tasks. The success of AI, as well as other advanced analytical techniques, depends on first building a mature conventional analytics program with a strong foundation in governance, culture, and skills.

Artificial Intelligence in Health Care

Summary Microfluidics permit the automated manipulation of fluids at the microscale with high throughput and spatiotemporal precision, enabling the generation of large, multidimensional datasets. Machine intelligence provides powerful predictive tools with the ability to learn from data. The analysis of microfluidics-generated data via machine learning has been applied in a variety of contexts, achieving impressive results. Here, we elaborate on the potential of operating microfluidic platforms via closed-loop data-driven models by leveraging multimodal monitoring and data-acquisition instrumentation. We believe this approach will provide a robust framework for fundamental explorations in materials science and biomedicine, with implications in fields such as drug discovery, nanomaterials, in vitro organ modeling, and developmental biology. We identify challenges and propose research strategies in the context of the prediction and optimization of chemical reactions and materials syntheses and the development of the next generation of more robust and functional organs-on-chips and emerging organoids-on-chips.

Intelligent Microfluidics: The Convergence of Machine Learning and Microfluidics in Materials Science and Biomedicine

The frequent outbreak of global infectious diseases has prompted the development of rapid and effective diagnostic tools for the early screening of potential patients in point-of-care testing scenarios. With advances in mobile computing power and microfluidic technology, the smartphone-based mobile health platform has drawn significant attention from researchers developing point-of-care testing devices that integrate microfluidic optical detection with artificial intelligence analysis. In this article, we summarize recent progress in these mobile health platforms, including the aspects of microfluidic chips, imaging modalities, supporting components, and the development of software algorithms. We document the application of mobile health platforms in terms of the detection objects, including molecules, viruses, cells, and parasites. Finally, we discuss the prospects for future development of mobile health platforms. 

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Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence

The risk stratification of prostate cancer and breast cancer tumours from patients relies on histopathology, selective genomic testing, or on other methods employing fixed formalin tissue samples. However, static biomarker measurements from bulk fixed-tissue samples provide limited accuracy and actionability. Here, we report the development of a live-primary-cell phenotypic-biomarker assay with single-cell resolution, and its validation with prostate cancer and breast cancer tissue samples for the prediction of post-surgical adverse pathology. The assay includes a collagen-I/fibronectin extracellular-matrix formulation, dynamic live-cell biomarkers, a microfluidic device, machine-vision analysis and machine-learning algorithms, and generates predictive scores of adverse pathology at the time of surgery. Predictive scores for the risk stratification of 59 prostate cancer patients and 47 breast cancer patients, with values for area under the curve in receiver-operating-characteristic curves surpassing 80%, support the validation of the assay and its potential clinical applicability for the risk stratification of cancer patients.

/pdf/live-cell-phenotypic-biomarker-microfluidic-assay-for-the-2ljfjrk3zu.pdf

Live-cell phenotypic-biomarker microfluidic assay for the risk stratification of cancer patients via machine learning

211 Background: Over-treatment of prostate cancer affects 144,000 patients annually in the U.S. due to the inaccuracy of current diagnostics for risk stratification. Functional molecular and biophysical biomarkers combined with clinically actionable diagnostic platforms are used for evaluating the local invasiveness and metastatic potential of cells derived from biopsy. Such a diagnostic test may improve patient outcomes in low-to high-risk prostate cancers according to D’Amico’s definition. This study describes the development of a novel phenotypic diagnostic using a set of biomarkers measured with a proprietary live cell assay coupled with innovative machine vision algorithms for patient tumor samples using a unique microfluidic platform. Methods: We assessed 60 prostate cancer samples collected post radical prostatectomy (RP). We report the assay details for culturing live tumor cells ex vivo, enabling automated imaging of phenotypic biomarkers. The test is designed to sustain survival of prostate tumo...

Novel live tumor cell diagnostic test utilizing biophysical and molecular biomarkers to assess local, advanced, and metastatic prostate cancer.

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Live-cell phenotypic-biomarker microfluidic assay for the risk stratification of cancer patients via machine learning

Novel live tumor cell diagnostic test utilizing biophysical and molecular biomarkers to assess local, advanced, and metastatic prostate cancer.