Artificial intelligence (AI) research in healthcare is accelerating rapidly, with potential applications being demonstrated across various domains of medicine. However, there are currently limited examples of such techniques being successfully deployed into clinical practice. This article explores the main challenges and limitations of AI in healthcare, and considers the steps required to translate these potentially transformative technologies from research to clinical practice. Key challenges for the translation of AI systems in healthcare include those intrinsic to the science of machine learning, logistical difficulties in implementation, and consideration of the barriers to adoption as well as of the necessary sociocultural or pathway changes. Robust peer-reviewed clinical evaluation as part of randomised controlled trials should be viewed as the gold standard for evidence generation, but conducting these in practice may not always be appropriate or feasible. Performance metrics should aim to capture real clinical applicability and be understandable to intended users. Regulation that balances the pace of innovation with the potential for harm, alongside thoughtful post-market surveillance, is required to ensure that patients are not exposed to dangerous interventions nor deprived of access to beneficial innovations. Mechanisms to enable direct comparisons of AI systems must be developed, including the use of independent, local and representative test sets. Developers of AI algorithms must be vigilant to potential dangers, including dataset shift, accidental fitting of confounders, unintended discriminatory bias, the challenges of generalisation to new populations, and the unintended negative consequences of new algorithms on health outcomes. The safe and timely translation of AI research into clinically validated and appropriately regulated systems that can benefit everyone is challenging. Robust clinical evaluation, using metrics that are intuitive to clinicians and ideally go beyond measures of technical accuracy to include quality of care and patient outcomes, is essential. Further work is required (1) to identify themes of algorithmic bias and unfairness while developing mitigations to address these, (2) to reduce brittleness and improve generalisability, and (3) to develop methods for improved interpretability of machine learning predictions. If these goals can be achieved, the benefits for patients are likely to be transformational.

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Key challenges for delivering clinical impact with artificial intelligence.

https://repository.tudelft.nl/islandora/object/uuid%3A7bdae86a-0209-4e53-83d0-c531dd3465ec/datastream/OBJ/download

Artificial Intelligence (AI) : Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

Genetic diversity created by transposable elements is an important source of functional variation upon which selection acts during evolution. Transposable elements are associated with adaptation to temperate climates in Drosophila, a SINE element is associated with the domestication of small dog breeds from the gray wolf and there is evidence that transposable elements were targets of selection during human evolution. Although the list of examples of transposable elements associated with host gene function continues to grow, proof that transposable elements are causative and not just correlated with functional variation is limited. Here we show that a transposable element (Hopscotch) inserted in a regulatory region of the maize domestication gene, teosinte branched1 (tb1), acts as an enhancer of gene expression and partially explains the increased apical dominance in maize compared to its progenitor, teosinte. Molecular dating indicates that the Hopscotch insertion predates maize domestication by at least 10,000 years, indicating that selection acted on standing variation rather than new mutation.

Identification of a Functional Transposon Insertion in the Maize Domestication Gene tb1

The multi-factorial nature of clinical multidrug resistance in cancer.

Genome-wide association studies in maize: praise and stargaze.

In the field of cancer genomics, the broad availability of genetic information offered by next-generation sequencing technologies and rapid growth in biomedical publication has led to the advent of the big-data era. Integration of artificial intelligence (AI) approaches such as machine learning, deep learning, and natural language processing (NLP) to tackle the challenges of scalability and high dimensionality of data and to transform big data into clinically actionable knowledge is expanding and becoming the foundation of precision medicine. In this paper, we review the current status and future directions of AI application in cancer genomics within the context of workflows to integrate genomic analysis for precision cancer care. The existing solutions of AI and their limitations in cancer genetic testing and diagnostics such as variant calling and interpretation are critically analyzed. Publicly available tools or algorithms for key NLP technologies in the literature mining for evidence-based clinical recommendations are reviewed and compared. In addition, the present paper highlights the challenges to AI adoption in digital healthcare with regard to data requirements, algorithmic transparency, reproducibility, and real-world assessment, and discusses the importance of preparing patients and physicians for modern digitized healthcare. We believe that AI will remain the main driver to healthcare transformation toward precision medicine, yet the unprecedented challenges posed should be addressed to ensure safety and beneficial impact to healthcare.

https://link.springer.com/content/pdf/10.1007%2Fs00439-019-01970-5.pdf

Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives.

Strong directional selection occurred during the domestication of maize from its wild ancestor teosinte, reducing its genetic diversity, particularly at genes controlling domestication-related traits. Nevertheless, variability for some domestication-related traits is maintained in maize. The genetic basis of this could be sequence variation at the same key genes controlling maize-teosinte differentiation (due to lack of fixation or arising as new mutations after domestication), distinct loci with large effects, or polygenic background variation. Previous studies permit annotation of maize genome regions associated with the major differences between maize and teosinte or that exhibit population genetic signals of selection during either domestication or postdomestication improvement. Genome-wide association studies and genetic variance partitioning analyses were performed in two diverse maize inbred line panels to compare the phenotypic effects and variances of sequence polymorphisms in regions involved in domestication and improvement to the rest of the genome. Additive polygenic models explained most of the genotypic variation for domestication-related traits; no large-effect loci were detected for any trait. Most trait variance was associated with background genomic regions lacking previous evidence for involvement in domestication. Improvement sweep regions were associated with more trait variation than expected based on the proportion of the genome they represent. Selection during domestication eliminated large-effect genetic variants that would revert maize toward a teosinte type. Small-effect polygenic variants (enriched in the improvement sweep regions of the genome) are responsible for most of the standing variation for domestication-related traits in maize.

https://www.genetics.org/content/genetics/204/1/99.full.pdf

Genetic Architecture of Domestication-Related Traits in Maize

Objectives Describe an augmented intelligence approach to facilitate the update of evidence for associations in knowledge graphs. Methods New publications are filtered through multiple machine learning study classifiers, and filtered publications are combined with articles already included as evidence in the knowledge graph. The corpus is then subjected to named entity recognition, semantic dictionary mapping, term vector space modeling, pairwise similarity, and focal entity match to identify highly related publications. Subject matter experts review recommended articles to assess inclusion in the knowledge graph; discrepancies are resolved by consensus. Results Study classifiers achieved F-scores from 0.88 to 0.94, and similarity thresholds for each study type were determined by experimentation. Our approach reduces human literature review load by 99%, and over the past 12 months, 41% of recommendations were accepted to update the knowledge graph. Conclusion Integrated search and recommendation exploiting current evidence in a knowledge graph is useful for reducing human cognition load.

Predictive article recommendation using natural language processing and machine learning to support evidence updates in domain-specific knowledge graphs.

Linear mixed models are widely used in humans, animals, and plants to conduct genome-wide association studies (GWAS). A characteristic of experimental designs for plants is that experimental units are typically multiple-plant plots of families or lines that are replicated across environments. This structure can present computational challenges to conducting a genome scan on raw (plot-level) data. Two-stage methods have been proposed to reduce the complexity and increase the computational speed of whole-genome scans. The first stage of the analysis fits raw data to a model including environment and line effects, but no individual marker effects. The second stage involves the whole genome scan of marker tests using summary values for each line as the dependent variable. Missing data and unbalanced experimental designs can result in biased estimates of marker association effects from two-stage analyses. In this study, we developed a weighted two-stage analysis to reduce bias and improve power of GWAS while maintaining the computational efficiency of two-stage analyses. Simulation based on real marker data of a diverse panel of maize inbred lines was used to compare power and false discovery rate of the new weighted two-stage method to single-stage and other two-stage analyses and to compare different two-stage models. In the case of severely unbalanced data, only the weighted two-stage GWAS has power and false discovery rate similar to the one-stage analysis. The weighted GWAS method has been implemented in the open-source software TASSEL.

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Shang Xue

Papers

Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives.

Genetic Architecture of Domestication-Related Traits in Maize

Predictive article recommendation using natural language processing and machine learning to support evidence updates in domain-specific knowledge graphs.

Comparison of one-stage and two-stage genome-wide association studies