Genome Analysis for Inherited Retinal Disease: The State of the Art

Oprych, Kate; Silva, Raquel Sofia; Pontikos, Nikolas; Arno, Gavin

doi:10.1007/978-981-15-9184-6_12

Cited by 2 publications

(2 citation statements)

References 110 publications

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“…SOPHiA GENETICS employs AI for the analysis of genomic data, particularly in the context of rare diseases. The platform interprets complex genetic information, identifies potential disease-causing variants, and assists healthcare professionals in making informed decisions (Oprych et al, 2021). This case study illustrates how AI can contribute to advancements in genomics, offering insights into rare diseases and facilitating more accurate diagnoses.…”

Section: Case Studies Of Artificial Intelligence Application In Healt...mentioning

confidence: 99%

Artificial Intelligence in Healthcare: A Review of Ethical Dilemmas and Practical Applications

Evangel Chinyere Anyanwu,

Chiamaka Chinaemelum Okongwu,

Tolulope O Olorunsogo

et al. 2024

imsrj

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The fusion of Artificial Intelligence (AI) and healthcare heralds a new era of innovation and transformation, yet it is not without its ethical quandaries. This comprehensive review traverses the intricate landscape where AI meets healthcare, delving into the ethical dilemmas that arise alongside practical applications. The ethical considerations span a spectrum, encompassing issues of patient privacy, transparency, accountability, and the inadvertent perpetuation of biases within AI algorithms. Privacy concerns emerge as a central ethical dilemma as healthcare providers leverage AI to process vast amounts of patient data. Striking a delicate balance between harnessing the power of AI for diagnostic and predictive purposes and safeguarding sensitive medical information is a critical challenge. Moreover, the review scrutinizes the ethical implications of AI algorithms and their potential to perpetuate biases, inadvertently exacerbating health disparities. A nuanced examination of bias mitigation strategies becomes imperative to ensure that AI technologies contribute to equitable healthcare outcomes. In tandem with ethical considerations, the review illuminates the practical applications reshaping the healthcare landscape. AI-driven diagnostics, predictive modeling, and personalized treatment plans emerge as transformative tools, enhancing clinical decision-making and patient outcomes. The efficient allocation of resources, streamlined workflows, and the acceleration of drug discovery processes showcase the tangible benefits of AI integration. This review aspires to guide healthcare practitioners, policymakers, and technologists in navigating the ethical crossroads of AI in healthcare. By fostering an awareness of ethical pitfalls and emphasizing responsible AI development, stakeholders can collaboratively shape a future where AI augments healthcare delivery, upholds ethical standards, and ultimately improves the quality of patient care. Keywords: AI, Healthcare, Ethics, Review, AI Application.

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Section: Case Studies Of Artificial Intelligence Application In Healt...mentioning

confidence: 99%

Artificial Intelligence in Healthcare: A Review of Ethical Dilemmas and Practical Applications

Evangel Chinyere Anyanwu,

Chiamaka Chinaemelum Okongwu,

Tolulope O Olorunsogo

et al. 2024

imsrj

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“…Genetic testing in the ophthalmic genetics’ clinic has evolved dramatically over the last decades, as technologies have become more accessible and inexpensive. Single-candidate gene approach by Sanger sequencing, next generation sequencing (NGS)-based panels, exome sequencing (ES) and genome sequencing (GS) are currently employed by clinical diagnostic, commercial and research laboratories worldwide ( 13 ). GS represents the most comprehensive test, with a significant improvement in diagnostic yield compared with NGS panels ( 14 ) and ES ( 15 ).…”

Section: Introductionmentioning

confidence: 99%

Multidisciplinary team directed analysis of whole genome sequencing reveals pathogenic non-coding variants in molecularly undiagnosed inherited retinal dystrophies

Varela

Bellingham²,

Motta

et al. 2022

Human Molecular Genetics

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Purpose: To identify, using genome sequencing (GS), likely pathogenic non-coding variants in inherited retinal dystrophy (IRD) genes Methods: Patients with IRD were recruited to the study and underwent comprehensive ophthalmological evaluation and GS. The results of GS were investigated through virtual gene panel analysis and plausible pathogenic variants and clinical phenotype evaluated by multi-disciplinary team (MDT) discussion. For unsolved patients in whom a specific gene was suspected to harbour a missed pathogenic variant, targeted re-analysis of non-coding regions was performed on GS data. Candidate variants were functionally tested including by mRNA analysis, minigene and luciferase reporter assays. Results: Previously unreported, likely pathogenic, non-coding variants, in 7 genes (PRPF31, NDP, IFT140, CRB1, USH2A, BBS10, and GUCY2D), were identified in 11 patients. These were shown to lead to mis-splicing (PRPF31, IFT140, CRB1, USH2A) or altered transcription levels (BBS10, GUCY2D). Conclusion: MDT-led, phenotype driven, non-coding variant re-analysis of GS is effective in identifying missing causative alleles.

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Genome Analysis for Inherited Retinal Disease: The State of the Art

Cited by 2 publications

References 110 publications

Artificial Intelligence in Healthcare: A Review of Ethical Dilemmas and Practical Applications

Artificial Intelligence in Healthcare: A Review of Ethical Dilemmas and Practical Applications

Multidisciplinary team directed analysis of whole genome sequencing reveals pathogenic non-coding variants in molecularly undiagnosed inherited retinal dystrophies

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