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Rennie O. Navigating the uncommon: challenges in applying evidence-based medicine to rare diseases and the prospects of artificial intelligence solutions. MEDICINE, HEALTH CARE, AND PHILOSOPHY 2024; 27:269-284. [PMID: 38722452 DOI: 10.1007/s11019-024-10206-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/16/2024] [Indexed: 08/09/2024]
Abstract
The study of rare diseases has long been an area of challenge for medical researchers, with agonizingly slow movement towards improved understanding of pathophysiology and treatments compared with more common illnesses. The push towards evidence-based medicine (EBM), which prioritizes certain types of evidence over others, poses a particular issue when mapped onto rare diseases, which may not be feasibly investigated using the methodologies endorsed by EBM, due to a number of constraints. While other trial designs have been suggested to overcome these limitations (with varying success), perhaps the most recent and enthusiastically adopted is the application of artificial intelligence to rare disease data. This paper critically examines the pitfalls of EBM (and its trial design offshoots) as it pertains to rare diseases, exploring the current landscape of AI as a potential solution to these challenges. This discussion is also taken a step further, providing philosophical commentary on the weaknesses and dangers of AI algorithms applied to rare disease research. While not proposing a singular solution, this article does provide a thoughtful reminder that no 'one-size-fits-all' approach exists in the complex world of rare diseases. We must balance cautious optimism with critical evaluation of new research paradigms and technology, while at the same time not neglecting the ever-important aspect of patient values and preferences, which may be challenging to incorporate into computer-driven models.
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Affiliation(s)
- Olivia Rennie
- Institute for the History and Philosophy of Science and Technology, University of Toronto, 73 Queen's Park Cres. E, Toronto, ON, M5S 1K7, Canada.
- Temerty Faculty of Medicine, University of Toronto, 1 King's College Cir., Toronto, ON, M5S 1A8, Canada.
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2
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Ozcelik F, Dundar MS, Yildirim AB, Henehan G, Vicente O, Sánchez-Alcázar JA, Gokce N, Yildirim DT, Bingol NN, Karanfilska DP, Bertelli M, Pojskic L, Ercan M, Kellermayer M, Sahin IO, Greiner-Tollersrud OK, Tan B, Martin D, Marks R, Prakash S, Yakubi M, Beccari T, Lal R, Temel SG, Fournier I, Ergoren MC, Mechler A, Salzet M, Maffia M, Danalev D, Sun Q, Nei L, Matulis D, Tapaloaga D, Janecke A, Bown J, Cruz KS, Radecka I, Ozturk C, Nalbantoglu OU, Sag SO, Ko K, Arngrimsson R, Belo I, Akalin H, Dundar M. The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution. Funct Integr Genomics 2024; 24:138. [PMID: 39147901 DOI: 10.1007/s10142-024-01417-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2024] [Revised: 08/01/2024] [Accepted: 08/05/2024] [Indexed: 08/17/2024]
Abstract
Artificial intelligence (AI) platforms have emerged as pivotal tools in genetics and molecular medicine, as in many other fields. The growth in patient data, identification of new diseases and phenotypes, discovery of new intracellular pathways, availability of greater sets of omics data, and the need to continuously analyse them have led to the development of new AI platforms. AI continues to weave its way into the fabric of genetics with the potential to unlock new discoveries and enhance patient care. This technology is setting the stage for breakthroughs across various domains, including dysmorphology, rare hereditary diseases, cancers, clinical microbiomics, the investigation of zoonotic diseases, omics studies in all medical disciplines. AI's role in facilitating a deeper understanding of these areas heralds a new era of personalised medicine, where treatments and diagnoses are tailored to the individual's molecular features, offering a more precise approach to combating genetic or acquired disorders. The significance of these AI platforms is growing as they assist healthcare professionals in the diagnostic and treatment processes, marking a pivotal shift towards more informed, efficient, and effective medical practice. In this review, we will explore the range of AI tools available and show how they have become vital in various sectors of genomic research supporting clinical decisions.
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Affiliation(s)
- Firat Ozcelik
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Mehmet Sait Dundar
- Department of Electrical and Computer Engineering, Graduate School of Engineering and Sciences, Abdullah Gul University, Kayseri, Turkey
| | - A Baki Yildirim
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Gary Henehan
- School of Food Science and Environmental Health, Technological University of Dublin, Dublin, Ireland
| | - Oscar Vicente
- Institute for the Conservation and Improvement of Valencian Agrodiversity (COMAV), Universitat Politècnica de València, Valencia, Spain
| | - José A Sánchez-Alcázar
- Centro de Investigación Biomédica en Red: Enfermedades Raras, Centro Andaluz de Biología del Desarrollo (CABD-CSIC-Universidad Pablo de Olavide), Instituto de Salud Carlos III, Sevilla, Spain
| | - Nuriye Gokce
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Duygu T Yildirim
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Nurdeniz Nalbant Bingol
- Department of Translational Medicine, Institute of Health Sciences, Bursa Uludag University, Bursa, Turkey
| | - Dijana Plaseska Karanfilska
- Research Centre for Genetic Engineering and Biotechnology, Macedonian Academy of Sciences and Arts, Skopje, Macedonia
| | | | - Lejla Pojskic
- Institute for Genetic Engineering and Biotechnology, University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Mehmet Ercan
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Miklos Kellermayer
- Department of Biophysics and Radiation Biology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Izem Olcay Sahin
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | | | - Busra Tan
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Donald Martin
- University Grenoble Alpes, CNRS, TIMC-IMAG/SyNaBi (UMR 5525), Grenoble, France
| | - Robert Marks
- Avram and Stella Goldstein-Goren Department of Biotechnology Engineering, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Satya Prakash
- Department of Biomedical Engineering, University of McGill, Montreal, QC, Canada
| | - Mustafa Yakubi
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Tommaso Beccari
- Department of Pharmeceutical Sciences, University of Perugia, Perugia, Italy
| | - Ratnesh Lal
- Neuroscience Research Institute, University of California, Santa Barbara, USA
| | - Sehime G Temel
- Department of Translational Medicine, Institute of Health Sciences, Bursa Uludag University, Bursa, Turkey
- Department of Medical Genetics, Bursa Uludag University Faculty of Medicine, Bursa, Turkey
- Department of Histology and Embryology, Faculty of Medicine, Bursa Uludag University, Bursa, Turkey
| | - Isabelle Fournier
- Réponse Inflammatoire et Spectrométrie de Masse-PRISM, University of Lille, Lille, France
| | - M Cerkez Ergoren
- Department of Medical Genetics, Near East University Faculty of Medicine, Nicosia, Cyprus
| | - Adam Mechler
- Department of Chemistry, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, VIC, Australia
| | - Michel Salzet
- Réponse Inflammatoire et Spectrométrie de Masse-PRISM, University of Lille, Lille, France
| | - Michele Maffia
- Department of Experimental Medicine, University of Salento, Via Lecce-Monteroni, Lecce, 73100, Italy
| | - Dancho Danalev
- University of Chemical Technology and Metallurgy, Sofia, Bulgaria
| | - Qun Sun
- Department of Food Science and Technology, Sichuan University, Chengdu, China
| | - Lembit Nei
- School of Engineering Tallinn University of Technology, Tartu College, Tartu, Estonia
| | - Daumantas Matulis
- Department of Biothermodynamics and Drug Design, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Dana Tapaloaga
- Faculty of Veterinary Medicine, University of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania
| | - Andres Janecke
- Department of Paediatrics I, Medical University of Innsbruck, Innsbruck, Austria
- Division of Human Genetics, Medical University of Innsbruck, Innsbruck, Austria
| | - James Bown
- School of Science, Engineering and Technology, Abertay University, Dundee, UK
| | | | - Iza Radecka
- School of Science, Faculty of Science and Engineering, University of Wolverhampton, Wolverhampton, UK
| | - Celal Ozturk
- Department of Software Engineering, Erciyes University, Kayseri, Turkey
| | - Ozkan Ufuk Nalbantoglu
- Department of Computer Engineering, Engineering Faculty, Erciyes University, Kayseri, Turkey
| | - Sebnem Ozemri Sag
- Department of Medical Genetics, Bursa Uludag University Faculty of Medicine, Bursa, Turkey
| | - Kisung Ko
- Department of Medicine, College of Medicine, Chung-Ang University, Seoul, Korea
| | - Reynir Arngrimsson
- Iceland Landspitali University Hospital, University of Iceland, Reykjavik, Iceland
| | - Isabel Belo
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - Hilal Akalin
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey.
| | - Munis Dundar
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey.
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Zelin C, Chung WK, Jeanne M, Zhang G, Weng C. Rare disease diagnosis using knowledge guided retrieval augmentation for ChatGPT. J Biomed Inform 2024; 157:104702. [PMID: 39084480 DOI: 10.1016/j.jbi.2024.104702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 07/19/2024] [Accepted: 07/24/2024] [Indexed: 08/02/2024]
Abstract
Although rare diseases individually have a low prevalence, they collectively affect nearly 400 million individuals around the world. On average, it takes five years for an accurate rare disease diagnosis, but many patients remain undiagnosed or misdiagnosed. As machine learning technologies have been used to aid diagnostics in the past, this study aims to test ChatGPT's suitability for rare disease diagnostic support with the enhancement provided by Retrieval Augmented Generation (RAG). RareDxGPT, our enhanced ChatGPT model, supplies ChatGPT with information about 717 rare diseases from an external knowledge resource, the RareDis Corpus, through RAG. In RareDxGPT, when a query is entered, the three documents most relevant to the query in the RareDis Corpus are retrieved. Along with the query, they are returned to ChatGPT to provide a diagnosis. Additionally, phenotypes for thirty different diseases were extracted from free text from PubMed's Case Reports. They were each entered with three different prompt types: "prompt", "prompt + explanation" and "prompt + role play." The accuracy of ChatGPT and RareDxGPT with each prompt was then measured. With "Prompt", RareDxGPT had a 40 % accuracy, while ChatGPT 3.5 got 37 % of the cases correct. With "Prompt + Explanation", RareDxGPT had a 43 % accuracy, while ChatGPT 3.5 got 23 % of the cases correct. With "Prompt + Role Play", RareDxGPT had a 40 % accuracy, while ChatGPT 3.5 got 23 % of the cases correct. To conclude, ChatGPT, especially when supplying extra domain specific knowledge, demonstrates early potential for rare disease diagnosis with adjustments.
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Affiliation(s)
| | - Wendy K Chung
- Department of Pediatrics, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Mederic Jeanne
- Department of Pediatrics, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Gongbo Zhang
- Department of Biomedical Informatics, Columbia University, New York City, NY 10032, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York City, NY 10032, USA.
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Van Coillie S, Prévot J, Sánchez-Ramón S, Lowe DM, Borg M, Autran B, Segundo G, Pecoraro A, Garcelon N, Boersma C, Silva SL, Drabwell J, Quinti I, Meyts I, Ali A, Burns SO, van Hagen M, Pergent M, Mahlaoui N. Charting a course for global progress in PIDs by 2030 - proceedings from the IPOPI global multi-stakeholders' summit (September 2023). Front Immunol 2024; 15:1430678. [PMID: 39055704 PMCID: PMC11270239 DOI: 10.3389/fimmu.2024.1430678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 06/13/2024] [Indexed: 07/27/2024] Open
Abstract
The International Patient Organisation for Primary Immunodeficiencies (IPOPI) held its second Global Multi-Stakeholders' Summit, an annual stimulating and forward-thinking meeting uniting experts to anticipate pivotal upcoming challenges and opportunities in the field of primary immunodeficiency (PID). The 2023 summit focused on three key identified discussion points: (i) How can immunoglobulin (Ig) therapy meet future personalized patient needs? (ii) Pandemic preparedness: what's next for public health and potential challenges for the PID community? (iii) Diagnosing PIDs in 2030: what needs to happen to diagnose better and to diagnose more? Clinician-Scientists, patient representatives and other stakeholders explored avenues to improve Ig therapy through mechanistic insights and tailored Ig preparations/products according to patient-specific needs and local exposure to infectious agents, amongst others. Urgency for pandemic preparedness was discussed, as was the threat of shortage of antibiotics and increasing antimicrobial resistance, emphasizing the need for representation of PID patients and other vulnerable populations throughout crisis and care management. Discussion also covered the complexities of PID diagnosis, addressing issues such as global diagnostic disparities, the integration of patient-reported outcome measures, and the potential of artificial intelligence to increase PID diagnosis rates and to enhance diagnostic precision. These proceedings outline the outcomes and recommendations arising from the 2023 IPOPI Global Multi-Stakeholders' Summit, offering valuable insights to inform future strategies in PID management and care. Integral to this initiative is its role in fostering collaborative efforts among stakeholders to prepare for the multiple challenges facing the global PID community.
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Affiliation(s)
- Samya Van Coillie
- International Patient Organisation for Primary Immunodeficiencies (IPOPI), Brussels, Belgium
| | - Johan Prévot
- International Patient Organisation for Primary Immunodeficiencies (IPOPI), Brussels, Belgium
| | - Silvia Sánchez-Ramón
- Department of Clinical Immunology, Health Research Institute of the Hospital Clínico San Carlos/Fundación para la Investigación Biomédica del Hospital Clínico San Carlos (IML and IdISSC), Health Research Institute of the Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - David M. Lowe
- Department of Immunology, Royal Free London National Heath System (NHS) Foundation Trust, London, United Kingdom
- Institute of Immunity and Transplantation, University College London, London, United Kingdom
| | - Michael Borg
- Department of Infection Control & Sterile Services, Mater Dei Hospital, Msida, Malta
| | - Brigitte Autran
- Sorbonne-Université, Cimi-Paris, Institut national de la santé et de la recherche médicale (INSERM) U1135, centre national de la recherche scientifique (CNRS) ERL8255, Université Pierre et Marie Curie Centre de Recherche n°7 (UPMC CR7), Paris, France
| | - Gesmar Segundo
- Departamento de Pediatra, Universidade Federal de Uberlândia, Uberlandia, MG, Brazil
| | - Antonio Pecoraro
- Transfusion Medicine Unit, Azienda Sanitaria Territoriale, Ascoli Piceno, Italy
| | - Nicolas Garcelon
- Université de Paris, Imagine Institute, Data Science Platform, Institut national de la santé et de la recherche médicale Unité Mixte de Recherche (INSERM UMR) 1163, Paris, France
| | - Cornelis Boersma
- Health-Ecore B.V., Zeist, Netherlands
- Unit of Global Health, Department of Health Sciences, University Medical Center Groningen (UMCG), University of Groningen, Groningen, Netherlands
- Department of Management Sciences, Open University, Heerlen, Netherlands
| | - Susana L. Silva
- Serviço de Imunoalergologia, Unidade Local de Saúde de Santa Maria, Lisbon, Portugal
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | - Jose Drabwell
- International Patient Organisation for Primary Immunodeficiencies (IPOPI), Brussels, Belgium
| | - Isabella Quinti
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy
| | - Isabelle Meyts
- Department of Pediatrics, University Hospitals Leuven, Department of Microbiology, Immunology and Transplantation, Katholieke Universiteit (KU) Leuven, Leuven, Belgium
| | - Adli Ali
- Department of Paediatrics, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
- Hospital Tunku Ampuan Besar Tuanku Aishah Rohani, Universiti Kebangsaan Malaysia (UKM) Specialist Children’s Hospital, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Siobhan O. Burns
- Department of Immunology, Royal Free London National Heath System (NHS) Foundation Trust, London, United Kingdom
- Institute of Immunity and Transplantation, University College London, London, United Kingdom
| | - Martin van Hagen
- Department of Internal Medicine, Division of Allergy & Clinical Immunology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
- Department of Immunology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Martine Pergent
- International Patient Organisation for Primary Immunodeficiencies (IPOPI), Brussels, Belgium
| | - Nizar Mahlaoui
- Pediatric Hematology-Immunology and Rheumatology Unit, Necker-Enfants malades University Hospital, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France
- French National Reference Center for Primary Immune Deficiencies (CEREDIH), Necker-Enfants malades University Hospital, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France
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Milella MS, Geminiani M, Trezza A, Visibelli A, Braconi D, Santucci A. Alkaptonuria: From Molecular Insights to a Dedicated Digital Platform. Cells 2024; 13:1072. [PMID: 38920699 PMCID: PMC11201470 DOI: 10.3390/cells13121072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 06/17/2024] [Accepted: 06/18/2024] [Indexed: 06/27/2024] Open
Abstract
Alkaptonuria (AKU) is a genetic disorder that affects connective tissues of several body compartments causing cartilage degeneration, tendon calcification, heart problems, and an invalidating, early-onset form of osteoarthritis. The molecular mechanisms underlying AKU involve homogentisic acid (HGA) accumulation in cells and tissues. HGA is highly reactive, able to modify several macromolecules, and activates different pathways, mostly involved in the onset and propagation of oxidative stress and inflammation, with consequences spreading from the microscopic to the macroscopic level leading to irreversible damage. Gaining a deeper understanding of AKU molecular mechanisms may provide novel possible therapeutical approaches to counteract disease progression. In this review, we first describe inflammation and oxidative stress in AKU and discuss similarities with other more common disorders. Then, we focus on HGA reactivity and AKU molecular mechanisms. We finally describe a multi-purpose digital platform, named ApreciseKUre, created to facilitate data collection, integration, and analysis of AKU-related data.
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Affiliation(s)
- Maria Serena Milella
- ONE-HEALTH Lab, Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy; (M.S.M.); (A.T.); (A.V.); (D.B.); (A.S.)
| | - Michela Geminiani
- ONE-HEALTH Lab, Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy; (M.S.M.); (A.T.); (A.V.); (D.B.); (A.S.)
- SienabioACTIVE-SbA, Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Alfonso Trezza
- ONE-HEALTH Lab, Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy; (M.S.M.); (A.T.); (A.V.); (D.B.); (A.S.)
| | - Anna Visibelli
- ONE-HEALTH Lab, Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy; (M.S.M.); (A.T.); (A.V.); (D.B.); (A.S.)
| | - Daniela Braconi
- ONE-HEALTH Lab, Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy; (M.S.M.); (A.T.); (A.V.); (D.B.); (A.S.)
| | - Annalisa Santucci
- ONE-HEALTH Lab, Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy; (M.S.M.); (A.T.); (A.V.); (D.B.); (A.S.)
- SienabioACTIVE-SbA, Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
- ARTES 4.0, Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
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Cortial L, Montero V, Tourlet S, Del Bano J, Blin O. Artificial intelligence in drug repurposing for rare diseases: a mini-review. Front Med (Lausanne) 2024; 11:1404338. [PMID: 38841574 PMCID: PMC11150798 DOI: 10.3389/fmed.2024.1404338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 04/29/2024] [Indexed: 06/07/2024] Open
Abstract
Drug repurposing, the process of identifying new uses for existing drugs beyond their original indications, offers significant advantages in terms of reduced development time and costs, particularly in addressing unmet medical needs in rare diseases. Artificial intelligence (AI) has emerged as a transformative force in healthcare, and by leveraging AI technologies, researchers aim to overcome some of the challenges associated with rare diseases. This review presents concrete case studies, as well as pre-existing platforms, initiatives, and companies that demonstrate the application of AI for drug repurposing in rare diseases. Despite representing a modest part of the literature compared to other diseases such as COVID-19 or cancer, the growing interest, and investment in AI for drug repurposing in rare diseases underscore its potential to accelerate treatment availability for patients with unmet medical needs.
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Affiliation(s)
- Lucas Cortial
- OrphanDEV FCRIN Reference Network, Aix Marseille Univ, APHM, INSERM, Inst Neurosci Syst, CHU Timone, Marseille, France
- Thelonius Mind, Marseille, France
| | - Vincent Montero
- OrphanDEV FCRIN Reference Network, Aix Marseille Univ, APHM, INSERM, Inst Neurosci Syst, CHU Timone, Marseille, France
- Thelonius Mind, Marseille, France
| | | | | | - Olivier Blin
- OrphanDEV FCRIN Reference Network, Aix Marseille Univ, APHM, INSERM, Inst Neurosci Syst, CHU Timone, Marseille, France
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Raycheva R, Kostadinov K, Mitova E, Iskrov G, Stefanov G, Vakevainen M, Elomaa K, Man YS, Gross E, Zschüntzsch J, Röttger R, Stefanov R. Landscape analysis of available European data sources amenable for machine learning and recommendations on usability for rare diseases screening. Orphanet J Rare Dis 2024; 19:147. [PMID: 38582900 PMCID: PMC10998425 DOI: 10.1186/s13023-024-03162-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 03/30/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND Patient registries and databases are essential tools for advancing clinical research in the area of rare diseases, as well as for enhancing patient care and healthcare planning. The primary aim of this study is a landscape analysis of available European data sources amenable to machine learning (ML) and their usability for Rare Diseases screening, in terms of findable, accessible, interoperable, reusable(FAIR), legal, and business considerations. Second, recommendations will be proposed to provide a better understanding of the health data ecosystem. METHODS In the period of March 2022 to December 2022, a cross-sectional study using a semi-structured questionnaire was conducted among potential respondents, identified as main contact person of a health-related databases. The design of the self-completed questionnaire survey instrument was based on information drawn from relevant scientific publications, quantitative and qualitative research, and scoping review on challenges in mapping European rare disease (RD) databases. To determine database characteristics associated with the adherence to the FAIR principles, legal and business aspects of database management Bayesian models were fitted. RESULTS In total, 330 unique replies were processed and analyzed, reflecting the same number of distinct databases (no duplicates included). In terms of geographical scope, we observed 24.2% (n = 80) national, 10.0% (n = 33) regional, 8.8% (n = 29) European, and 5.5% (n = 18) international registries coordinated in Europe. Over 80.0% (n = 269) of the databases were still active, with approximately 60.0% (n = 191) established after the year 2000 and 71.0% last collected new data in 2022. Regarding their geographical scope, European registries were associated with the highest overall FAIR adherence, while registries with regional and "other" geographical scope were ranked at the bottom of the list with the lowest proportion. Responders' willingness to share data as a contribution to the goals of the Screen4Care project was evaluated at the end of the survey. This question was completed by 108 respondents; however, only 18 of them (16.7%) expressed a direct willingness to contribute to the project by sharing their databases. Among them, an equal split between pro-bono and paid services was observed. CONCLUSIONS The most important results of our study demonstrate not enough sufficient FAIR principles adherence and low willingness of the EU health databases to share patient information, combined with some legislation incapacities, resulting in barriers to the secondary use of data.
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Affiliation(s)
- Ralitsa Raycheva
- Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria.
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria.
| | - Kostadin Kostadinov
- Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
| | - Elena Mitova
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
| | - Georgi Iskrov
- Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
| | - Georgi Stefanov
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
| | - Merja Vakevainen
- Pfizer Biopharmaceuticals Group, Medical Affairs, Helsinki, Finland
| | | | - Yuen-Sum Man
- Global Medical Affairs Rare Disease, Novo Nordisk Health Care AG, Zurich, Switzerland
| | - Edith Gross
- EURORDIS - Rare Diseases Europe, 96 Rue Didot, Paris, 75014, France
| | - Jana Zschüntzsch
- Department of Neurology, University Medical Center, Göttingen, Germany
| | - Richard Röttger
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Rumen Stefanov
- Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
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Cohen AM, Kaner J, Miller R, Kopesky JW, Hersh W. Automatically pre-screening patients for the rare disease aromatic l-amino acid decarboxylase deficiency using knowledge engineering, natural language processing, and machine learning on a large EHR population. J Am Med Inform Assoc 2024; 31:692-704. [PMID: 38134953 PMCID: PMC10873832 DOI: 10.1093/jamia/ocad244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 11/28/2023] [Accepted: 12/01/2023] [Indexed: 12/24/2023] Open
Abstract
OBJECTIVES Electronic health record (EHR) data may facilitate the identification of rare diseases in patients, such as aromatic l-amino acid decarboxylase deficiency (AADCd), an autosomal recessive disease caused by pathogenic variants in the dopa decarboxylase gene. Deficiency of the AADC enzyme results in combined severe reductions in monoamine neurotransmitters: dopamine, serotonin, epinephrine, and norepinephrine. This leads to widespread neurological complications affecting motor, behavioral, and autonomic function. The goal of this study was to use EHR data to identify previously undiagnosed patients who may have AADCd without available training cases for the disease. MATERIALS AND METHODS A multiple symptom and related disease annotated dataset was created and used to train individual concept classifiers on annotated sentence data. A multistep algorithm was then used to combine concept predictions into a single patient rank value. RESULTS Using an 8000-patient dataset that the algorithms had not seen before ranking, the top and bottom 200 ranked patients were manually reviewed for clinical indications of performing an AADCd diagnostic screening test. The top-ranked patients were 22.5% positively assessed for diagnostic screening, with 0% for the bottom-ranked patients. This result is statistically significant at P < .0001. CONCLUSION This work validates the approach that large-scale rare-disease screening can be accomplished by combining predictions for relevant individual symptoms and related conditions which are much more common and for which training data is easier to create.
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Affiliation(s)
- Aaron M Cohen
- Department of Medical Informatics and Clinical Epidemiology, School of Medicine, Oregon Health & Science University, Portland, OR 97239, United States
| | - Jolie Kaner
- Department of Medical Informatics and Clinical Epidemiology, School of Medicine, Oregon Health & Science University, Portland, OR 97239, United States
| | - Ryan Miller
- PTC Therapeutics, South Plainfield, NJ 07080, United States
| | | | - William Hersh
- Department of Medical Informatics and Clinical Epidemiology, School of Medicine, Oregon Health & Science University, Portland, OR 97239, United States
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9
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S Alshuhri M, Al-Musawi SG, Al-Alwany AA, Uinarni H, Rasulova I, Rodrigues P, Alkhafaji AT, Alshanberi AM, Alawadi AH, Abbas AH. Artificial intelligence in cancer diagnosis: Opportunities and challenges. Pathol Res Pract 2024; 253:154996. [PMID: 38118214 DOI: 10.1016/j.prp.2023.154996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 11/20/2023] [Accepted: 11/27/2023] [Indexed: 12/22/2023]
Abstract
Since cancer is one of the world's top causes of death, early diagnosis is critical to improving patient outcomes. Artificial intelligence (AI) has become a viable technique for cancer diagnosis by using machine learning algorithms to examine large volumes of data for accurate and efficient diagnosis. AI has the potential to alter the way cancer is detected fundamentally. Still, it has several disadvantages, such as requiring a large amount of data, technological limitations, and ethical concerns. This overview looks at the possibilities and restrictions of AI in cancer detection, as well as current applications and possible future developments. We can better understand how to use AI to improve patient outcomes and reduce cancer mortality rates by looking at its potential for cancer detection.
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Affiliation(s)
- Mohammed S Alshuhri
- Radiology and Medical Imaging Department, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Kharj, Saudi Arabia
| | | | | | - Herlina Uinarni
- Department of Anatomy, School of Medicine and Health Sciences Atma Jaya Catholic University of Indonesia, Indonesia; Radiology department of Pantai Indah Kapuk Hospital Jakarta, Jakarta, Indonesia.
| | - Irodakhon Rasulova
- School of Humanities, Natural & Social Sciences, New Uzbekistan University, 54 Mustaqillik Ave., Tashkent 100007, Uzbekistan; Department of Public Health, Samarkand State Medical University, Amir Temur Street 18, Samarkand, Uzbekistan
| | - Paul Rodrigues
- Department of Computer Engineering, College of Computer Science, King Khalid University, Al-Faraa, Abha, Asir, Kingdom of Saudi Arabia
| | | | - Asim Muhammed Alshanberi
- Department of Community Medicine & Pilgrim Healthcare, Umm Alqura University, Makkah 24382, Saudi Arabia; General Medicine Practice Program, Batterjee Medical College, Jeddah 21442, Saudi Arabia
| | - Ahmed Hussien Alawadi
- College of Technical Engineering, the Islamic University, Najaf, Iraq; College of Technical Engineering, the Islamic University of Al Diwaniyah, Iraq; College of Technical Engineering, the Islamic University of Babylon, Iraq
| | - Ali Hashim Abbas
- College of Technical Engineering, Imam Ja'afar Al-Sadiq University, Al-Muthanna 66002, Iraq
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10
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Biswas A, Kumari A, Gaikwad DS, Pandey DK. Revolutionizing Biological Science: The Synergy of Genomics in Health, Bioinformatics, Agriculture, and Artificial Intelligence. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2023; 27:550-569. [PMID: 38100404 DOI: 10.1089/omi.2023.0197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
With climate emergency, COVID-19, and the rise of planetary health scholarship, the binary of human and ecosystem health has been deeply challenged. The interdependence of human and nonhuman animal health is increasingly acknowledged and paving the way for new frontiers in integrative biology. The convergence of genomics in health, bioinformatics, agriculture, and artificial intelligence (AI) has ushered in a new era of possibilities and applications. However, the sheer volume of genomic/multiomics big data generated also presents formidable sociotechnical challenges in extracting meaningful biological, planetary health and ecological insights. Over the past few years, AI-guided bioinformatics has emerged as a powerful tool for managing, analyzing, and interpreting complex biological datasets. The advances in AI, particularly in machine learning and deep learning, have been transforming the fields of genomics, planetary health, and agriculture. This article aims to unpack and explore the formidable range of possibilities and challenges that result from such transdisciplinary integration, and emphasizes its radically transformative potential for human and ecosystem health. The integration of these disciplines is also driving significant advancements in precision medicine and personalized health care. This presents an unprecedented opportunity to deepen our understanding of complex biological systems and advance the well-being of all life in planetary ecosystems. Notwithstanding in mind its sociotechnical, ethical, and critical policy challenges, the integration of genomics, multiomics, planetary health, and agriculture with AI-guided bioinformatics opens up vast opportunities for transnational collaborative efforts, data sharing, analysis, valorization, and interdisciplinary innovations in life sciences and integrative biology.
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Affiliation(s)
- Aakanksha Biswas
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, India
| | - Aditi Kumari
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, India
| | - D S Gaikwad
- Amity Institute of Organic Agriculture, Amity University, Noida, India
| | - Dhananjay K Pandey
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, India
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11
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Addad VV, Palma LMP, Vaisbich MH, Pacheco Barbosa AM, da Rocha NC, de Almeida Cardoso MM, de Almeida JTC, de Paula de Sordi MA, Machado-Rugolo J, Arantes LF, de Andrade LGM. A comprehensive model for assessing and classifying patients with thrombotic microangiopathy: the TMA-INSIGHT score. Thromb J 2023; 21:119. [PMID: 37993892 PMCID: PMC10664252 DOI: 10.1186/s12959-023-00564-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 11/13/2023] [Indexed: 11/24/2023] Open
Abstract
BACKGROUND Thrombotic Microangiopathy (TMA) is a syndrome characterized by the presence of anemia, thrombocytopenia and organ damage and has multiple etiologies. The primary aim is to develop an algorithm to classify TMA (TMA-INSIGHT score). METHODS This was a single-center retrospective cohort study including hospitalized patients with TMA at a single center. We included all consecutive patients diagnosed with TMA between 2012 and 2021. TMA was defined based on the presence of anemia (hemoglobin level < 10 g/dL) and thrombocytopenia (platelet count < 150,000/µL), signs of hemolysis, and organ damage. We classified patients in eight categories: infections; Malignant Hypertension; Transplant; Malignancy; Pregnancy; Thrombotic Thrombocytopenic Purpura (TTP); Shiga toxin-mediated hemolytic uremic syndrome (STEC-SHU) and Complement Mediated TMA (aHUS). We fitted a model to classify patients using clinical characteristics, biochemical exams, and mean arterial pressure at presentation. RESULTS We retrospectively retrieved TMA phenotypes using automatic strategies in electronic health records in almost 10 years (n = 2407). Secondary TMA was found in 97.5% of the patients. Primary TMA was found in 2.47% of the patients (TTP and aHUS). The best model was LightGBM with accuracy of 0.979, and multiclass ROC-AUC of 0.966. The predictions had higher accuracy in most TMA classes, although the confidence was lower in aHUS and STEC-HUS cases. CONCLUSION Secondary conditions were the most common etiologies of TMA. We retrieved comorbidities, associated conditions, and mean arterial pressure to fit a model to predict TMA and define TMA phenotypic characteristics. This is the first multiclass model to predict TMA including primary and secondary conditions.
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Affiliation(s)
- Vanessa Vilani Addad
- Department of Internal Medicine - UNESP, Univ Estadual Paulista, Rubião Jr, s/n, Botucatu/SP, 18618-687, Brazil
| | - Lilian Monteiro Pereira Palma
- Department of Pediatrics, Universidade Estadual de Campinas, R. Tessália Vieira de Camargo, 126 - Cidade Universitária, Campinas/SP, 13083-887, Brazil
| | - Maria Helena Vaisbich
- Pediatric Nephrology Service, Child Institute, University of São Paulo, Av. Dr. Enéas Carvalho de Aguiar, 647, São Paulo, SP, 05403-000, Brazil
| | | | - Naila Camila da Rocha
- Department of Internal Medicine - UNESP, Univ Estadual Paulista, Rubião Jr, s/n, Botucatu/SP, 18618-687, Brazil
| | | | | | | | - Juliana Machado-Rugolo
- Health Technology Assessment Center of Hospital das Clínicas - HCFMB, Botucatu, SP, Brazil
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12
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Shelke YP, Badge AK, Bankar NJ. Applications of Artificial Intelligence in Microbial Diagnosis. Cureus 2023; 15:e49366. [PMID: 38146579 PMCID: PMC10749263 DOI: 10.7759/cureus.49366] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 11/24/2023] [Indexed: 12/27/2023] Open
Abstract
The diagnosis is an important factor in healthcare care, and it is essential to identify microorganisms that cause infections and diseases. The application of artificial intelligence (AI) systems can improve disease management, drug development, antibiotic resistance prediction, and epidemiological monitoring in the field of microbial diagnosis. AI systems can quickly and accurately detect infections, including new and drug-resistant strains, and enable early detection of antibiotic resistance and improved diagnostic techniques. The application of AI in bacterial diagnosis focuses on the speed, precision, and identification of pathogens and the ability to predict antibiotic resistance.
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Affiliation(s)
- Yogendra P Shelke
- Microbiology, Bhaktshreshtha Kamalakarpant Laxmanrao Walawalkar Rural Medical College, Ratnagiri, IND
| | - Ankit K Badge
- Microbiology, Datta Meghe Medical College, Datta Meghe Institute of Higher Education and Research (Deemed to Be University), Wardha, IND
| | - Nandkishor J Bankar
- Microbiology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research (Deemed to Be University), Wardha, IND
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13
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Wojtara MS, Kang J, Zaman M. Congenital Telangiectatic Erythema: Scoping Review. JMIR DERMATOLOGY 2023; 6:e48413. [PMID: 37796556 PMCID: PMC10587801 DOI: 10.2196/48413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 08/19/2023] [Accepted: 09/15/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND Congenital telangiectatic erythema (CTE), also known as Bloom syndrome, is a rare autosomal recessive disorder characterized by below-average height, a narrow face, a red skin rash occurring on sun-exposed areas of the body, and an increased risk of cancer. CTE is one of many genodermatoses and photodermatoses associated with defects in DNA repair. CTE is caused by a mutation occurring in the BLM gene, which causes abnormal breaks in chromosomes. OBJECTIVE We aimed to analyze the existing literature on CTE to provide additional insight into its heredity, the spectrum of clinical presentations, and the management of this disorder. In addition, the gaps in current research and the use of artificial intelligence to streamline clinical diagnosis and the management of CTE are outlined. METHODS A literature search was conducted on PubMed, DOAJ, and Scopus using search terms such as "congenital telangiectatic erythema," "bloom syndrome," and "bloom-torre-machacek." Due to limited current literature, studies published from January 2000 to January 2023 were considered for this review. A total of 49 sources from the literature were analyzed. RESULTS Through this scoping review, the researchers were able to identify several publications focusing on Bloom syndrome. Some common subject areas included the heredity of CTE, clinical presentations of CTE, and management of CTE. In addition, the literature on rare diseases shows the potential advancements in understanding and treatment with artificial intelligence. Future studies should address the causes of heterogeneity in presentation and examine potential therapeutic candidates for CTE and similarly presenting syndromes. CONCLUSIONS This review illuminated current advances in potential molecular targets or causative pathways in the development of CTE as well as clinical features including erythema, increased cancer risk, and growth abnormalities. Future studies should continue to explore innovations in this space, especially in regard to the use of artificial intelligence, including machine learning and deep learning, for the diagnosis and clinical management of rare diseases such as CTE.
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Affiliation(s)
- Magda Sara Wojtara
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Jayne Kang
- Department of Health Sciences, Queen's University, Kingston, ON, Canada
| | - Mohammed Zaman
- Department of Biology, Stony Brook University, Stony Brook, NY, United States
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14
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Abdallah S, Sharifa M, I Kh Almadhoun MK, Khawar MM, Shaikh U, Balabel KM, Saleh I, Manzoor A, Mandal AK, Ekomwereren O, Khine WM, Oyelaja OT. The Impact of Artificial Intelligence on Optimizing Diagnosis and Treatment Plans for Rare Genetic Disorders. Cureus 2023; 15:e46860. [PMID: 37954711 PMCID: PMC10636514 DOI: 10.7759/cureus.46860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/11/2023] [Indexed: 11/14/2023] Open
Abstract
Rare genetic disorders (RDs), characterized by their low prevalence and diagnostic complexities, present significant challenges to healthcare systems. This article explores the transformative impact of artificial intelligence (AI) and machine learning (ML) in addressing these challenges. It emphasizes the need for accurate and early diagnosis of RDs, often hindered by genetic and clinical heterogeneity. This article discusses how AI and ML are reshaping healthcare, providing examples of their effectiveness in disease diagnosis, prognosis, image analysis, and drug repurposing. It highlights AI's ability to efficiently analyze extensive datasets and expedite diagnosis, showcasing case studies like Face2Gene. Furthermore, the article explores how AI tailors treatment plans for RDs, leveraging ML and deep learning (DL) to create personalized therapeutic regimens. It emphasizes AI's role in drug discovery, including the identification of potential candidates for rare disease treatments. Challenges and limitations related to AI in healthcare, including ethical, legal, technical, and human aspects, are addressed. This article underscores the importance of data ethics, privacy, and algorithmic fairness, as well as the need for standardized evaluation techniques and transparency in AI research. It highlights second-generation AI systems that prioritize patient-centric care, efficient patient recruitment for clinical trials, and the significance of high-quality data. The integration of AI with telemedicine, the growth of health databases, and the potential for personalized therapeutic recommendations are identified as promising directions for the field. In summary, this article provides a comprehensive exploration of how AI and ML are revolutionizing the diagnosis and treatment of RDs, addressing challenges while considering ethical implications in this rapidly evolving healthcare landscape.
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Affiliation(s)
- Shenouda Abdallah
- Surgery, Jaber Al Ahmad Al Jaber Al Sabah Hospital, Kuwait City, KWT
| | | | | | | | - Unzla Shaikh
- Internal Medicine, Liaquat University of Medical and Health Sciences, Hyderabad, PAK
| | | | - Inam Saleh
- Pediatrics, University of Kentucky College of Medicine, Lexington, USA
| | - Amima Manzoor
- Internal Medicine, Jinnah Sindh Medical University, Karachi, PAK
| | - Arun Kumar Mandal
- General Medicine, Mahawai Basic Hospital/The Oda Foundation, Kalikot, NPL
- Medicine, Manipal College of Medical Sciences, Pokhara, NPL
| | - Osatohanmwen Ekomwereren
- Trauma and Orthopaedics, Royal Shrewsbury Hospital, Shrewsbury and Telford Hospital NHS Trust, Shrewsbury, GBR
| | - Wai Mon Khine
- Internal Medicine, Caribbean Medical School, St. Georges, GRD
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15
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Aradhya S, Facio FM, Metz H, Manders T, Colavin A, Kobayashi Y, Nykamp K, Johnson B, Nussbaum RL. Applications of artificial intelligence in clinical laboratory genomics. AMERICAN JOURNAL OF MEDICAL GENETICS. PART C, SEMINARS IN MEDICAL GENETICS 2023; 193:e32057. [PMID: 37507620 DOI: 10.1002/ajmg.c.32057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/13/2023] [Accepted: 07/19/2023] [Indexed: 07/30/2023]
Abstract
The transition from analog to digital technologies in clinical laboratory genomics is ushering in an era of "big data" in ways that will exceed human capacity to rapidly and reproducibly analyze those data using conventional approaches. Accurately evaluating complex molecular data to facilitate timely diagnosis and management of genomic disorders will require supportive artificial intelligence methods. These are already being introduced into clinical laboratory genomics to identify variants in DNA sequencing data, predict the effects of DNA variants on protein structure and function to inform clinical interpretation of pathogenicity, link phenotype ontologies to genetic variants identified through exome or genome sequencing to help clinicians reach diagnostic answers faster, correlate genomic data with tumor staging and treatment approaches, utilize natural language processing to identify critical published medical literature during analysis of genomic data, and use interactive chatbots to identify individuals who qualify for genetic testing or to provide pre-test and post-test education. With careful and ethical development and validation of artificial intelligence for clinical laboratory genomics, these advances are expected to significantly enhance the abilities of geneticists to translate complex data into clearly synthesized information for clinicians to use in managing the care of their patients at scale.
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Affiliation(s)
- Swaroop Aradhya
- Invitae Corporation, San Francisco, California, USA
- Adjunct Clinical Faculty, Department of Pathology, Stanford University School of Medicine, Stanford, California, USA
| | | | - Hillery Metz
- Invitae Corporation, San Francisco, California, USA
| | - Toby Manders
- Invitae Corporation, San Francisco, California, USA
| | | | | | - Keith Nykamp
- Invitae Corporation, San Francisco, California, USA
| | | | - Robert L Nussbaum
- Invitae Corporation, San Francisco, California, USA
- Volunteer Faculty, School of Medicine, University of California San Francisco, San Francisco, California, USA
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16
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Molnar MJ, Molnar V. AI-based tools for the diagnosis and treatment of rare neurological disorders. Nat Rev Neurol 2023:10.1038/s41582-023-00841-y. [PMID: 37400549 DOI: 10.1038/s41582-023-00841-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2023]
Affiliation(s)
- Maria J Molnar
- Institute of Genomic Medicine and Rare Disorders Semmelweis University, Budapest, Hungary.
- ELKH-SE Multiomic Neurodegenerative Research Group, Budapest, Hungary.
| | - Viktor Molnar
- Institute of Genomic Medicine and Rare Disorders Semmelweis University, Budapest, Hungary
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