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Hampel H, Gao P, Cummings J, Toschi N, Thompson PM, Hu Y, Cho M, Vergallo A. The foundation and architecture of precision medicine in neurology and psychiatry. Trends Neurosci 2023; 46:176-198. [PMID: 36642626 PMCID: PMC10720395 DOI: 10.1016/j.tins.2022.12.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 11/18/2022] [Accepted: 12/14/2022] [Indexed: 01/15/2023]
Abstract
Neurological and psychiatric diseases have high degrees of genetic and pathophysiological heterogeneity, irrespective of clinical manifestations. Traditional medical paradigms have focused on late-stage syndromic aspects of these diseases, with little consideration of the underlying biology. Advances in disease modeling and methodological design have paved the way for the development of precision medicine (PM), an established concept in oncology with growing attention from other medical specialties. We propose a PM architecture for central nervous system diseases built on four converging pillars: multimodal biomarkers, systems medicine, digital health technologies, and data science. We discuss Alzheimer's disease (AD), an area of significant unmet medical need, as a case-in-point for the proposed framework. AD can be seen as one of the most advanced PM-oriented disease models and as a compelling catalyzer towards PM-oriented neuroscience drug development and advanced healthcare practice.
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Affiliation(s)
- Harald Hampel
- Alzheimer's Disease & Brain Health, Eisai Inc., Nutley, NJ, USA.
| | - Peng Gao
- Alzheimer's Disease & Brain Health, Eisai Inc., Nutley, NJ, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas (UNLV), Las Vegas, NV, USA
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy; Athinoula A. Martinos Center for Biomedical Imaging and Harvard Medical School, Boston, MA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yan Hu
- Alzheimer's Disease & Brain Health, Eisai Inc., Nutley, NJ, USA
| | - Min Cho
- Alzheimer's Disease & Brain Health, Eisai Inc., Nutley, NJ, USA
| | - Andrea Vergallo
- Alzheimer's Disease & Brain Health, Eisai Inc., Nutley, NJ, USA
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Nilsen P, Svedberg P, Nygren J, Frideros M, Johansson J, Schueller S. Accelerating the impact of artificial intelligence in mental healthcare through implementation science. IMPLEMENTATION RESEARCH AND PRACTICE 2022; 3:26334895221112033. [PMID: 37091110 PMCID: PMC9924259 DOI: 10.1177/26334895221112033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Background The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps regarding how to implement and best use AI to add value to mental healthcare services, providers, and consumers. The aim of this paper is to identify challenges and opportunities for AI use in mental healthcare and to describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. Methods The paper is based on a selective review of articles concerning AI in mental healthcare and implementation science. Results Research in implementation science has established the importance of considering and planning for implementation from the start, the progression of implementation through different stages, and the appreciation of determinants at multiple levels. Determinant frameworks and implementation theories have been developed to understand and explain how different determinants impact on implementation. AI research should explore the relevance of these determinants for AI implementation. Implementation strategies to support AI implementation must address determinants specific to AI implementation in mental health. There might also be a need to develop new theoretical approaches or augment and recontextualize existing ones. Implementation outcomes may have to be adapted to be relevant in an AI implementation context. Conclusion Knowledge derived from implementation science could provide an important starting point for research on implementation of AI in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research. However, when taking advantage of the existing knowledge basis, it is important to also be explorative and study AI implementation in health and mental healthcare as a new phenomenon in its own right since implementing AI may differ in various ways from implementing evidence-based practices in terms of what implementation determinants, strategies, and outcomes are most relevant. Plain Language Summary: The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps concerning how to implement and best use AI to add value to mental healthcare services, providers, and consumers. This paper is based on a selective review of articles concerning AI in mental healthcare and implementation science, with the aim to identify challenges and opportunities for the use of AI in mental healthcare and describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. AI offers opportunities for identifying the patients most in need of care or the interventions that might be most appropriate for a given population or individual. AI also offers opportunities for supporting a more reliable diagnosis of psychiatric disorders and ongoing monitoring and tailoring during the course of treatment. However, AI implementation challenges exist at organizational/policy, individual, and technical levels, making it relevant to draw on implementation science knowledge for understanding and facilitating implementation of AI in mental healthcare. Knowledge derived from implementation science could provide an important starting point for research on AI implementation in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research.
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Affiliation(s)
| | - Petra Svedberg
- Halmstad University School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Jens Nygren
- Halmstad University School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | | | | | - Stephen Schueller
- Psychological Science, University of California Irvine, Irvine, CA, USA
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Nuevas tecnologías digitales en la práctica médica. Med Clin (Barc) 2020; 154:257-259. [DOI: 10.1016/j.medcli.2019.07.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 07/10/2019] [Accepted: 07/15/2019] [Indexed: 11/22/2022]
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McInnis M, Bertram H. Phenomenology 2.0. Bipolar Disord 2019; 21:660-661. [PMID: 31301683 DOI: 10.1111/bdi.12811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Melvin McInnis
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan
| | - Holli Bertram
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan
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Glicksberg BS, Johnson KW, Dudley JT. The next generation of precision medicine: observational studies, electronic health records, biobanks and continuous monitoring. Hum Mol Genet 2019; 27:R56-R62. [PMID: 29659828 DOI: 10.1093/hmg/ddy114] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Accepted: 03/27/2018] [Indexed: 02/06/2023] Open
Abstract
Precision medicine can utilize new techniques in order to more effectively translate research findings into clinical practice. In this article, we first explore the limitations of traditional study designs, which stem from (to name a few): massive cost for the assembly of large patient cohorts; non-representative patient data; and the astounding complexity of human biology. Second, we propose that harnessing electronic health records and mobile device biometrics coupled to longitudinal data may prove to be a solution to many of these problems by capturing a 'real world' phenotype. We envision that future biomedical research utilizing more precise approaches to patient care will utilize continuous and longitudinal data sources.
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Affiliation(s)
- Benjamin S Glicksberg
- Institute for Next Generation Healthcare Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York City, NY 10029, USA.,Institute for Computational Health Sciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Kipp W Johnson
- Institute for Next Generation Healthcare Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York City, NY 10029, USA
| | - Joel T Dudley
- Institute for Next Generation Healthcare Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York City, NY 10029, USA
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Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019; 25:44-56. [PMID: 30617339 DOI: 10.1038/s41591-018-0300-7] [Citation(s) in RCA: 2086] [Impact Index Per Article: 417.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Accepted: 11/12/2018] [Indexed: 11/08/2022]
Abstract
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.
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Affiliation(s)
- Eric J Topol
- Department of Molecular Medicine, Scripps Research, La Jolla, CA, USA.
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Reinertsen E, Clifford GD. A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses. Physiol Meas 2018; 39:05TR01. [PMID: 29671754 PMCID: PMC5995114 DOI: 10.1088/1361-6579/aabf64] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Physiological, behavioral, and psychological changes associated with neuropsychiatric illness are reflected in several related signals, including actigraphy, location, word sentiment, voice tone, social activity, heart rate, and responses to standardized questionnaires. These signals can be passively monitored using sensors in smartphones, wearable accelerometers, Holter monitors, and multimodal sensing approaches that fuse multiple data types. Connection of these devices to the internet has made large scale studies feasible and is enabling a revolution in neuropsychiatric monitoring. Currently, evaluation and diagnosis of neuropsychiatric disorders relies on clinical visits, which are infrequent and out of the context of a patient's home environment. Moreover, the demand for clinical care far exceeds the supply of providers. The growing prevalence of context-aware and physiologically relevant digital sensors in consumer technology could help address these challenges, enable objective indexing of patient severity, and inform rapid adjustment of treatment in real-time. Here we review recent studies utilizing such sensors in the context of neuropsychiatric illnesses including stress and depression, bipolar disorder, schizophrenia, post traumatic stress disorder, Alzheimer's disease, and Parkinson's disease.
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Affiliation(s)
- Erik Reinertsen
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America
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