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Sirocchi C, Bogliolo A, Montagna S. Medical-informed machine learning: integrating prior knowledge into medical decision systems. BMC Med Inform Decis Mak 2024; 24:186. [PMID: 38943085 PMCID: PMC11212227 DOI: 10.1186/s12911-024-02582-4] [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: 01/26/2024] [Accepted: 06/20/2024] [Indexed: 07/01/2024] Open
Abstract
BACKGROUND Clinical medicine offers a promising arena for applying Machine Learning (ML) models. However, despite numerous studies employing ML in medical data analysis, only a fraction have impacted clinical care. This article underscores the importance of utilising ML in medical data analysis, recognising that ML alone may not adequately capture the full complexity of clinical data, thereby advocating for the integration of medical domain knowledge in ML. METHODS The study conducts a comprehensive review of prior efforts in integrating medical knowledge into ML and maps these integration strategies onto the phases of the ML pipeline, encompassing data pre-processing, feature engineering, model training, and output evaluation. The study further explores the significance and impact of such integration through a case study on diabetes prediction. Here, clinical knowledge, encompassing rules, causal networks, intervals, and formulas, is integrated at each stage of the ML pipeline, resulting in a spectrum of integrated models. RESULTS The findings highlight the benefits of integration in terms of accuracy, interpretability, data efficiency, and adherence to clinical guidelines. In several cases, integrated models outperformed purely data-driven approaches, underscoring the potential for domain knowledge to enhance ML models through improved generalisation. In other cases, the integration was instrumental in enhancing model interpretability and ensuring conformity with established clinical guidelines. Notably, knowledge integration also proved effective in maintaining performance under limited data scenarios. CONCLUSIONS By illustrating various integration strategies through a clinical case study, this work provides guidance to inspire and facilitate future integration efforts. Furthermore, the study identifies the need to refine domain knowledge representation and fine-tune its contribution to the ML model as the two main challenges to integration and aims to stimulate further research in this direction.
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Affiliation(s)
- Christel Sirocchi
- Department of Pure and Applied Sciences, University of Urbino, Piazza della Repubblica, 13, Urbino, 61029, Italy.
| | - Alessandro Bogliolo
- Department of Pure and Applied Sciences, University of Urbino, Piazza della Repubblica, 13, Urbino, 61029, Italy
| | - Sara Montagna
- Department of Pure and Applied Sciences, University of Urbino, Piazza della Repubblica, 13, Urbino, 61029, Italy
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Khan SD, Hoodbhoy Z, Raja MHR, Kim JY, Hogg HDJ, Manji AAA, Gulamali F, Hasan A, Shaikh A, Tajuddin S, Khan NS, Patel MR, Balu S, Samad Z, Sendak MP. Frameworks for procurement, integration, monitoring, and evaluation of artificial intelligence tools in clinical settings: A systematic review. PLOS DIGITAL HEALTH 2024; 3:e0000514. [PMID: 38809946 PMCID: PMC11135672 DOI: 10.1371/journal.pdig.0000514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 04/18/2024] [Indexed: 05/31/2024]
Abstract
Research on the applications of artificial intelligence (AI) tools in medicine has increased exponentially over the last few years but its implementation in clinical practice has not seen a commensurate increase with a lack of consensus on implementing and maintaining such tools. This systematic review aims to summarize frameworks focusing on procuring, implementing, monitoring, and evaluating AI tools in clinical practice. A comprehensive literature search, following PRSIMA guidelines was performed on MEDLINE, Wiley Cochrane, Scopus, and EBSCO databases, to identify and include articles recommending practices, frameworks or guidelines for AI procurement, integration, monitoring, and evaluation. From the included articles, data regarding study aim, use of a framework, rationale of the framework, details regarding AI implementation involving procurement, integration, monitoring, and evaluation were extracted. The extracted details were then mapped on to the Donabedian Plan, Do, Study, Act cycle domains. The search yielded 17,537 unique articles, out of which 47 were evaluated for inclusion based on their full texts and 25 articles were included in the review. Common themes extracted included transparency, feasibility of operation within existing workflows, integrating into existing workflows, validation of the tool using predefined performance indicators and improving the algorithm and/or adjusting the tool to improve performance. Among the four domains (Plan, Do, Study, Act) the most common domain was Plan (84%, n = 21), followed by Study (60%, n = 15), Do (52%, n = 13), & Act (24%, n = 6). Among 172 authors, only 1 (0.6%) was from a low-income country (LIC) and 2 (1.2%) were from lower-middle-income countries (LMICs). Healthcare professionals cite the implementation of AI tools within clinical settings as challenging owing to low levels of evidence focusing on integration in the Do and Act domains. The current healthcare AI landscape calls for increased data sharing and knowledge translation to facilitate common goals and reap maximum clinical benefit.
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Affiliation(s)
- Sarim Dawar Khan
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Zahra Hoodbhoy
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | | | - Jee Young Kim
- Duke Institute for Health Innovation, Duke University School of Medicine, Durham, North Carolina, United States
| | - Henry David Jeffry Hogg
- Population Health Science Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Afshan Anwar Ali Manji
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Freya Gulamali
- Duke Institute for Health Innovation, Duke University School of Medicine, Durham, North Carolina, United States
| | - Alifia Hasan
- Duke Institute for Health Innovation, Duke University School of Medicine, Durham, North Carolina, United States
| | - Asim Shaikh
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Salma Tajuddin
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Nida Saddaf Khan
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Manesh R. Patel
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, United States
- Division of Cardiology, Duke University School of Medicine, Durham, North Carolina, United States
| | - Suresh Balu
- Duke Institute for Health Innovation, Duke University School of Medicine, Durham, North Carolina, United States
| | - Zainab Samad
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
- Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Mark P. Sendak
- Duke Institute for Health Innovation, Duke University School of Medicine, Durham, North Carolina, United States
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3
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Tamrat T, Zhao Y, Schalet D, AlSalamah S, Pujari S, Say L. Exploring the Use and Implications of AI in Sexual and Reproductive Health and Rights: Protocol for a Scoping Review. JMIR Res Protoc 2024; 13:e53888. [PMID: 38593433 PMCID: PMC11040437 DOI: 10.2196/53888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 01/23/2024] [Accepted: 02/09/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has emerged as a transformative force across the health sector and has garnered significant attention within sexual and reproductive health and rights (SRHR) due to polarizing views on its opportunities to advance care and the heightened risks and implications it brings to people's well-being and bodily autonomy. As the fields of AI and SRHR evolve, clarity is needed to bridge our understanding of how AI is being used within this historically politicized health area and raise visibility on the critical issues that can facilitate its responsible and meaningful use. OBJECTIVE This paper presents the protocol for a scoping review to synthesize empirical studies that focus on the intersection of AI and SRHR. The review aims to identify the characteristics of AI systems and tools applied within SRHR, regarding health domains, intended purpose, target users, AI data life cycle, and evidence on benefits and harms. METHODS The scoping review follows the standard methodology developed by Arksey and O'Malley. We will search the following electronic databases: MEDLINE (PubMed), Scopus, Web of Science, and CINAHL. Inclusion criteria comprise the use of AI systems and tools in sexual and reproductive health and clear methodology describing either quantitative or qualitative approaches, including program descriptions. Studies will be excluded if they focus entirely on digital interventions that do not explicitly use AI systems and tools, are about robotics or nonhuman subjects, or are commentaries. We will not exclude articles based on geographic location, language, or publication date. The study will present the uses of AI across sexual and reproductive health domains, the intended purpose of the AI system and tools, and maturity within the AI life cycle. Outcome measures will be reported on the effect, accuracy, acceptability, resource use, and feasibility of studies that have deployed and evaluated AI systems and tools. Ethical and legal considerations, as well as findings from qualitative studies, will be synthesized through a narrative thematic analysis. We will use the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) format for the publication of the findings. RESULTS The database searches resulted in 12,793 records when the searches were conducted in October 2023. Screening is underway, and the analysis is expected to be completed by July 2024. CONCLUSIONS The findings will provide key insights on usage patterns and evidence on the use of AI in SRHR, as well as convey key ethical, safety, and legal considerations. The outcomes of this scoping review are contributing to a technical brief developed by the World Health Organization and will guide future research and practice in this highly charged area of work. TRIAL REGISTRATION OSF Registries osf.io/ma4d9; https://osf.io/ma4d9. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/53888.
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Affiliation(s)
- Tigest Tamrat
- UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction, Department of Sexual and Reproductive Health and Research, World Health Organization, Geneva, Switzerland
| | - Yu Zhao
- Department of Digital Health and Innovations, Science Division, World Health Organization, Geneva, Switzerland
| | - Denise Schalet
- Department of Digital Health and Innovations, Science Division, World Health Organization, Geneva, Switzerland
| | - Shada AlSalamah
- Department of Digital Health and Innovations, Science Division, World Health Organization, Geneva, Switzerland
- Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Sameer Pujari
- Department of Digital Health and Innovations, Science Division, World Health Organization, Geneva, Switzerland
| | - Lale Say
- UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction, Department of Sexual and Reproductive Health and Research, World Health Organization, Geneva, Switzerland
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Polasek TM. Pharmacogenomics - a minor rather than major force in clinical medicine. Expert Rev Clin Pharmacol 2024; 17:203-212. [PMID: 38307498 DOI: 10.1080/17512433.2024.2314726] [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: 11/01/2023] [Accepted: 02/01/2024] [Indexed: 02/04/2024]
Abstract
INTRODUCTION Pharmacogenomics (PGx) is touted as essential for the future of precision medicine. But the opportunity cost of PGx from the prescribers' perspective is rarely considered. The aim of this article is to critique PGx-guided prescribing using clinical pharmacology principles so that important cases for PGx testing are not missed by doctors responsible for therapeutic decision making. AREAS COVERED Three categories of PGx and their limitations are outlined - exposure PGx, response PGx, and immune-mediated safety PGx. Clinical pharmacology reasons are given for the narrow scope of PGx-guided prescribing apart from a few medical specialties. Clinical problems for doctors that may arise from PGx are then explained, including mismatch between patients' expectations of PGx testing and the benefits or answers it provides. EXPERT OPINION Contrary to popular opinion, PGx is unlikely to become the cornerstone of precision medicine. Sound clinical pharmacology reasons explain why PGx-guided prescribing is unnecessary for most drugs. Pharmacogenomics is important for niche areas of prescribing but has limited clinical utility more broadly. The opportunity cost of PGx-guided prescribing is currently too great for most doctors.
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Affiliation(s)
- Thomas M Polasek
- Centre for Medicine Use and Safety, Monash University, Melbourne, Australia
- CMAX Clinical Research, Adelaide, Australia
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5
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Polasek TM, Peck RW. Beyond Population-Level Targets for Drug Concentrations: Precision Dosing Needs Individual-Level Targets that Include Superior Biomarkers of Drug Responses. Clin Pharmacol Ther 2024. [PMID: 38328977 DOI: 10.1002/cpt.3197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 01/17/2024] [Indexed: 02/09/2024]
Abstract
The purpose of precision dosing is to increase the chances of therapeutic success in individual patients. This is achieved in practice by adjusting doses to reach precision dosing targets determined previously in relevant populations, ideally with robust supportive evidence showing improved clinical outcomes compared with standard dosing. But is this implicit assumption of translatable population-level precision dosing targets correct and the best for all patients? In this review, the types of precision dosing targets and how they are determined are outlined, problems with the translatability of these targets to individual patients are identified, and ways forward to address these challengers are proposed. Achieving improved clinical outcomes to support precision dosing over standard dosing is currently hampered by applying population-level targets to all patients. Just as "one-dose-fits-all" may be an inappropriate philosophy for drug treatment overall, a "one-target-fits-all" philosophy may limit the broad clinical benefits of precision dosing. Defining individual-level precision dosing targets may be needed for greatest therapeutic success. Superior future precision dosing targets will integrate several biomarkers that together account for the multiple sources of drug response variability.
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Affiliation(s)
- Thomas M Polasek
- Centre for Medicine Use and Safety, Monash University, Melbourne, Victoria, Australia
- CMAX Clinical Research, Adelaide, South Australia, Australia
| | - Richard W Peck
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
- Pharma Research & Development (pRED), Roche Innovation Center Basel, Basel, Switzerland
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Zrubka Z, Kertész G, Gulácsi L, Czere J, Hölgyesi Á, Nezhad HM, Mosavi A, Kovács L, Butte AJ, Péntek M. The Reporting Quality of Machine Learning Studies on Pediatric Diabetes Mellitus: Systematic Review. J Med Internet Res 2024; 26:e47430. [PMID: 38241075 PMCID: PMC10837761 DOI: 10.2196/47430] [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: 03/20/2023] [Revised: 04/29/2023] [Accepted: 11/17/2023] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND Diabetes mellitus (DM) is a major health concern among children with the widespread adoption of advanced technologies. However, concerns are growing about the transparency, replicability, biasedness, and overall validity of artificial intelligence studies in medicine. OBJECTIVE We aimed to systematically review the reporting quality of machine learning (ML) studies of pediatric DM using the Minimum Information About Clinical Artificial Intelligence Modelling (MI-CLAIM) checklist, a general reporting guideline for medical artificial intelligence studies. METHODS We searched the PubMed and Web of Science databases from 2016 to 2020. Studies were included if the use of ML was reported in children with DM aged 2 to 18 years, including studies on complications, screening studies, and in silico samples. In studies following the ML workflow of training, validation, and testing of results, reporting quality was assessed via MI-CLAIM by consensus judgments of independent reviewer pairs. Positive answers to the 17 binary items regarding sufficient reporting were qualitatively summarized and counted as a proxy measure of reporting quality. The synthesis of results included testing the association of reporting quality with publication and data type, participants (human or in silico), research goals, level of code sharing, and the scientific field of publication (medical or engineering), as well as with expert judgments of clinical impact and reproducibility. RESULTS After screening 1043 records, 28 studies were included. The sample size of the training cohort ranged from 5 to 561. Six studies featured only in silico patients. The reporting quality was low, with great variation among the 21 studies assessed using MI-CLAIM. The number of items with sufficient reporting ranged from 4 to 12 (mean 7.43, SD 2.62). The items on research questions and data characterization were reported adequately most often, whereas items on patient characteristics and model examination were reported adequately least often. The representativeness of the training and test cohorts to real-world settings and the adequacy of model performance evaluation were the most difficult to judge. Reporting quality improved over time (r=0.50; P=.02); it was higher than average in prognostic biomarker and risk factor studies (P=.04) and lower in noninvasive hypoglycemia detection studies (P=.006), higher in studies published in medical versus engineering journals (P=.004), and higher in studies sharing any code of the ML pipeline versus not sharing (P=.003). The association between expert judgments and MI-CLAIM ratings was not significant. CONCLUSIONS The reporting quality of ML studies in the pediatric population with DM was generally low. Important details for clinicians, such as patient characteristics; comparison with the state-of-the-art solution; and model examination for valid, unbiased, and robust results, were often the weak points of reporting. To assess their clinical utility, the reporting standards of ML studies must evolve, and algorithms for this challenging population must become more transparent and replicable.
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Affiliation(s)
- Zsombor Zrubka
- HECON Health Economics Research Center, University Research and Innovation Center, Óbuda University, Budapest, Hungary
| | - Gábor Kertész
- John von Neumann Faculty of Informatics, Óbuda University, Budapest, Hungary
| | - László Gulácsi
- HECON Health Economics Research Center, University Research and Innovation Center, Óbuda University, Budapest, Hungary
| | - János Czere
- Doctoral School of Innovation Management, Óbuda University, Budapest, Hungary
| | - Áron Hölgyesi
- HECON Health Economics Research Center, University Research and Innovation Center, Óbuda University, Budapest, Hungary
- Doctoral School of Molecular Medicine, Semmelweis University, Budapest, Hungary
| | - Hossein Motahari Nezhad
- HECON Health Economics Research Center, University Research and Innovation Center, Óbuda University, Budapest, Hungary
- Doctoral School of Business and Management, Corvinus University of Budapest, Budapest, Hungary
| | - Amir Mosavi
- John von Neumann Faculty of Informatics, Óbuda University, Budapest, Hungary
| | - Levente Kovács
- Physiological Controls Research Center, University Research and Innovation Center, Óbuda University, Budapest, Hungary
| | - Atul J Butte
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, United States
| | - Márta Péntek
- HECON Health Economics Research Center, University Research and Innovation Center, Óbuda University, Budapest, Hungary
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7
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Ahmed MI, Spooner B, Isherwood J, Lane M, Orrock E, Dennison A. A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare. Cureus 2023; 15:e46454. [PMID: 37927664 PMCID: PMC10623210 DOI: 10.7759/cureus.46454] [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] [Accepted: 10/03/2023] [Indexed: 11/07/2023] Open
Abstract
Artificial intelligence (AI) is expected to improve healthcare outcomes by facilitating early diagnosis, reducing the medical administrative burden, aiding drug development, personalising medical and oncological management, monitoring healthcare parameters on an individual basis, and allowing clinicians to spend more time with their patients. In the post-pandemic world where there is a drive for efficient delivery of healthcare and manage long waiting times for patients to access care, AI has an important role in supporting clinicians and healthcare systems to streamline the care pathways and provide timely and high-quality care for the patients. Despite AI technologies being used in healthcare for some decades, and all the theoretical potential of AI, the uptake in healthcare has been uneven and slower than anticipated and there remain a number of barriers, both overt and covert, which have limited its incorporation. This literature review highlighted barriers in six key areas: ethical, technological, liability and regulatory, workforce, social, and patient safety barriers. Defining and understanding the barriers preventing the acceptance and implementation of AI in the setting of healthcare will enable clinical staff and healthcare leaders to overcome the identified hurdles and incorporate AI technologies for the benefit of patients and clinical staff.
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Affiliation(s)
- Molla Imaduddin Ahmed
- Paediatric Respiratory Medicine, University Hospitals of Leicester NHS Trust, Leicester, GBR
| | - Brendan Spooner
- Intensive Care and Anaesthesia, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, GBR
| | - John Isherwood
- Hepatobiliary and Pancreatic Surgery, University Hospitals of Leicester NHS Trust, Leicester, GBR
| | - Mark Lane
- Ophthalmology, Birmingham and Midland Eye Centre, Birmingham, GBR
| | - Emma Orrock
- Head of Clinical Senates, East and West Midlands Clinical Senate, Leicester, GBR
| | - Ashley Dennison
- Hepatobiliary and Pancreatic Surgery, University Hospitals of Leicester NHS Trust, Leicester, GBR
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Polasek TM. Virtual twin for healthcare management. Front Digit Health 2023; 5:1246659. [PMID: 37781454 PMCID: PMC10540783 DOI: 10.3389/fdgth.2023.1246659] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/01/2023] [Indexed: 10/03/2023] Open
Abstract
Healthcare is increasingly fragmented, resulting in escalating costs, patient dissatisfaction, and sometimes adverse clinical outcomes. Strategies to decrease healthcare fragmentation are therefore attractive from payer and patient perspectives. In this commentary, a patient-centered smart phone application called Virtual Twin for Healthcare Management (VTHM) is proposed, including its organizational layout, basic functionality, and potential clinical applications. The platform features a virtual twin hub that displays the body and its health data. This is a physiologically based human model that is "virtualized" for the patient based on their unique genetic, molecular, physiological, and disease characteristics. The spokes of the system are a full service and interoperable electronic-health record, accessible to healthcare providers with permission on any device with internet access. Theoretical case studies based on real scenarios are presented to show how VTHM could potentially improve patient care and clinical efficiency. Challenges that must be overcome to turn VTHM into reality are also briefly outlined. Notably, the VTHM platform is designed to operationalize current and future precision medicine initiatives, such as access to molecular diagnostic results, pharmacogenomics-guided prescribing, and model-informed precision dosing.
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Affiliation(s)
- Thomas M. Polasek
- Certara, Princeton, NJ, United States
- Centre for Medicines Use and Safety, Monash University, Melbourne, VIC, Australia
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9
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Arya SS, Dias SB, Jelinek HF, Hadjileontiadis LJ, Pappa AM. The convergence of traditional and digital biomarkers through AI-assisted biosensing: A new era in translational diagnostics? Biosens Bioelectron 2023; 235:115387. [PMID: 37229842 DOI: 10.1016/j.bios.2023.115387] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 04/11/2023] [Accepted: 05/10/2023] [Indexed: 05/27/2023]
Abstract
Advances in consumer electronics, alongside the fields of microfluidics and nanotechnology have brought to the fore low-cost wearable/portable smart devices. Although numerous smart devices that track digital biomarkers have been successfully translated from bench-to-bedside, only a few follow the same fate when it comes to track traditional biomarkers. Current practices still involve laboratory-based tests, followed by blood collection, conducted in a clinical setting as they require trained personnel and specialized equipment. In fact, real-time, passive/active and robust sensing of physiological and behavioural data from patients that can feed artificial intelligence (AI)-based models can significantly improve decision-making, diagnosis and treatment at the point-of-procedure, by circumventing conventional methods of sampling, and in person investigation by expert pathologists, who are scarce in developing countries. This review brings together conventional and digital biomarker sensing through portable and autonomous miniaturized devices. We first summarise the technological advances in each field vs the current clinical practices and we conclude by merging the two worlds of traditional and digital biomarkers through AI/ML technologies to improve patient diagnosis and treatment. The fundamental role, limitations and prospects of AI in realizing this potential and enhancing the existing technologies to facilitate the development and clinical translation of "point-of-care" (POC) diagnostics is finally showcased.
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Affiliation(s)
- Sagar S Arya
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates
| | - Sofia B Dias
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates; Interdisciplinary Center for Human Performance, Faculdade de Motricidade Humana, Universidade de Lisboa, Portugal.
| | - Herbert F Jelinek
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates; Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
| | - Leontios J Hadjileontiadis
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates; Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates; Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, GR, 54124, Thessaloniki, Greece
| | - Anna-Maria Pappa
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates; Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates; Department of Chemical Engineering and Biotechnology, Cambridge University, Cambridge, UK.
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10
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Nittas V, Daniore P, Landers C, Gille F, Amann J, Hubbs S, Puhan MA, Vayena E, Blasimme A. Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging. PLOS DIGITAL HEALTH 2023; 2:e0000189. [PMID: 36812620 PMCID: PMC9931290 DOI: 10.1371/journal.pdig.0000189] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 01/02/2023] [Indexed: 02/04/2023]
Abstract
Machine learning has become a key driver of the digital health revolution. That comes with a fair share of high hopes and hype. We conducted a scoping review on machine learning in medical imaging, providing a comprehensive outlook of the field's potential, limitations, and future directions. Most reported strengths and promises included: improved (a) analytic power, (b) efficiency (c) decision making, and (d) equity. Most reported challenges included: (a) structural barriers and imaging heterogeneity, (b) scarcity of well-annotated, representative and interconnected imaging datasets (c) validity and performance limitations, including bias and equity issues, and (d) the still missing clinical integration. The boundaries between strengths and challenges, with cross-cutting ethical and regulatory implications, remain blurred. The literature emphasizes explainability and trustworthiness, with a largely missing discussion about the specific technical and regulatory challenges surrounding these concepts. Future trends are expected to shift towards multi-source models, combining imaging with an array of other data, in a more open access, and explainable manner.
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Affiliation(s)
- Vasileios Nittas
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, Faculty of Medicine, Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Paola Daniore
- Institute for Implementation Science in Health Care, Faculty of Medicine, University of Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Switzerland
| | - Constantin Landers
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Felix Gille
- Institute for Implementation Science in Health Care, Faculty of Medicine, University of Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Switzerland
| | - Julia Amann
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Shannon Hubbs
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Milo Alan Puhan
- Epidemiology, Biostatistics and Prevention Institute, Faculty of Medicine, Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Effy Vayena
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Alessandro Blasimme
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
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11
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Tavakoli H, Mohammadi S, Li X, Fu G, Li X. Microfluidic platforms integrated with nano-sensors for point-of-care bioanalysis. Trends Analyt Chem 2022; 157:116806. [PMID: 37929277 PMCID: PMC10621318 DOI: 10.1016/j.trac.2022.116806] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Microfluidic technology provides a portable, cost-effective, and versatile tool for point-of-care (POC) bioanalysis because of its associated advantages such as fast analysis, low volumes of reagent consumption, and high portability. Along with microfluidics, the application of nanomaterials in biosensing has attracted lots of attention due to their unique physical and chemical properties for enhanced signal modulation such as signal amplification and signal transduction for POC bioanalysis. Hence, an enormous number of microfluidic devices integrated with nano-sensors have been developed for POC bioanalysis targeting low-resource settings. Herein, we review recent advances in POC bioanalysis on nano-sensor-based microfluidic platforms. We first briefly summarized the different types of cost-effective microfluidic platforms, followed by a concise introduction to nanomaterial-based biosensors. Then, we highlighted the application of microfluidic platforms integrated with nano-sensors for POC bioanalysis. Finally, we discussed the current limitations and perspective trends of the nano-sensor-based microfluidic platforms for POC bioanalysis.
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Affiliation(s)
- Hamed Tavakoli
- Department of Chemistry and Biochemistry, University of Texas at El Paso, El Paso, TX, 79968, USA
| | - Samayeh Mohammadi
- Department of Chemistry and Biochemistry, University of Texas at El Paso, El Paso, TX, 79968, USA
| | - Xiaochun Li
- College of Biomedical Engineering, Taiyuan University of Technology, Shanxi, 030606, China
| | - Guanglei Fu
- School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation, Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, Yantai, Shandong, 264005, China
| | - XiuJun Li
- Department of Chemistry and Biochemistry, University of Texas at El Paso, El Paso, TX, 79968, USA
- Border Biomedical Research Center, Forensic Science, & Environmental Science and Engineering, University of Texas at El Paso, El Paso, 79968, USA
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12
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Hauben M. Artificial intelligence in pharmacovigilance: Do we need explainability? Pharmacoepidemiol Drug Saf 2022; 31:1311-1316. [PMID: 35747938 DOI: 10.1002/pds.5501] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 05/27/2022] [Accepted: 06/08/2022] [Indexed: 12/15/2022]
Affiliation(s)
- Manfred Hauben
- Pfizer Inc., New York, New York, USA.,NYU Langone Health, New York, New York, USA
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13
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Fasterholdt I, Naghavi-Behzad M, Rasmussen BSB, Kjølhede T, Skjøth MM, Hildebrandt MG, Kidholm K. Value assessment of artificial intelligence in medical imaging: a scoping review. BMC Med Imaging 2022; 22:187. [PMID: 36316665 PMCID: PMC9620604 DOI: 10.1186/s12880-022-00918-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 10/22/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is seen as one of the major disrupting forces in the future healthcare system. However, the assessment of the value of these new technologies is still unclear, and no agreed international health technology assessment-based guideline exists. This study provides an overview of the available literature in the value assessment of AI in the field of medical imaging. METHODS We performed a systematic scoping review of published studies between January 2016 and September 2020 using 10 databases (Medline, Scopus, ProQuest, Google Scholar, and six related databases of grey literature). Information about the context (country, clinical area, and type of study) and mentioned domains with specific outcomes and items were extracted. An existing domain classification, from a European assessment framework, was used as a point of departure, and extracted data were grouped into domains and content analysis of data was performed covering predetermined themes. RESULTS Seventy-nine studies were included out of 5890 identified articles. An additional seven studies were identified by searching reference lists, and the analysis was performed on 86 included studies. Eleven domains were identified: (1) health problem and current use of technology, (2) technology aspects, (3) safety assessment, (4) clinical effectiveness, (5) economics, (6) ethical analysis, (7) organisational aspects, (8) patients and social aspects, (9) legal aspects, (10) development of AI algorithm, performance metrics and validation, and (11) other aspects. The frequency of mentioning a domain varied from 20 to 78% within the included papers. Only 15/86 studies were actual assessments of AI technologies. The majority of data were statements from reviews or papers voicing future needs or challenges of AI research, i.e. not actual outcomes of evaluations. CONCLUSIONS This review regarding value assessment of AI in medical imaging yielded 86 studies including 11 identified domains. The domain classification based on European assessment framework proved useful and current analysis added one new domain. Included studies had a broad range of essential domains about addressing AI technologies highlighting the importance of domains related to legal and ethical aspects.
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Affiliation(s)
- Iben Fasterholdt
- grid.7143.10000 0004 0512 5013CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Sdr. Boulevard 29, Entrance 102, 4rd Floor, 5000 Odense C, Denmark
| | - Mohammad Naghavi-Behzad
- grid.10825.3e0000 0001 0728 0170Department of Clinical Research, University of Southern Denmark, Odense, Denmark ,grid.7143.10000 0004 0512 5013Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark
| | - Benjamin S. B. Rasmussen
- grid.10825.3e0000 0001 0728 0170Department of Clinical Research, University of Southern Denmark, Odense, Denmark ,grid.7143.10000 0004 0512 5013Department of Radiology, Odense University Hospital, Odense, Denmark ,grid.7143.10000 0004 0512 5013CAI-X – Centre for Clinical Artificial Intelligence, Odense University Hospital, Odense, Denmark
| | - Tue Kjølhede
- grid.7143.10000 0004 0512 5013CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Sdr. Boulevard 29, Entrance 102, 4rd Floor, 5000 Odense C, Denmark
| | - Mette Maria Skjøth
- grid.7143.10000 0004 0512 5013Department of Dermatology and Allergy Centre, Odense University Hospital, Odense, Denmark
| | - Malene Grubbe Hildebrandt
- grid.7143.10000 0004 0512 5013CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Sdr. Boulevard 29, Entrance 102, 4rd Floor, 5000 Odense C, Denmark ,grid.10825.3e0000 0001 0728 0170Department of Clinical Research, University of Southern Denmark, Odense, Denmark ,grid.7143.10000 0004 0512 5013Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark
| | - Kristian Kidholm
- grid.7143.10000 0004 0512 5013CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Sdr. Boulevard 29, Entrance 102, 4rd Floor, 5000 Odense C, Denmark
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14
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Barry B, Zhu X, Behnken E, Inselman J, Schaepe K, McCoy R, Rushlow D, Noseworthy P, Richardson J, Curtis S, Sharp R, Misra A, Akfaly A, Molling P, Bernard M, Yao X. Provider Perspectives on Artificial Intelligence-Guided Screening for Low Ejection Fraction in Primary Care: Qualitative Study. JMIR AI 2022; 1:e41940. [PMID: 38875550 PMCID: PMC11041436 DOI: 10.2196/41940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/13/2022] [Accepted: 09/17/2022] [Indexed: 06/16/2024]
Abstract
BACKGROUND The promise of artificial intelligence (AI) to transform health care is threatened by a tangle of challenges that emerge as new AI tools are introduced into clinical practice. AI tools with high accuracy, especially those that detect asymptomatic cases, may be hindered by barriers to adoption. Understanding provider needs and concerns is critical to inform implementation strategies that improve provider buy-in and adoption of AI tools in medicine. OBJECTIVE This study aimed to describe provider perspectives on the adoption of an AI-enabled screening tool in primary care to inform effective integration and sustained use. METHODS A qualitative study was conducted between December 2019 and February 2020 as part of a pragmatic randomized controlled trial at a large academic medical center in the United States. In all, 29 primary care providers were purposively sampled using a positive deviance approach for participation in semistructured focus groups after their use of the AI tool in the randomized controlled trial was complete. Focus group data were analyzed using a grounded theory approach; iterative analysis was conducted to identify codes and themes, which were synthesized into findings. RESULTS Our findings revealed that providers understood the purpose and functionality of the AI tool and saw potential value for more accurate and faster diagnoses. However, successful adoption into routine patient care requires the smooth integration of the tool with clinical decision-making and existing workflow to address provider needs and preferences during implementation. To fulfill the AI tool's promise of clinical value, providers identified areas for improvement including integration with clinical decision-making, cost-effectiveness and resource allocation, provider training, workflow integration, care pathway coordination, and provider-patient communication. CONCLUSIONS The implementation of AI-enabled tools in medicine can benefit from sensitivity to the nuanced context of care and provider needs to enable the useful adoption of AI tools at the point of care. TRIAL REGISTRATION ClinicalTrials.gov NCT04000087; https://clinicaltrials.gov/ct2/show/NCT04000087.
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Affiliation(s)
- Barbara Barry
- Division of Health Care Delivery Research, Mayo Clinic, Rochester, MN, United States
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States
| | - Xuan Zhu
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States
| | - Emma Behnken
- Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, MN, United States
| | - Jonathan Inselman
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States
| | - Karen Schaepe
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States
| | - Rozalina McCoy
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States
| | - David Rushlow
- Department of Family Medicine, Mayo Clinic, Rochester, MN, United States
| | - Peter Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States
| | - Jordan Richardson
- Biomedical Ethics Research Program, Mayo Clinic, Rochester, MN, United States
| | - Susan Curtis
- Biomedical Ethics Research Program, Mayo Clinic, Rochester, MN, United States
| | - Richard Sharp
- Biomedical Ethics Research Program, Mayo Clinic, Rochester, MN, United States
| | - Artika Misra
- Department of Family Medicine, Mayo Clinic Health System, Mankato, MN, United States
| | - Abdulla Akfaly
- Department of Community Internal Medicine, Mayo Clinic Health System, Eau Claire, WI, United States
| | - Paul Molling
- Department of Family Medicine, Mayo Clinic Health System, Onalaska, WI, United States
| | - Matthew Bernard
- Department of Family Medicine, Mayo Clinic, Rochester, MN, United States
| | - Xiaoxi Yao
- Division of Health Care Delivery Research, Mayo Clinic, Rochester, MN, United States
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States
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15
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White RD, Demirer M, Gupta V, Sebro RA, Kusumoto FM, Erdal BS. Pre-deployment assessment of an AI model to assist radiologists in chest X-ray detection and identification of lead-less implanted electronic devices for pre-MRI safety screening: realized implementation needs and proposed operational solutions. J Med Imaging (Bellingham) 2022; 9:054504. [PMID: 36310648 PMCID: PMC9603740 DOI: 10.1117/1.jmi.9.5.054504] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 09/23/2022] [Indexed: 09/29/2023] Open
Abstract
Purpose Chest X-ray (CXR) use in pre-MRI safety screening, such as for lead-less implanted electronic device (LLIED) recognition, is common. To assist CXR interpretation, we "pre-deployed" an artificial intelligence (AI) model to assess (1) accuracies in LLIED-type (and consequently safety-level) identification, (2) safety implications of LLIED nondetections or misidentifications, (3) infrastructural or workflow requirements, and (4) demands related to model adaptation to real-world conditions. Approach A two-tier cascading methodology for LLIED detection/localization and identification on a frontal CXR was applied to evaluate the performance of the original nine-class AI model. With the unexpected early appearance of LLIED types during simulated real-world trialing, retraining of a newer 12-class version preceded retrialing. A zero footprint (ZF) graphical user interface (GUI)/viewer with DICOM-based output was developed for inference-result display and adjudication, supporting end-user engagement and model continuous learning and/or modernization. Results During model testing or trialing using both the nine-class and 12-class models, robust detection/localization was consistently 100%, with mAP 0.99 from fivefold cross-validation. Safety-level categorization was high during both testing ( AUC ≥ 0.98 and ≥ 0.99 , respectively) and trialing (accuracy 98% and 97%, respectively). LLIED-type identifications by the two models during testing (1) were 98.9% and 99.5% overall correct and (2) consistently showed AUC ≥ 0.92 (1.00 for 8/9 and 9/12 LLIED-types, respectively). Pre-deployment trialing of both models demonstrated overall type-identification accuracies of 94.5% and 95%, respectively. Of the small number of misidentifications, none involved MRI-stringently conditional or MRI-unsafe types of LLIEDs. Optimized ZF GUI/viewer operations led to greater user-friendliness for radiologist engagement. Conclusions Our LLIED-related AI methodology supports (1) 100% detection sensitivity, (2) high identification (including MRI-safety) accuracy, and (3) future model deployment with facilitated inference-result display and adjudication for ongoing model adaptation to future real-world experiences.
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Affiliation(s)
- Richard D. White
- Mayo Clinic, Department of Radiology, Center for Augmented Intelligence in Imaging, Jacksonville, Florida, United States
| | - Mutlu Demirer
- Mayo Clinic, Department of Radiology, Center for Augmented Intelligence in Imaging, Jacksonville, Florida, United States
| | - Vikash Gupta
- Mayo Clinic, Department of Radiology, Center for Augmented Intelligence in Imaging, Jacksonville, Florida, United States
| | - Ronnie A. Sebro
- Mayo Clinic, Department of Radiology, Center for Augmented Intelligence in Imaging, Jacksonville, Florida, United States
| | - Frederick M. Kusumoto
- Mayo Clinic, Department of Cardiovascular Medicine, Jacksonville, Florida, United States
| | - Barbaros Selnur Erdal
- Mayo Clinic, Department of Radiology, Center for Augmented Intelligence in Imaging, Jacksonville, Florida, United States
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16
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Meier LJ, Hein A, Diepold K, Buyx A. Algorithms for Ethical Decision-Making in the Clinic: A Proof of Concept. THE AMERICAN JOURNAL OF BIOETHICS : AJOB 2022; 22:4-20. [PMID: 35293841 DOI: 10.1080/15265161.2022.2040647] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress' prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical institutions. We explain why we chose fuzzy cognitive maps to set up the advisory system and how we utilized machine learning to train it. We report on the difficult task of operationalizing the principles of beneficence, non-maleficence and patient autonomy, and describe how we selected suitable input parameters that we extracted from a training dataset of clinical cases. The first performance results are promising, but an algorithmic approach to ethics also comes with several weaknesses and limitations. Should one really entrust the sensitive domain of clinical ethics to machine intelligence?
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Affiliation(s)
- Lukas J Meier
- Technical University of Munich
- University of Cambridge
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17
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Chu H, Moon S, Park J, Bak S, Ko Y, Youn BY. The Use of Artificial Intelligence in Complementary and Alternative Medicine: A Systematic Scoping Review. Front Pharmacol 2022; 13:826044. [PMID: 35431917 PMCID: PMC9011141 DOI: 10.3389/fphar.2022.826044] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 03/01/2022] [Indexed: 01/04/2023] Open
Abstract
Background: The development of artificial intelligence (AI) in the medical field has been growing rapidly. As AI models have been introduced in complementary and alternative medicine (CAM), a systematized review must be performed to understand its current status. Objective: To categorize and seek the current usage of AI in CAM. Method: A systematic scoping review was conducted based on the method proposed by the Joanna Briggs Institute. The three databases, PubMed, Embase, and Cochrane Library, were used to find studies regarding AI and CAM. Only English studies from 2000 were included. Studies without mentioning either AI techniques or CAM modalities were excluded along with the non-peer-reviewed studies. A broad-range search strategy was applied to locate all relevant studies. Results: A total of 32 studies were identified, and three main categories were revealed: 1) acupuncture treatment, 2) tongue and lip diagnoses, and 3) herbal medicine. Other CAM modalities were music therapy, meditation, pulse diagnosis, and TCM syndromes. The majority of the studies utilized AI models to predict certain patterns and find reliable computerized models to assist physicians. Conclusion: Although the results from this review have shown the potential use of AI models in CAM, future research ought to focus on verifying and validating the models by performing a large-scale clinical trial to better promote AI in CAM in the era of digital health.
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Affiliation(s)
- Hongmin Chu
- Daecheong Public Health Subcenter, Incheon, South Korea
| | - Seunghwan Moon
- Department of Global Public Health and Korean Medicine Management, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Jeongsu Park
- Department of College of Korean Medicine, Wonkwang University, Iksan, South Korea
| | - Seongjun Bak
- Department of College of Korean Medicine, Wonkwang University, Iksan, South Korea
| | - Youme Ko
- National Institute for Korean Medicine Development (NIKOM), Seoul, South Korea
| | - Bo-Young Youn
- Department of Preventive Medicine, College of Korean Medicine, Kyung Hee University, Seoul, South Korea
- *Correspondence: Bo-Young Youn,
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18
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Woo JH, Kim EC, Kim SM. The Current Status of Breakthrough Devices Designation in the United States and Innovative Medical Devices Designation in Korea for Digital Health Software. Expert Rev Med Devices 2022; 19:213-228. [PMID: 35255755 DOI: 10.1080/17434440.2022.2051479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Artificial Intelligence (AI) is becoming increasingly utilized in the medical device industry as it can address unmet demands in clinical sites and provide more patient treatment options. This study aims to analyze the FDA's Breakthrough Device Program and MFDS' Innovative Medical Device Program, which support regulatory science for innovative medical devices today. Through this study, it is intended to enable prediction of current development trends of Software as a Medical Device (SaMD) and Digital Therapeutics (DTx), which combine AI and technologies to be used in the clinical field soon. AREAS COVERED A systematic search was conducted on the broad topics of "FDA and MFDS Program's SaMD, DTx". A parallel review and update of PubMed, and the official websites were conducted to investigate the regulator's databases, review official press releases of regulatory agencies, and provide detailed descriptions of researchers. EXPERT OPINION The efforts of related stakeholders are needed to expand AI technology to diagnosis, prevention, and treatment technologies for diseases that are difficult to diagnose early or are classified as clinical challenges. It is important to prepare regulatory policies suitable for the rapid pace of technological development and to create an environment where regulatory science can be realized by developers.
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Affiliation(s)
- Jae Hyun Woo
- Research Institute for Commercialization of Biomedical Convergence Technology, Seoul, Republic of Korea.,Medical Device Industry Program in Graduate School, Dongguk University, Seoul, Republic of Korea.,National Institute of Medical Device Safety Information, Seoul, Republic of Korea.,Department of Medical Biotechnology, Dongguk University-Seoul, Seoul, Korea
| | - Eun Cheol Kim
- Research Institute for Commercialization of Biomedical Convergence Technology, Seoul, Republic of Korea.,Medical Device Industry Program in Graduate School, Dongguk University, Seoul, Republic of Korea.,National Institute of Medical Device Safety Information, Seoul, Republic of Korea.,Department of Medical Biotechnology, Dongguk University-Seoul, Seoul, Korea
| | - Sung Min Kim
- Research Institute for Commercialization of Biomedical Convergence Technology, Seoul, Republic of Korea.,Medical Device Industry Program in Graduate School, Dongguk University, Seoul, Republic of Korea.,National Institute of Medical Device Safety Information, Seoul, Republic of Korea.,Department of Medical Biotechnology, Dongguk University-Seoul, Seoul, Korea
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19
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Trenfield SJ, Awad A, McCoubrey LE, Elbadawi M, Goyanes A, Gaisford S, Basit AW. Advancing pharmacy and healthcare with virtual digital technologies. Adv Drug Deliv Rev 2022; 182:114098. [PMID: 34998901 DOI: 10.1016/j.addr.2021.114098] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 12/20/2021] [Accepted: 12/21/2021] [Indexed: 02/07/2023]
Abstract
Digitalisation of the healthcare sector promises to revolutionise patient healthcare globally. From the different technologies, virtual tools including artificial intelligence, blockchain, virtual, and augmented reality, to name but a few, are providing significant benefits to patients and the pharmaceutical sector alike, ranging from improving access to clinicians and medicines, as well as improving real-time diagnoses and treatments. Indeed, it is envisioned that such technologies will communicate together in real-time, as well as with their physical counterparts, to create a large-scale, cyber healthcare system. Despite the significant benefits that virtual-based digital health technologies can bring to patient care, a number of challenges still remain, ranging from data security to acceptance within the healthcare sector. This review provides a timely account of the benefits and challenges of virtual health interventions, as well an outlook on how such technologies can be transitioned from research-focused towards real-world healthcare and pharmaceutical applications to transform treatment pathways for patients worldwide.
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20
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Abuasal B, Ahmed MA, Patel P, Albusaysi S, Sabarinath S, Uppoor R, Mehta M. Clinical Pharmacology in Drug Development for Rare Diseases in Neurology: Contributions and Opportunities. Clin Pharmacol Ther 2021; 111:786-798. [PMID: 34860361 DOI: 10.1002/cpt.2501] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 11/30/2021] [Indexed: 01/02/2023]
Abstract
Several challenges are associated with rare disease drug development in neurology. In this article, we summarize the US Food and Drug Administration's experience with clinical drug development for rare neurological diseases and discuss clinical pharmacology's critical contributions to drug development for rare diseases. We used publicly available information to identify and screen drug products approved for rare neurological indications between 1983 and 2019. We highlighted cases in which clinical pharmacology contributed to the evidence of drug efficacy, dose selection for pivotal clinical trials, dose optimization based on intrinsic and extrinsic factors, pharmacokinetic bridging for formulations, and efficacy bridging across different racial groups. Fifty-one approved drug products were identified since the introduction of the Orphan Drug Act in 1983. Interestingly, the number of approvals in the last few years increased significantly, probably due to advances in genomic research and targeted drug modalities. Evaluation of dose selection in patient populations showed that in 52% of cases, the sponsors did not evaluate efficacy for more than one or two dose levels throughout the development program. Clinical pharmacology studies to evaluate the effect of intrinsic or extrinsic factors were adequately characterized in most of the applications. With the expansion of model informed drug development applications, (e.g., quantitative systems pharmacology and deep learning neural network models), the role and impact of clinical pharmacology is expected to grow exponentially in the next decade and enhance the development of novel treatment modalities for neurological rare diseases.
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Affiliation(s)
- Bilal Abuasal
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Mariam A Ahmed
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.,Takeda Pharmaceutical US, Cambridge, Massachusetts, USA
| | - Priyank Patel
- School of Pharmacy, University of Maryland, Baltimore, Maryland, USA
| | - Salwa Albusaysi
- Department of Pharmaceutics, Faculty of Pharmacy, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Sreedharan Sabarinath
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Ramana Uppoor
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Mehul Mehta
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
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Abstract
Trust in artificial intelligence (AI) by society and the development of trustworthy AI systems and ecosystems are critical for the progress and implementation of AI technology in medicine. With the growing use of AI in a variety of medical and imaging applications, it is more vital than ever to make these systems dependable and trustworthy. Fourteen core principles are considered in this article aiming to move the needle more closely to systems that are accurate, resilient, fair, explainable, safe, and transparent: toward trustworthy AI.
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22
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Joymangul JS, Sekhari A, Chatelet A, Moalla N, Grasset O. Obstructive Sleep Apnea compliance: verifications and validations of personalized interventions for PAP therapy. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2367-2373. [PMID: 34891758 DOI: 10.1109/embc46164.2021.9629905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The Positive Airway Pressure (PAP) therapy is the most capable therapy against Obstruction Sleep Apnea (OSA). PAP therapy prevents the narrowing and collapsing of the soft tissues of the upper airway. A patient diagnosed with OSA is expected to use their CPAP machines every night for at least more than 4h for experiencing any clinical improvement. However, for the last two decades, trials were carried out to improve compliance and understand factors impacting compliance, but there were not enough conclusive results. With the advent of big data analytic and real-time monitoring, new opportunities open up to tackle this compliance issue. This paper's significant contribution is a novel framework that blends multiple external verification and validation carried out by different healthcare stakeholders. We provide a systematic verification and validation process to push towards explainable data analytic and automatic learning processes. We also present a complete mHealth solution that includes two mobile applications. The first application is for delivering tailored interventions directly to the patients. The second application is bound to different healthcare stakeholders for the verification and validation process.
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23
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Wildemberg LE, da Silva Camacho AH, Miranda RL, Elias PCL, de Castro Musolino NR, Nazato D, Jallad R, Huayllas MKP, Mota JIS, Almeida T, Portes E, Ribeiro-Oliveira A, Vilar L, Boguszewski CL, Winter Tavares AB, Nunes-Nogueira VS, Mazzuco TL, Rech CGSL, Marques NV, Chimelli L, Czepielewski M, Bronstein MD, Abucham J, de Castro M, Kasuki L, Gadelha M. Machine Learning-based Prediction Model for Treatment of Acromegaly With First-generation Somatostatin Receptor Ligands. J Clin Endocrinol Metab 2021; 106:2047-2056. [PMID: 33686418 DOI: 10.1210/clinem/dgab125] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Indexed: 01/12/2023]
Abstract
CONTEXT Artificial intelligence (AI), in particular machine learning (ML), may be used to deeply analyze biomarkers of response to first-generation somatostatin receptor ligands (fg-SRLs) in the treatment of acromegaly. OBJECTIVE To develop a prediction model of therapeutic response of acromegaly to fg-SRL. METHODS Patients with acromegaly not cured by primary surgical treatment and who had adjuvant therapy with fg-SRL for at least 6 months after surgery were included. Patients were considered controlled if they presented growth hormone (GH) <1.0 ng/mL and normal age-adjusted insulin-like growth factor (IGF)-I levels. Six AI models were evaluated: logistic regression, k-nearest neighbor classifier, support vector machine, gradient-boosted classifier, random forest, and multilayer perceptron. The features included in the analysis were age at diagnosis, sex, GH, and IGF-I levels at diagnosis and at pretreatment, somatostatin receptor subtype 2 and 5 (SST2 and SST5) protein expression and cytokeratin granulation pattern (GP). RESULTS A total of 153 patients were analyzed. Controlled patients were older (P = .002), had lower GH at diagnosis (P = .01), had lower pretreatment GH and IGF-I (P < .001), and more frequently harbored tumors that were densely granulated (P = .014) or highly expressed SST2 (P < .001). The model that performed best was the support vector machine with the features SST2, SST5, GP, sex, age, and pretreatment GH and IGF-I levels. It had an accuracy of 86.3%, positive predictive value of 83.3% and negative predictive value of 87.5%. CONCLUSION We developed a ML-based prediction model with high accuracy that has the potential to improve medical management of acromegaly, optimize biochemical control, decrease long-term morbidities and mortality, and reduce health services costs.
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Affiliation(s)
- Luiz Eduardo Wildemberg
- Endocrine Unit and Neuroendocrinology Research Center, Medical School and Hospital Universitário Clementino Fraga Filho-Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil
- Neuroendocrine Unit-Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde , Rio de Janeiro, RJ, Brazil
| | - Aline Helen da Silva Camacho
- Neuropathology and Molecular Genetics Laboratory, Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde, Rio de Janeiro, RJ, Brazil
| | - Renan Lyra Miranda
- Neuropathology and Molecular Genetics Laboratory, Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde, Rio de Janeiro, RJ, Brazil
| | - Paula C L Elias
- Division of Endocrinology-Department of Internal Medicine, Ribeirao Preto Medical School-University of Sao Paulo, São Paulo, SP, Brazil
| | - Nina R de Castro Musolino
- Neuroendocrine Unit, Division of Functional Neurosurgery, Hospital das Clinicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Debora Nazato
- Neuroendocrine Unit-Division of Endocrinology and Metabolism-Escola Paulista de Medicina-Universidade Federal de São Paulo (Unifesp), São Paulo, SP, Brazil
| | - Raquel Jallad
- Neuroendocrine Unit, Division of Endocrinology and Metabolism, Hospital das Clínicas, University of São Paulo Medical School, São Paulo, SP, Brazil
- Cellular and Molecular Endocrinology Laboratory/LIM25, Discipline of Endocrinology, Hospital das Clinicas HCFMUSP, Faculty of Medicine, University of Sao Paulo, São Paulo, SP, Brazil
| | - Martha K P Huayllas
- Neuroendocrinology and Neurosurgery unit Hospital Brigadeiro, São Paulo, SP, Brazil
| | - Jose Italo S Mota
- Endocrinology and Metabolism Unit, Hospital Geral de Fortaleza, Secretaria Estadual de Saúde, Fortaleza, CE, Brazil
| | - Tobias Almeida
- Division of Endocrinology, Hospital de Clinicas de Porto Alegre (UFRGS), Porto Alegre, RS, Brazil
| | - Evandro Portes
- Institute of Medical Assistance to the State Public Hospital, São Paulo, SP, Brazil
| | | | - Lucio Vilar
- Neuroendocrine Unit, Division of Endocrinology and Metabolism, Hospital das Clínicas, Federal University of Pernambuco Medical School, Recife, PE, Brazil
| | - Cesar Luiz Boguszewski
- Endocrine Division (SEMPR), Department of Internal Medicine, Universidade Federal do Parana, Curitiba, PR, Brazil
| | - Ana Beatriz Winter Tavares
- Endocrine Unit-Department of Internal Medicine, Faculty of Medical Sciences, Universidade do Estado do Rio de Janeiro, RJ, Brazil
| | - Vania S Nunes-Nogueira
- Department of Internal Medicine, São Paulo State University/UNESP, Medical School, Botucatu, SP, Brazil
| | - Tânia Longo Mazzuco
- Division of Endocrinology of Medical Clinical Department, Universidade Estadual de Londrina (UEL), Londrina, PR, Brazil
| | | | - Nelma Veronica Marques
- Endocrine Unit and Neuroendocrinology Research Center, Medical School and Hospital Universitário Clementino Fraga Filho-Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - Leila Chimelli
- Neuropathology and Molecular Genetics Laboratory, Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde, Rio de Janeiro, RJ, Brazil
| | - Mauro Czepielewski
- Division of Endocrinology, Hospital de Clinicas de Porto Alegre (UFRGS), Porto Alegre, RS, Brazil
| | - Marcello D Bronstein
- Neuroendocrine Unit, Division of Endocrinology and Metabolism, Hospital das Clínicas, University of São Paulo Medical School, São Paulo, SP, Brazil
- Cellular and Molecular Endocrinology Laboratory/LIM25, Discipline of Endocrinology, Hospital das Clinicas HCFMUSP, Faculty of Medicine, University of Sao Paulo, São Paulo, SP, Brazil
| | - Julio Abucham
- Neuroendocrine Unit-Division of Endocrinology and Metabolism-Escola Paulista de Medicina-Universidade Federal de São Paulo (Unifesp), São Paulo, SP, Brazil
| | - Margaret de Castro
- Division of Endocrinology-Department of Internal Medicine, Ribeirao Preto Medical School-University of Sao Paulo, São Paulo, SP, Brazil
| | - Leandro Kasuki
- Endocrine Unit and Neuroendocrinology Research Center, Medical School and Hospital Universitário Clementino Fraga Filho-Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil
- Neuroendocrine Unit-Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde , Rio de Janeiro, RJ, Brazil
| | - Mônica Gadelha
- Endocrine Unit and Neuroendocrinology Research Center, Medical School and Hospital Universitário Clementino Fraga Filho-Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil
- Neuroendocrine Unit-Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde , Rio de Janeiro, RJ, Brazil
- Neuropathology and Molecular Genetics Laboratory, Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde, Rio de Janeiro, RJ, Brazil
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Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11115088] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Machine Learning and Artificial Intelligence (AI) more broadly have great immediate and future potential for transforming almost all aspects of medicine. However, in many applications, even outside medicine, a lack of transparency in AI applications has become increasingly problematic. This is particularly pronounced where users need to interpret the output of AI systems. Explainable AI (XAI) provides a rationale that allows users to understand why a system has produced a given output. The output can then be interpreted within a given context. One area that is in great need of XAI is that of Clinical Decision Support Systems (CDSSs). These systems support medical practitioners in their clinic decision-making and in the absence of explainability may lead to issues of under or over-reliance. Providing explanations for how recommendations are arrived at will allow practitioners to make more nuanced, and in some cases, life-saving decisions. The need for XAI in CDSS, and the medical field in general, is amplified by the need for ethical and fair decision-making and the fact that AI trained with historical data can be a reinforcement agent of historical actions and biases that should be uncovered. We performed a systematic literature review of work to-date in the application of XAI in CDSS. Tabular data processing XAI-enabled systems are the most common, while XAI-enabled CDSS for text analysis are the least common in literature. There is more interest in developers for the provision of local explanations, while there was almost a balance between post-hoc and ante-hoc explanations, as well as between model-specific and model-agnostic techniques. Studies reported benefits of the use of XAI such as the fact that it could enhance decision confidence for clinicians, or generate the hypothesis about causality, which ultimately leads to increased trustworthiness and acceptability of the system and potential for its incorporation in the clinical workflow. However, we found an overall distinct lack of application of XAI in the context of CDSS and, in particular, a lack of user studies exploring the needs of clinicians. We propose some guidelines for the implementation of XAI in CDSS and explore some opportunities, challenges, and future research needs.
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25
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Shirshahi V, Liu G. Enhancing the analytical performance of paper lateral flow assays: From chemistry to engineering. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2021.116200] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Liu PR, Lu L, Zhang JY, Huo TT, Liu SX, Ye ZW. Application of Artificial Intelligence in Medicine: An Overview. Curr Med Sci 2021; 41:1105-1115. [PMID: 34874486 PMCID: PMC8648557 DOI: 10.1007/s11596-021-2474-3] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 12/01/2020] [Indexed: 02/06/2023]
Abstract
Artificial intelligence (AI) is a new technical discipline that uses computer technology to research and develop the theory, method, technique, and application system for the simulation, extension, and expansion of human intelligence. With the assistance of new AI technology, the traditional medical environment has changed a lot. For example, a patient's diagnosis based on radiological, pathological, endoscopic, ultrasonographic, and biochemical examinations has been effectively promoted with a higher accuracy and a lower human workload. The medical treatments during the perioperative period, including the preoperative preparation, surgical period, and postoperative recovery period, have been significantly enhanced with better surgical effects. In addition, AI technology has also played a crucial role in medical drug production, medical management, and medical education, taking them into a new direction. The purpose of this review is to introduce the application of AI in medicine and to provide an outlook of future trends.
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Affiliation(s)
- Peng-ran Liu
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Lin Lu
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Jia-yao Zhang
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Tong-tong Huo
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Song-xiang Liu
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Zhe-wei Ye
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
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