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Neuroscience and Biobehavioral Reviews Nov 2022MONTELEONE, A.M., F. Pellegrino, G. Croatto, M. Carfagno, A. Hilbert, J. Treasure, T. Wade, C. Bulik, S. Zipfel, P. Hay, U. Schmidt, G. Castellini, A. Favaro, F.... (Review)
Review
MONTELEONE, A.M., F. Pellegrino, G. Croatto, M. Carfagno, A. Hilbert, J. Treasure, T. Wade, C. Bulik, S. Zipfel, P. Hay, U. Schmidt, G. Castellini, A. Favaro, F. Fernandez-Aranda, J. Il Shin, U. Voderholzer, V. Ricca, D. Moretti, D. Busatta, G. Abbate-Daga, F. Ciullini, G. Cascino, F. Monaco, C.U. Correll and M. Solmi. Treatment of Eating Disorders: a systematic meta-review of meta-analyses and network meta-analyses. NEUROSCI BIOBEHAV REV 21(1) XXX-XXX, 2022.- Treatment efficacy for eating disorders (EDs) is modest and guidelines differ. We summarized findings/quality of (network) meta-analyses (N)MA of randomized controlled trials (RCTs) in EDs. Systematic meta-review ((N)MA of RCTs, ED, active/inactive control), using (anorexia or bulimia or eating disorder) AND (meta-analy*) in PubMed/PsycINFO/Cochrane database up to December 15th, 2020. Standardized mean difference, odds/risk ratio vs control were summarized at end of treatment and follow-up. Interventions involving family (family-based therapy, FBT) outperformed active control in adults/adolescents with anorexia nervosa (AN), and in adolescents with bulimia nervosa (BN). In adults with BN, individual cognitive behavioural therapy (CBT)-ED had the broadest efficacy versus active control; also, antidepressants outperformed active. In mixed age groups with binge-eating disorder (BED), psychotherapy, and lisdexamfetamine outperformed active control. Antidepressants, stimulants outperformed placebo, despite lower acceptability, as did CBT-ED versus waitlist/no treatment. Family-based therapy is effective in AN and BN (adolescents). CBT-ED has the largest efficacy in BN (adults), followed by antidepressants, as well as psychotherapy in BED (mixed). Medications have short-term efficacy in BED (adults).
Topics: Adolescent; Adult; Humans; Antidepressive Agents; Binge-Eating Disorder; Bulimia; Bulimia Nervosa; Feeding and Eating Disorders; Network Meta-Analysis; Meta-Analysis as Topic
PubMed: 36084848
DOI: 10.1016/j.neubiorev.2022.104857 -
BMJ (Clinical Research Ed.) Mar 2022To determine the comparative effectiveness and safety of psychological interventions for chronic low back pain. (Meta-Analysis)
Meta-Analysis
OBJECTIVE
To determine the comparative effectiveness and safety of psychological interventions for chronic low back pain.
DESIGN
Systematic review with network meta-analysis.
DATA SOURCES
Medline, Embase, PsycINFO, Cochrane Central Register of Controlled Trials, Web of Science, SCOPUS, and CINAHL from database inception to 31 January 2021.
ELIGIBILITY CRITERIA FOR STUDY SELECTION
Randomised controlled trials comparing psychological interventions with any comparison intervention in adults with chronic, non-specific low back pain. Two reviewers independently screened studies, extracted data, and assessed risk of bias and confidence in the evidence. Primary outcomes were physical function and pain intensity. A random effects network meta-analysis using a frequentist approach was performed at post-intervention (from the end of treatment to <2 months post-intervention); and at short term (≥2 to <6 months post-intervention), mid-term (≥6 to <12 months post-intervention), and long term follow-up (≥12 months post-intervention). Physiotherapy care was the reference comparison intervention. The design-by-treatment interaction model was used to assess global inconsistency and the Bucher method was used to assess local inconsistency.
RESULTS
97 randomised controlled trials involving 13 136 participants and 17 treatment nodes were included. Inconsistency was detected at short term and mid-term follow-up for physical function, and short term follow-up for pain intensity, and were resolved through sensitivity analyses. For physical function, cognitive behavioural therapy (standardised mean difference 1.01, 95% confidence interval 0.58 to 1.44), and pain education (0.62, 0.08 to 1.17), delivered with physiotherapy care, resulted in clinically important improvements at post-intervention (moderate quality evidence). The most sustainable effects of treatment for improving physical function were reported with pain education delivered with physiotherapy care, at least until mid-term follow-up (0.63, 0.25 to 1.00; low quality evidence). No studies investigated the long term effectiveness of pain education delivered with physiotherapy care. For pain intensity, behavioural therapy (1.08, 0.22 to 1.94), cognitive behavioural therapy (0.92, 0.43 to 1.42), and pain education (0.91, 0.37 to 1.45), delivered with physiotherapy care, resulted in clinically important effects at post-intervention (low to moderate quality evidence). Only behavioural therapy delivered with physiotherapy care maintained clinically important effects on reducing pain intensity until mid-term follow-up (1.01, 0.41 to 1.60; high quality evidence).
CONCLUSIONS
For people with chronic, non-specific low back pain, psychological interventions are most effective when delivered in conjunction with physiotherapy care (mainly structured exercise). Pain education programmes (low to moderate quality evidence) and behavioural therapy (low to high quality evidence) result in the most sustainable effects of treatment; however, uncertainty remains as to their long term effectiveness. Although inconsistency was detected, potential sources were identified and resolved.
SYSTEMATIC REVIEW REGISTRATION
PROSPERO CRD42019138074.
Topics: Adult; Cognitive Behavioral Therapy; Humans; Low Back Pain; Network Meta-Analysis; Psychosocial Intervention; Research Design
PubMed: 35354560
DOI: 10.1136/bmj-2021-067718 -
BMC Pediatrics Nov 2021Motor deficiencies are observed in a large number of children with ADHD. Especially fine motor impairments can lead to academic underachievement, low self-esteem and... (Review)
Review
BACKGROUND
Motor deficiencies are observed in a large number of children with ADHD. Especially fine motor impairments can lead to academic underachievement, low self-esteem and frustration in affected children. Despite these far-reaching consequences, fine motor deficiencies have remained widely undertreated in the ADHD population. The aim of this review was to systematically map the evidence on existing training programs for remediating fine motor impairments in children with ADHD and to assess their effectiveness.
METHODS
The scoping review followed the PRISMA-ScR guidelines. In March 2020, PsycINFO, MEDLINE (PubMed), Web of Science, Google Scholar and The Cochrane Database of Systematic Reviews were searched for evidence. The eligibility criteria and the data charting process followed the PICO framework, complemented by study design. The investigated population included children with a formal ADHD diagnosis (either subtype) or elevated ADHD symptoms aged between 4 and 12 years, both on and off medication. All training interventions aiming at improving fine motor skills, having a fine motor component or fine motor improvements as a secondary outcome were assessed for eligibility; no comparators were specified.
RESULTS
Twelve articles were included in the final report, comprising observational and experimental studies as well as a review. Both offline and online or virtual training interventions were reported, often accompanied by physical activity and supplemented by training sessions at home. The training programs varied in length and intensity, but generally comprised several weeks and single or multiple training sessions per week. All interventions including more than one session were effective in the treatment of fine motor deficiencies in children with ADHD and had a wide range of additional positive outcomes. The effects could be maintained at follow-up.
CONCLUSIONS
Fine motor training in children with ADHD can be very effective and multiple approaches including specific fine motor and cognitive training components, some kind of physical activity, feedback mechanisms, or multimodal treatments can be successful. Training programs need to be tailored to the specific characteristics of the ADHD population. A mHealth approach using serious games could be promising in this context due to its strong motivational components.
Topics: Attention Deficit Disorder with Hyperactivity; Child; Child, Preschool; Educational Status; Humans; Research Design
PubMed: 34736439
DOI: 10.1186/s12887-021-02916-5 -
The American Journal of Psychiatry Jan 2023The aim of this study was to catalog and evaluate response biomarkers correlated with autism spectrum disorder (ASD) symptoms to improve clinical trials. (Meta-Analysis)
Meta-Analysis Review
OBJECTIVE
The aim of this study was to catalog and evaluate response biomarkers correlated with autism spectrum disorder (ASD) symptoms to improve clinical trials.
METHODS
A systematic review of MEDLINE, Embase, and Scopus was conducted in April 2020. Seven criteria were applied to focus on original research that includes quantifiable response biomarkers measured alongside ASD symptoms. Interventional studies or human studies that assessed the correlation between biomarkers and ASD-related behavioral measures were included.
RESULTS
A total of 5,799 independent records yielded 280 articles for review that reported on 940 biomarkers, 755 of which were unique to a single publication. Molecular biomarkers were the most frequently assayed, including cytokines, growth factors, measures of oxidative stress, neurotransmitters, and hormones, followed by neurophysiology (e.g., EEG and eye tracking), neuroimaging (e.g., functional MRI), and other physiological measures. Studies were highly heterogeneous, including in phenotypes, demographic characteristics, tissues assayed, and methods for biomarker detection. With a median total sample size of 64, almost all of the reviewed studies were only powered to identify biomarkers with large effect sizes. Reporting of individual-level values and summary statistics was inconsistent, hampering mega- and meta-analysis. Biomarkers assayed in multiple studies yielded mostly inconsistent results, revealing a "replication crisis."
CONCLUSIONS
There is currently no response biomarker with sufficient evidence to inform ASD clinical trials. This review highlights methodological imperatives for ASD biomarker research necessary to make definitive progress: consistent experimental design, correction for multiple comparisons, formal replication, sharing of sample-level data, and preregistration of study designs. Systematic "big data" analyses of multiple potential biomarkers could accelerate discovery.
Topics: Humans; Autism Spectrum Disorder; Biomarkers; Phenotype; Magnetic Resonance Imaging; Research Design
PubMed: 36475375
DOI: 10.1176/appi.ajp.21100992 -
International Journal of Environmental... Jun 2020Resolving late failure of dental implant is difficult and costly; however, only few reviews have addressed the risk factors associated with late failure of dental...
Resolving late failure of dental implant is difficult and costly; however, only few reviews have addressed the risk factors associated with late failure of dental implant. The aim of this literature review was to summarize the influences of different potential risk factors on the incidence of late dental implant failure. The protocol of this systematic review was prepared and implemented based on the PRISMA (Preferred reporting items for systematic reviews and meta-analyses) guideline. In December 2018, studies published within the previous 10 years on late dental implant failure were selected by fulfilling the eligibility criteria and the risk factors identified in qualified studies were extracted by using a predefined extraction template. Fourteen eligible studies were assessed. The common risk factors for late failure were divided into three groups according to whether they were related to (1) the patient history (radiation therapy, periodontitis, bruxism and early implant failure), (2) clinical parameters (posterior implant location and bone grade 4) or (3) decisions made by the clinician (low initial stability, more than one implant placed during surgery, inflammation at the surgical site during the first year or using an overdenture with conus-type connection). Clinicians should be cautions throughout the treatment process of dental implant-from the initial examination to the treatment planning, surgical operation and prosthesis selection-in order to minimize the risk of late failure of dental implant.
Topics: Dental Implants; Dental Restoration Failure; Humans; Periodontitis; Research Design; Risk Factors
PubMed: 32498256
DOI: 10.3390/ijerph17113931 -
The Cochrane Database of Systematic... Feb 2018Recruiting participants to trials can be extremely difficult. Identifying strategies that improve trial recruitment would benefit both trialists and health research. (Review)
Review
BACKGROUND
Recruiting participants to trials can be extremely difficult. Identifying strategies that improve trial recruitment would benefit both trialists and health research.
OBJECTIVES
To quantify the effects of strategies for improving recruitment of participants to randomised trials. A secondary objective is to assess the evidence for the effect of the research setting (e.g. primary care versus secondary care) on recruitment.
SEARCH METHODS
We searched the Cochrane Methodology Review Group Specialised Register (CMR) in the Cochrane Library (July 2012, searched 11 February 2015); MEDLINE and MEDLINE In Process (OVID) (1946 to 10 February 2015); Embase (OVID) (1996 to 2015 Week 06); Science Citation Index & Social Science Citation Index (ISI) (2009 to 11 February 2015) and ERIC (EBSCO) (2009 to 11 February 2015).
SELECTION CRITERIA
Randomised and quasi-randomised trials of methods to increase recruitment to randomised trials. This includes non-healthcare studies and studies recruiting to hypothetical trials. We excluded studies aiming to increase response rates to questionnaires or trial retention and those evaluating incentives and disincentives for clinicians to recruit participants.
DATA COLLECTION AND ANALYSIS
We extracted data on: the method evaluated; country in which the study was carried out; nature of the population; nature of the study setting; nature of the study to be recruited into; randomisation or quasi-randomisation method; and numbers and proportions in each intervention group. We used a risk difference to estimate the absolute improvement and the 95% confidence interval (CI) to describe the effect in individual trials. We assessed heterogeneity between trial results. We used GRADE to judge the certainty we had in the evidence coming from each comparison.
MAIN RESULTS
We identified 68 eligible trials (24 new to this update) with more than 74,000 participants. There were 63 studies involving interventions aimed directly at trial participants, while five evaluated interventions aimed at people recruiting participants. All studies were in health care.We found 72 comparisons, but just three are supported by high-certainty evidence according to GRADE.1. Open trials rather than blinded, placebo trials. The absolute improvement was 10% (95% CI 7% to 13%).2. Telephone reminders to people who do not respond to a postal invitation. The absolute improvement was 6% (95% CI 3% to 9%). This result applies to trials that have low underlying recruitment. We are less certain for trials that start out with moderately good recruitment (i.e. over 10%).3. Using a particular, bespoke, user-testing approach to develop participant information leaflets. This method involved spending a lot of time working with the target population for recruitment to decide on the content, format and appearance of the participant information leaflet. This made little or no difference to recruitment: absolute improvement was 1% (95% CI -1% to 3%).We had moderate-certainty evidence for eight other comparisons; our confidence was reduced for most of these because the results came from a single study. Three of the methods were changes to trial management, three were changes to how potential participants received information, one was aimed at recruiters, and the last was a test of financial incentives. All of these comparisons would benefit from other researchers replicating the evaluation. There were no evaluations in paediatric trials.We had much less confidence in the other 61 comparisons because the studies had design flaws, were single studies, had very uncertain results or were hypothetical (mock) trials rather than real ones.
AUTHORS' CONCLUSIONS
The literature on interventions to improve recruitment to trials has plenty of variety but little depth. Only 3 of 72 comparisons are supported by high-certainty evidence according to GRADE: having an open trial and using telephone reminders to non-responders to postal interventions both increase recruitment; a specialised way of developing participant information leaflets had little or no effect. The methodology research community should improve the evidence base by replicating evaluations of existing strategies, rather than developing and testing new ones.
Topics: Humans; Patient Education as Topic; Patient Selection; Randomized Controlled Trials as Topic; Reminder Systems; Sample Size; Telephone
PubMed: 29468635
DOI: 10.1002/14651858.MR000013.pub6 -
PloS One 2011Delphi technique is a structured process commonly used to developed healthcare quality indicators, but there is a little recommendation for researchers who wish to use... (Meta-Analysis)
Meta-Analysis Review
OBJECTIVE
Delphi technique is a structured process commonly used to developed healthcare quality indicators, but there is a little recommendation for researchers who wish to use it. This study aimed 1) to describe reporting of the Delphi method to develop quality indicators, 2) to discuss specific methodological skills for quality indicators selection 3) to give guidance about this practice.
METHODOLOGY AND MAIN FINDING
Three electronic data bases were searched over a 30 years period (1978-2009). All articles that used the Delphi method to select quality indicators were identified. A standardized data extraction form was developed. Four domains (questionnaire preparation, expert panel, progress of the survey and Delphi results) were assessed. Of 80 included studies, quality of reporting varied significantly between items (9% for year's number of experience of the experts to 98% for the type of Delphi used). Reporting of methodological aspects needed to evaluate the reliability of the survey was insufficient: only 39% (31/80) of studies reported response rates for all rounds, 60% (48/80) that feedback was given between rounds, 77% (62/80) the method used to achieve consensus and 57% (48/80) listed quality indicators selected at the end of the survey. A modified Delphi procedure was used in 49/78 (63%) with a physical meeting of the panel members, usually between Delphi rounds. Median number of panel members was 17(Q1:11; Q3:31). In 40/70 (57%) studies, the panel included multiple stakeholders, who were healthcare professionals in 95% (38/40) of cases. Among 75 studies describing criteria to select quality indicators, 28 (37%) used validity and 17(23%) feasibility.
CONCLUSION
The use and reporting of the Delphi method for quality indicators selection need to be improved. We provide some guidance to the investigators to improve the using and reporting of the method in future surveys.
Topics: Delphi Technique; Guidelines as Topic; Humans; Quality Indicators, Health Care; Research Design
PubMed: 21694759
DOI: 10.1371/journal.pone.0020476 -
BMJ (Clinical Research Ed.) Mar 2020To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for...
OBJECTIVE
To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging with that of expert clinicians.
DESIGN
Systematic review.
DATA SOURCES
Medline, Embase, Cochrane Central Register of Controlled Trials, and the World Health Organization trial registry from 2010 to June 2019.
ELIGIBILITY CRITERIA FOR SELECTING STUDIES
Randomised trial registrations and non-randomised studies comparing the performance of a deep learning algorithm in medical imaging with a contemporary group of one or more expert clinicians. Medical imaging has seen a growing interest in deep learning research. The main distinguishing feature of convolutional neural networks (CNNs) in deep learning is that when CNNs are fed with raw data, they develop their own representations needed for pattern recognition. The algorithm learns for itself the features of an image that are important for classification rather than being told by humans which features to use. The selected studies aimed to use medical imaging for predicting absolute risk of existing disease or classification into diagnostic groups (eg, disease or non-disease). For example, raw chest radiographs tagged with a label such as pneumothorax or no pneumothorax and the CNN learning which pixel patterns suggest pneumothorax.
REVIEW METHODS
Adherence to reporting standards was assessed by using CONSORT (consolidated standards of reporting trials) for randomised studies and TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) for non-randomised studies. Risk of bias was assessed by using the Cochrane risk of bias tool for randomised studies and PROBAST (prediction model risk of bias assessment tool) for non-randomised studies.
RESULTS
Only 10 records were found for deep learning randomised clinical trials, two of which have been published (with low risk of bias, except for lack of blinding, and high adherence to reporting standards) and eight are ongoing. Of 81 non-randomised clinical trials identified, only nine were prospective and just six were tested in a real world clinical setting. The median number of experts in the comparator group was only four (interquartile range 2-9). Full access to all datasets and code was severely limited (unavailable in 95% and 93% of studies, respectively). The overall risk of bias was high in 58 of 81 studies and adherence to reporting standards was suboptimal (<50% adherence for 12 of 29 TRIPOD items). 61 of 81 studies stated in their abstract that performance of artificial intelligence was at least comparable to (or better than) that of clinicians. Only 31 of 81 studies (38%) stated that further prospective studies or trials were required.
CONCLUSIONS
Few prospective deep learning studies and randomised trials exist in medical imaging. Most non-randomised trials are not prospective, are at high risk of bias, and deviate from existing reporting standards. Data and code availability are lacking in most studies, and human comparator groups are often small. Future studies should diminish risk of bias, enhance real world clinical relevance, improve reporting and transparency, and appropriately temper conclusions.
STUDY REGISTRATION
PROSPERO CRD42019123605.
Topics: Algorithms; Bias; Deep Learning; Diagnostic Imaging; Humans; Image Processing, Computer-Assisted; Physicians; Randomized Controlled Trials as Topic; Research Design
PubMed: 32213531
DOI: 10.1136/bmj.m689 -
JAMA Network Open Jul 2023Plant-based diets are known to improve cardiometabolic risk in the general population, but their effects on people at high risk of cardiovascular diseases (CVDs) remain... (Meta-Analysis)
Meta-Analysis
IMPORTANCE
Plant-based diets are known to improve cardiometabolic risk in the general population, but their effects on people at high risk of cardiovascular diseases (CVDs) remain inconclusive.
OBJECTIVE
To assess the association of vegetarian diets with major cardiometabolic risk factors, including low-density lipoprotein cholesterol (LDL-C), hemoglobin A1c (HbA1c), systolic blood pressure (SBP), and body weight in people with or at high risk of CVDs.
DATA SOURCES
This meta-analysis was registered before the study was conducted. Systematic searches performed included Embase, MEDLINE, CINAHL, and CENTRAL from inception until July 31, 2021.
STUDY SELECTION
Eligible randomized clinical trials (RCTs) that delivered vegetarian diets in adults with or at high risk of CVDs and measured LDL-C, HbA1c or SBP were included. Of the 7871 records screened, 29 (0.4%; 20 studies) met inclusion criteria.
DATA EXTRACTION AND SYNTHESIS
Two reviewers independently extracted data including demographics, study design, sample size, and diet description, and performed risk of bias assessment. A random-effects model was used to assess mean changes in LDL-C, HbA1c, SBP, and body weight. The overall certainty of evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) tool.
MAIN OUTCOMES AND MEASURES
Mean differences between groups in changes (preintervention vs postintervention) of LDL-C, HbA1c, and SBP; secondary outcomes were changes in body weight and energy intake.
RESULTS
Twenty RCTs involving 1878 participants (range of mean age, 28-64 years) were included, and mean duration of intervention was 25.4 weeks (range, 2 to 24 months). Four studies targeted people with CVDs, 7 focused on diabetes, and 9 included people with at least 2 CVD risk factors. Overall, relative to all comparison diets, meta-analyses showed that consuming vegetarian diets for an average of 6 months was associated with decreased LDL-C, HbA1c, and body weight by 6.6 mg/dL (95% CI, -10.1 to -3.1), 0.24% (95% CI, -0.40 to -0.07), and 3.4 kg (95% CI, -4.9 to -2.0), respectively, but the association with SBP was not significant (-0.1 mm Hg; 95% CI, -2.8 to 2.6). The GRADE assessment showed a moderate level of evidence for LDL-C and HbA1c reduction.
CONCLUSIONS AND RELEVANCE
In this study, consuming a vegetarian diet was associated with significant improvements in LDL-C, HbA1c and body weight beyond standard therapy in individuals at high risk of CVDs. Additional high-quality trials are warranted to further elucidate the effects of healthy plant-based diets in people with CVDs.
Topics: Adult; Humans; Middle Aged; Cardiovascular Diseases; Cholesterol, LDL; Glycated Hemoglobin; Vegetarians; Research Design; Body Weight
PubMed: 37490288
DOI: 10.1001/jamanetworkopen.2023.25658 -
Advances in Therapy Aug 2023Several studies have emphasized the potential of artificial intelligence (AI) and its subfields, such as machine learning (ML), as emerging and feasible approaches to... (Meta-Analysis)
Meta-Analysis
INTRODUCTION
Several studies have emphasized the potential of artificial intelligence (AI) and its subfields, such as machine learning (ML), as emerging and feasible approaches to optimize patient care in oncology. As a result, clinicians and decision-makers are faced with a plethora of reviews regarding the state of the art of applications of AI for head and neck cancer (HNC) management. This article provides an analysis of systematic reviews on the current status, and of the limitations of the application of AI/ML as adjunctive decision-making tools in HNC management.
METHODS
Electronic databases (PubMed, Medline via Ovid, Scopus, and Web of Science) were searched from inception until November 30, 2022. The study selection, searching and screening processes, inclusion, and exclusion criteria followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. A risk of bias assessment was conducted using a tailored and modified version of the Assessment of Systematic Review (AMSTAR-2) tool and quality assessment using the Risk of Bias in Systematic Reviews (ROBIS) guidelines.
RESULTS
Of the 137 search hits retrieved, 17 fulfilled the inclusion criteria. This analysis of systematic reviews revealed that the application of AI/ML as a decision aid in HNC management can be thematized as follows: (1) detection of precancerous and cancerous lesions within histopathologic slides; (2) prediction of the histopathologic nature of a given lesion from various sources of medical imaging; (3) prognostication; (4) extraction of pathological findings from imaging; and (5) different applications in radiation oncology. In addition, the challenges in implementation of AI/ML models for clinical evaluations include the lack of standardized methodological guidelines for the collection of clinical images, development of these models, reporting of their performance, external validation procedures, and regulatory frameworks.
CONCLUSION
At present, there is a paucity of evidence to suggest the adoption of these models in clinical practice due to the aforementioned limitations. Therefore, this manuscript highlights the need for development of standardized guidelines to facilitate the adoption and implementation of these models in the daily clinical practice. In addition, adequately powered, prospective, randomized controlled trials are urgently needed to further assess the potential of AI/ML models in real-world clinical settings for the management of HNC.
Topics: Humans; Artificial Intelligence; Head and Neck Neoplasms; Machine Learning; Prospective Studies; Research Design
PubMed: 37291378
DOI: 10.1007/s12325-023-02527-9