ArticlePLOS digital health2022
Ideal algorithms in healthcare: Explainable, dynamic, precise, autonomous, fair, and reproducible.
Article in PLOS digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- Assessing Algorithmic Fairness With a Multimodal Artificial Intelligence Model in Men of African and Non-African Origin on NRG Oncology Prostate Cancer Phase III Trials.JCO clinical cancer informatics · 2025Trial
- Optimisation and validation of line-of-therapy advancement algorithms in advanced/metastatic non-small-cell lung cancer: an I-O Optimise study.ESMO real world data and digital oncology · 2026Article
- Five tenets for advancing evidence-based precision medicine.Nature medicine · 2026Review
- Monitoring Deployed AI Systems in Health Care.ArXiv · 2026Article
- Article
- The Value of Clinical Decision Support in Healthcare: A Focus on Screening and Early Detection.Diagnostics (Basel, Switzerland) · 2025Review
- International, consensus-based, indications and treatment options for knee arthroplasty in acute fractures around the knee.Archives of orthopaedic and trauma surgery · 2025Review
- Perceptions and experiences of a multi-domain preventive health programme: a qualitative study informing future community-based health interventions in singapore.BMC public health · 2024Article
- Meta-research on reporting guidelines for artificial intelligence: are authors and reviewers encouraged enough in radiology, nuclear medicine, and medical imaging journals?Diagnostic and interventional radiology (Ankara, Turkey) · 2024Article
- Community-engaged artificial intelligence research: A scoping review.PLOS digital health · 2024Article
- MetaBakery: a Singularity implementation of bioBakery tools as a skeleton application for efficient HPC deconvolution of microbiome metagenomic sequencing data to machine learning ready information.Frontiers in microbiology · 2024Article
- Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Modeling Studies: Development and Validation.Journal of medical Internet research · 2023Article
- The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from "Nice to Have" to Mandatory Systems.Clinics and practice · 2023Review
- Machine Learning Techniques Applied to the Study of Drug Transporters.Molecules (Basel, Switzerland) · 2023Review
- The Quality and Utility of Artificial Intelligence in Patient Care.Deutsches Arzteblatt international · 2023Review
- Artificial Intelligence-enabled Decision Support in Surgery: State-of-the-art and Future Directions.Annals of surgery · 2023Article
- Article
- Addressing current challenges in optimization of lipid management following an ACS event: Outcomes of the ACS EuroPath III initiative.Clinical cardiology · 2023Article
- Deep Learning Solution for Quantification of Fluorescence Particles on a Membrane.Sensors (Basel, Switzerland) · 2023Article
- Discovery of associative patterns between workplace sound level and physiological wellbeing using wearable devices and empirical Bayes modeling.NPJ digital medicine · 2023Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Established guidelines describe minimum requirements for reporting algorithms in healthcare; it is equally important to objectify the characteristics of ideal algorithms that confer maximum potential benefits to patients, clinicians, and investigators. We propose a framework for ideal algorithms, including 6 desiderata: explainable (convey the relative importance of features in determining outputs), dynamic (capture temporal changes in physiologic signals and clinical events), precise (use high-resolution, multimodal data and aptly complex architecture), autonomous (learn with minimal supervision and execute without human input), fair (evaluate and mitigate implicit bias and social inequity), and reproducible (validated externally and prospectively and shared with academic communities). We present an ideal algorithms checklist and apply it to highly cited algorithms. Strategies and tools such as the predictive, descriptive, relevant (PDR) framework, the Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence (SPIRIT-AI) extension, sparse regression methods, and minimizing concept drift can help healthcare algorithms achieve these objectives, toward ideal algorithms in healthcare.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.