ReviewFrontiers in digital health2022
12 Plagues of AI in Healthcare: A Practical Guide to Current Issues With Using Machine Learning in a Medical Context.
Review in Frontiers in digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence-based tools for patient support to enhance medication adherence: a focused review.Frontiers in digital health · 2025Pooled it
- Parcellation-based tractographic modeling of the salience network through meta-analysis.Brain and behavior · 2022Pooled it
- Machine Learning Techniques for the Analysis of the Influence of Blood Gasometry Parameters on Acid-Base Homeostasis in Pediatric Patients.Diagnostics (Basel, Switzerland) · 2025Article
- AI for dental imaging: impressive in vitro, but what about in practice?Evidence-based dentistry · 2025Article
- Proposing core competencies for physicians in using artificial intelligence tools in clinical practice.Internal medicine journal · 2025Article
- Psychological Factors Influencing Appropriate Reliance on AI-enabled Clinical Decision Support Systems: Experimental Web-Based Study Among Dermatologists.Journal of medical Internet research · 2025Article
- Ethical theories, governance models, and strategic frameworks for responsible AI adoption and organizational success.Frontiers in artificial intelligence · 2025Review
- UpSMART: five years of digital innovation in cancer clinical research-achievements, challenges, and recommendations.Frontiers in digital health · 2025Article
- Measuring Raven's Progressive Matrices Combining Eye-Tracking Technology and Machine Learning (ML) Models.Journal of Intelligence · 2024Article
- Machine Learning Operations in Health Care: A Scoping Review.Mayo Clinic proceedings. Digital health · 2024Review
- Strengths, weaknesses, opportunities, and threats of using AI-enabled technology in sleep medicine: a commentary.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2024Review
- The Brain Connectome for Clinical Neuroscience.Advances in experimental medicine and biology · 2024Review
- Prediction of orthognathic surgery plan from 3D cephalometric analysis via deep learning.BMC oral health · 2023Article
- Review
- Disability risk prediction model based on machine learning among Chinese healthy older adults: results from the China Health and Retirement Longitudinal Study.Frontiers in public health · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The healthcare field has long been promised a number of exciting and powerful applications of Artificial Intelligence (AI) to improve the quality and delivery of health care services. AI techniques, such as machine learning (ML), have proven the ability to model enormous amounts of complex data and biological phenomena in ways only imaginable with human abilities alone. As such, medical professionals, data scientists, and Big Tech companies alike have all invested substantial time, effort, and funding into these technologies with hopes that AI systems will provide rigorous and systematic interpretations of large amounts of data that can be leveraged to augment clinical judgments in real time. However, despite not being newly introduced, AI-based medical devices have more than often been limited in their true clinical impact that was originally promised or that which is likely capable, such as during the current COVID-19 pandemic. There are several common pitfalls for these technologies that if not prospectively managed or adjusted in real-time, will continue to hinder their performance in high stakes environments outside of the lab in which they were created. To address these concerns, we outline and discuss many of the problems that future developers will likely face that contribute to these failures. Specifically, we examine the field under four lenses: approach, data, method and operation. If we continue to prospectively address and manage these concerns with reliable solutions and appropriate system processes in place, then we as a field may further optimize the clinical applicability and adoption of medical based AI technology moving forward.
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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.