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Open research questions in COVID-19 diagnosis using AI

210 unresolved questions extracted from the limitations and future-work sections of 422 COVID-19 diagnosis using AI papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • The gap in the current public health system in India is the lack of effective preventive measures for non-communicable diseases. The paper identifies the need for tailored approaches to address the specific needs and contexts of each country.

    Harnessing AI for public health: India's roadmap · 2024 · DOI

  • The evolving microbial world generating novel causative agents for pneumonia. The need for accurate, time-efficient, and deterministic diagnostic techniques. The challenge of differentiating pneumonia from other similar pathologies like congestive heart failure.

    Current Diagnostic Techniques for Pneumonia: A Scoping Review · 2024 · DOI

  • Development of non-invasive physiological parameters for pneumonia diagnosis, - Improvement of diagnostic techniques for pneumonia, - Exploration of new technologies for pneumonia diagnosis

    Current Diagnostic Techniques for Pneumonia: A Scoping Review · 2024 · DOI

  • Investing in capacity building and workforce development - Establishing robust data infrastructure and governance frameworks

    Harnessing AI for public health: India's roadmap · 2024 · DOI

  • Self-supervised learning (SSL) can reduce the need for labelled medical images, but the choice of pretext objective remains unclear for lung ultrasound (LUS).

    Which Pretext Task Transfers? Self-Supervised Pretraining Objectives for Lung Ultrasound · 2026

  • Further validation of the model is needed to establish its generalizability to other populations and settings. Comparison with other machine learning models or traditional prediction methods is needed. The model may be improved by incorporating additional clinical and laboratory indicators.

    A machine learning-based prediction model for treatment efficacy in smear and/or chest X-ray positive tuberculosis patients · 2026 · DOI

  • Traditional prediction methods have limitations in addressing the complexity of TB treatment. There is a need for a machine learning-based prediction model that integrates multiple clinical and laboratory indicators.

    A machine learning-based prediction model for treatment efficacy in smear and/or chest X-ray positive tuberculosis patients · 2026 · DOI

  • The study identifies a gap in current TB screening pipelines, which rely exclusively on deep semantic features from a single backbone. The gap is addressed by proposing a pipeline that combines semantic and gradient-texture information.

    Combining semantic CNN and HOG texture with LightGBM for explainable tuberculosis detection · 2026 · DOI

  • The risk of lung disease recurrence after a patient is declared cured remains a serious challenge. There is a need for an analytical approach capable of comprehensively integrating visual and clinical information for lung disease recurrence prediction.

    Performance Analysis of a Hybrid Deep Learning Framework Integrating CNN, RNN, LSTM, and ResNet50 for Lung Disease Recurrence Prediction Using Chest X-Ray Images and Post-Recovery Clinical Data · 2026 · DOI

  • The ResNet50 pre-trained architecture achieves identical 97.78% accuracy as CNN with F1-score 0.978, suggesting potential redundancy in the hybrid framework; ablation studies isolating the contribution of each component (CNN vs. ResNet50 vs. RNN vs. LSTM) and their interaction effects are needed to justify the four-model ensemble for lung disease recurrence prediction.

    Performance Analysis of a Hybrid Deep Learning Framework Integrating CNN, RNN, LSTM, and ResNet50 for Lung Disease Recurrence Prediction Using Chest X-Ray Images and Post-Recovery Clinical Data · 2026 · DOI

  • Incorporating explainable AI methods. Statistically robust validation strategies. Evaluation on independent and diverse clinical datasets.

    Enhancing Tuberculosis Detection from Chest X-Ray Images Using Deep Learning: Evaluating Multi-Architecture Performance and Efficiency · 2026 · DOI

  • Limited diagnostic resources and variability in manual Chest X-Ray interpretation. Need for accurate and efficient AI-assisted TB screening.

    Enhancing Tuberculosis Detection from Chest X-Ray Images Using Deep Learning: Evaluating Multi-Architecture Performance and Efficiency · 2026 · DOI

  • Detecting lung nodules in chest X-ray images remains challenging due to their small size, low contrast, and overlap with anatomical structures. The existing YOLOv12 model has limitations in detecting subtle nodules.

    Research on lung nodule detection in X-ray plain films based on improved YOLOv12 model · 2026 · DOI

  • Spectrum bias and case-control study designs can limit the generalizability of AI diagnostic studies. Dataset shift and performance drift over time can alter algorithm performance after deployment. The need for independent benchmarking initiatives and local verification studies before large-scale programmatic use of AI modalities in TB diagnosis.

    Artificial Intelligence for Tuberculosis Screening and Detection: From Evidence to Policy and Implementation · 2026 · DOI

  • Gaps in case detection and delays from symptom onset to treatment initiation limit national TB programs. The need for independent validation and prospective evaluation of AI modalities in TB diagnosis. The importance of considering the programmatic value of AI in TB screening and diagnosis, beyond diagnostic accuracy alone.

    Artificial Intelligence for Tuberculosis Screening and Detection: From Evidence to Policy and Implementation · 2026 · DOI

  • One of the main weaknesses of this study is that it uses only one dataset, which prevents the comparison of the model with different patient demographics. The existing data distribution is extremely biased towards adult groups, 10|Nwohiri et al. Period. Polytech. Elec. Eng. Comp. Sci. i.e., the fitted hyperparameters will not perfectly apply to pediatric patients. Moreover, given that the process of data division was done on an image-by-image basis but not on a patient-by- patient basis, two or more radiographs of the same patient can be found on the training and testing sets. External validation should take precedence in future work to tackle the limitations inherent in the use of single datasets. The optimized hyperparameter settings should be tested using independent X-ray data that are acquired from other hospitals to determine whether they are diag- nostic in real clinical setting. Scalability is another consideration because training deep learning models like InceptionV3 is resource- and time-intensive. Future research should focus on the use of hyperparameter tuning and less resource-intensive CNN architectures that train faster.

    Systematic Hyperparameter Optimization of Convolutional Neural Networks for Pneumonia Detection from Chest X-rays · 2026 · DOI

  • The lack of access to conventional diagnostic measures in underdeveloped and developing nations. The need for a lightweight and deployable diagnostic-aid solution.

    A Convolutional Neural Network Ensemble Model for Pneumonia Detection using Chest X-Ray Images · 2026 · DOI

  • Traditional statistical models are limited in their ability to handle high-dimensional and non-linear clinical data. There is a need for advanced machine learning approaches to analyze clinical data from TB patients.

    Clinical data analysis research on tuberculosis based on machine learning · 2026 · DOI

  • The study is limited by its reliance on a specific high-quality chest X-ray dataset. Further validation is required across diverse clinical settings and populations.

    Detection of Pneumonia from Chest X-Ray Images using Ensemble Deep Learning with A Voting Mechanism · 2026 · DOI

  • Enhancing data quality and creating explainable AI methodologies to increase the reliability of models in medical diagnostics.

    Detection of Pneumonia from Chest X-Ray Images using Ensemble Deep Learning with A Voting Mechanism · 2026 · DOI

  • Accurately identifying patients who benefit from targeted and immune-based therapies remains challenging due to tumor heterogeneity and variability in PD-L1 staining.

    A multi-task masked autoencoder with GAN-based augmentation for PD-L1 prediction from chest CT images · 2026 · DOI

  • Traditional machine learning and deep learning methods require consolidating large amounts of data into a centralized location, which raises privacy concerns. There is a need for a privacy-preserving distributed learning environment for lung disease classification.

    Federated Multi-Modal Deep Learning with Feature Fusion for Lung Disease Classification · 2026 · DOI

  • The paper does not mention any specific limitations. However, it is stated that future research is geared towards ensuring that there are no false positives.

    Cardiomegaly Prediction Using Deep Learning · 2026 · DOI

  • Future research is geared towards ensuring that there are no false positives. The paper suggests optimizing AI models for wider clinical integrations. The approach can be applied to other thoracic emergencies.

    Cardiomegaly Prediction Using Deep Learning · 2026 · DOI

  • The visual similarity of radiographic patterns introduces substantial diagnostic variability. Varied imaging conditions and patient-specific factors, such as age and comorbidities, affect diagnosis. The need for quick and accurate diagnosis methods is a significant challenge.

    Leveraging deep residual learning and gradient boosting for COVID-19 detection in chest radiographs · 2026 · DOI

Most-cited papers in COVID-19 diagnosis using AI

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210 open questions have been extracted from the limitations and future-work passages of 422 COVID-19 diagnosis using AI papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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