The next benchmark for the field is not broader

The gap

Evidence profile

Stated in the future work and limitations and inline gaps and cells research gap sections of the source papers, classified as general, spanning 7 journals. Those papers have been cited 5 times in total.

Supporting evidence — 7 representative gaps

• Artificial Intelligence and Big Data for Precision Medicine: A Review of Bioinformatics-Driven Healthcare Applications (2026) · Frontiers in Computer Science and Artificial Intelligence · cited 5× · doi

  This review has tried to do three things at once: synthesize a fragmented literature on AI in bioinformatics and precision medicine, propose a layered framework that captures the shared architecture across that literature, and put a numerical floor and ceiling on what current models actually deliver. The picture that emerges is cautiously optimistic. Reported accuracies above 90% are no longer rare in oncology imaging and cardiovascular monitoring; multi-omics integration is producing biologically plausible biomarker candidates (Manik, 2023; Manik et al., 2022; Manik et al., 2021a); and federated, privacy-preserving designs are beginning to address the sharing problem that has held the field back for years (Orthi et al., 2025). Real adoption will depend on engineering, not just algorithms. Better external validation, first-class explainability, fairness audits and tighter integration with clinician workflows are the levers that matter most over the next research cycle. We expect to see more digital-twin-based pre-deployment testing, more counterfactual reasoning, more cross-hospital benchmarks, and we hope more deliberate inclusion of under-represented populations in training and evaluation. In closing, AI is not going to replace the clinical encounter. The reviewed evidence does not support that claim, and we do not believe it should. What AI can do, and is increasingly doing, is hand the clinician a sharper, faster, more personalized picture of the patient in front of them. That alone is a meaningful change and it is the change worth building for.

  generalstated in future workevidence 5/5
  Keywords: manik bioinformatics ahmed review literature precision medicine current picture integration engineering external clinician personalized change
• Artificial Intelligence in Breast Pathology: Recent Advances in Multimodal Models, Explainability, and Clinical Applications (2026) · Journal of Clinical and Translational Pathology · doi

  Despite substantial progress, several challenges continue to limit the widespread clinical implementation of AI in breast pathology. These include variability in data quality and an- notation standards, limited dataset diversity, potential algo- rithmic bias, and concerns regarding model generalizability across institutions and populations. Additional barriers in- clude infrastructure and computational requirements, inte- gration into existing pathology workflows, and the need to maintain patient privacy, data security, and ethical stand- ards. Furthermore, issues related to interpretability, regula- tory approval, reimbursement, and standardized validation protocols remain critical considerations for safe and equitable adoption.

  generalstated in limitationsevidence 5/5
  Keywords: pathology despite substantial progress several challenges continue limit widespread clinical implementation breast include variability quality
• Artificial Intelligence-Based Healthcare Systems: A Review of Machine Learning, Deep Learning, Data Analytics, Supply Chain Management, and Electrical Engineering Technologies (2026) · Global Trends in Science and Technology · doi

  Let us be frank: if we are to have any hope of an AI healthcare system that is both efficient and worthy of our trust, we have to square up with the difficult issues of data quality, privacy, ethics and interpretability. You can’t have one without the other; it is a condition for any solution you want to see gain traction in the long run. The path ahead for AI in this sector is going to be transformative in the truest sense. We are facing a very dynamic future driven by advances in computing, data science and biomedical engineering. Once we leave the present deficiencies in our wake and let new innovations take root, tomorrow’s systems will be more predictive and decentralized, with a better feel for the individual patient. We want to do more than just improve the accuracy of a diagnosis or treatment, we want to make healthcare as a whole more accessible and on the right side of ethics. Consider XAI, or explainable artificial intelligence. It is perhaps the most important thing on the horizon. An AI should not be making decisions that impact human lives in a black box. There has to be transparency. A doctor has to be able to follow the logic of a recommendation before he puts his faith in it. XAI gives you that clarity and keeps accountability in the clinic. Or look at federated learning, which is essential for protecting patient privacy.

  generalstated in future workevidence 5/5
  Keywords: want healthcare privacy ethics patient frank hope system efficient worthy trust square difficult issues quality
• Intelligent Healthcare Tracking System Using Predictive Analytics (2026) · Indian Journal of Computer Science and Technology · doi

  Future work should focus on designing validation studies that meet regulatory standards for safety and effectiveness. Future work will focus on explainable artificial intelligence to build clinician trust, federated learning to combine data across hospitals without violating privacy, contextual awareness to reduce false alarms, edge computing for faster and more private processing, long term trend analysis for chronic disease detection, and regulatory validation through clinical trials.

  generalstated in inline gapsevidence 5/5
  Keywords: future focus validation regulatory designing meet standards safety effectiveness explainable artificial intelligence build clinician trust
• The Role of Artificial Intelligence in Early Disease Detection Techniques, Applications, Challenges and Future Directions (2026) · International Journal of Science and Research (IJSR) · doi

  Ciona Dewan1, Raghu Raja Mehra2 1Invictus International School, Amritsar Email: cionadewan025[at]gmail.com 2Invictus International School, Amritsar Email: raghu[at]invictusschool.edu.in Abstract: Early and accurate detection of disease is one of the most decisive factors in patient survival, treatment cost and quality of life. Artificial intelligence (AI), and in particular machine learning and deep learning, has emerged as a powerful ally in this effort, capable of analysing medical images, electronic health records, laboratory results and wearable-sensor data with remarkable speed and consistency. This paper reviews the role of AI in early disease detection, surveying the principal techniques, the typical detection pipeline, and applications across cancer, cardiovascular, ophthalmic and neurological disorders. A comparison with conventional diagnostic methods shows that AI systems can match or exceed clinician-level accuracy in several screening tasks while operating at scale. The paper then proposes an integrated, privacy-preserving and explainable framework for clinical deployment, and critically examines the advantages, limitations, and ethical and regulatory challenges involved. Finally, it outlines future directions—including federated learning, explainable AI and continuous wearable monitoring—that could make trustworthy, equitable early detection a routine part of care.

  generalstated in future workevidence 5/5
  Keywords: detection learning early disease explainable raghu invictus international school amritsar email artificial intelligence machine deep
• Artificial Intelligence in Osteoarthritis Pain Management: Current Applications and Future Perspectives (2026) · International Journal of Drug Delivery Technology · doi

  Future AI systems are expected to integrate imaging findings, clinical laboratory biomarkers, wearable sensor data, and molecular information to provide individualized prediction of pain progression and treatment response. Advances in explainable AI, federated learning, and multimodal modeling are likely to improve transparency while maintaining privacy. Successful patient implementation will require collaboration among clinicians, engineers, data scientists, regulators, and patients.

  generalstated in future workevidence 5/5
  Keywords: future systems expected integrate imaging clinical laboratory biomarkers wearable sensor molecular information provide individualized prediction
• From abnormality detection to decision-grade deployment: Strengthening the next translational step for DeepCXR (2026) · The Indian Journal of Medical Research · doi

  The next benchmark for the field is not broader deployment alone, but deployment that is transparent, monitored, equitable, and demonstrably linked to patient-important and system-level outcomes. - Clinically useful AI must be judged not only by discrimination metrics, but also by transparency, subgroup performance, calibration, implementation logic, and lifecycle monitoring.

  generalstated in cells research gapevidence 5/5
  Keywords: next benchmark field broader deployment alone transparent monitored

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