Clinical decision-making: Ai, digital health, humans

Maria Silva

doi:10.35841/aapcgp-8.2.197

Opinion Article - Journal of Primary Care and General Practice (2025) Volume 8, Issue 2

Clinical decision-making: Ai, digital health, humans

Maria Silva^*

Department of Primary Care, University of Sao Paulo, Sao Paulo, Brazil

*Corresponding Author:: Maria Silva
Department of Primary Care
University of Sao Paulo, Sao Paulo, Brazil.
E-mail: maria.silva@usp.br

Received : 01-Apr-2025, Manuscript No. aapcgp-197; Editor assigned : 03-Apr-2025, PreQC No. aapcgp-197(PQ); Reviewed : 23-Apr-2025, QC No aapcgp-197; Revised : 02-May-2025, Manuscript No. aapcgp-197(R); Published : 13-May-2025 , DOI : 10.35841/aapcgp-8.2.197

Citation: Silva M. Clinical decision-making: Ai, digital health, humans. aapcgp. 2025;08(02):197.

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Introduction

This article explores how Artificial Intelligence (AI) is transforming clinical decision-making. It highlights the vast opportunities AI offers, like improving diagnostic accuracy and personalizing treatments, but also points out significant challenges. These include issues with data quality, model interpretability, ethical considerations, and the need for robust regulatory frameworks. The piece emphasizes balancing innovation with patient safety and trust, suggesting AI's true potential is realized through collaborative, multidisciplinary approaches [1].

This scoping review examines how digital health technologies influence clinical decision-making, identifying key facilitators, barriers, and strategies for implementation. It finds that these technologies can enhance data access and communication, improving decision accuracy. However, issues like digital literacy, system interoperability, and clinician resistance present significant hurdles. The review proposes strategies focusing on user-centered design, comprehensive training, and policy support to maximize the benefits of digital health in clinical settings [2].

This article delves into the concept of Explainable Artificial Intelligence (XAI) as it applies to clinical decision-making. It highlights the critical need for AI systems to not just provide answers but also explain their reasoning, especially in healthcare where transparency and trust are paramount. The authors discuss various XAI techniques and their potential to foster clinician confidence, improve diagnostic processes, and navigate ethical challenges, ultimately making AI more acceptable and effective in clinical practice [3].

This qualitative study investigates how knowledge translation can enhance clinical decision-making in low-resource settings. It identifies that despite existing guidelines, contextual factors like limited resources, heavy workloads, and inadequate training often hinder their application. The research underscores the importance of local adaptation of evidence, peer learning, and strong leadership in facilitating the uptake of knowledge. Effective knowledge translation strategies are crucial for improving patient outcomes in these challenging environments [4].

This qualitative study explores the profound role of experience in shaping clinical decision-making among healthcare professionals. It reveals that seasoned clinicians rely on a blend of theoretical knowledge, intuition, and accumulated practical experience to navigate complex patient scenarios. The findings suggest that experience fosters pattern recognition, critical thinking, and a nuanced understanding of individual patient needs, which are invaluable for effective and patient-centered care. It emphasizes the continuous learning aspect throughout a clinician's career [5].

This systematic review investigates the implementation of shared decision-making (SDM) in various clinical practices. It highlights that SDM, which involves clinicians and patients collaboratively making healthcare choices, leads to improved patient satisfaction, adherence to treatment, and better health outcomes. The review identifies key components for successful SDM, such as effective communication, patient education, and a supportive organizational culture, while also noting barriers like time constraints and differing perceptions of roles [6].

This scoping review synthesizes clinicians' perspectives on integrating Artificial Intelligence (AI) into clinical decision-making. It reveals that while clinicians acknowledge AI's potential to enhance diagnostic accuracy and efficiency, they also express concerns about data privacy, accountability, and the loss of human touch in patient care. The review emphasizes the need for AI systems to be transparent, reliable, and designed to augment, rather than replace, clinical judgment, fostering a collaborative human-AI approach [7].

This article discusses the integration of Artificial Intelligence (AI) into clinical decision-making processes, highlighting its capacity to analyze vast datasets and identify subtle patterns beyond human ability. It posits that AI can significantly improve diagnostic precision, predict disease progression, and personalize treatment plans. However, successful integration hinges on addressing data privacy, algorithmic bias, and the necessity for clinicians to understand and trust AI outputs. The focus is on creating synergistic workflows where AI supports and enhances human expertise [8].

This scoping review examines the role of electronic health records (EHRs) in supporting clinical decision-making. It finds that EHRs offer immediate access to comprehensive patient information, streamline workflows, and enable data-driven insights. These features can significantly improve diagnostic accuracy, medication management, and overall patient safety. However, challenges such as system usability, data overload, and interoperability issues can impede their effectiveness, underscoring the need for well-designed and integrated EHR systems [9].

This article discusses the burgeoning field of data-driven clinical decision-making, exploring both its vast opportunities and significant challenges. It highlights how leveraging large datasets and advanced analytics can lead to more precise diagnoses, personalized treatments, and proactive disease management. However, the authors also address hurdles such as data quality, privacy concerns, algorithmic bias, and the need for new skill sets among clinicians. Effective implementation requires robust infrastructure and ethical considerations [10].

Conclusion

The landscape of clinical decision-making is rapidly evolving, driven significantly by advancements in Artificial Intelligence (AI) and digital health technologies. AI presents vast opportunities to enhance diagnostic accuracy, personalize treatments, and predict disease progression by analyzing complex datasets [1, 8]. The integration of Explainable AI (XAI) is essential to foster clinician trust and ensure transparency in AI's reasoning, addressing ethical concerns in healthcare [3]. Clinicians, while acknowledging AI's efficiency gains, voice concerns over data privacy, accountability, and maintaining the human element in patient care, advocating for AI systems that augment rather than replace their judgment [7]. Digital health technologies, including Electronic Health Records (EHRs), improve data access, streamline workflows, and enable data-driven insights, which collectively enhance diagnostic precision and patient safety [2, 9]. However, challenges like digital literacy, system interoperability, and data overload must be strategically addressed through user-centered design and policy support. Beyond technology, human elements remain paramount. The profound role of clinician experience, blending theoretical knowledge with intuition and practical insight, is invaluable for navigating complex patient scenarios [5]. Additionally, effective knowledge translation is crucial for applying evidence-based guidelines, particularly in low-resource settings where contextual factors can hinder implementation [4]. Finally, shared decision-making (SDM), involving collaborative choices between clinicians and patients, consistently leads to improved patient satisfaction, treatment adherence, and better health outcomes, underscoring the importance of communication and patient education [6]. Data-driven approaches overall promise precise diagnoses but require careful consideration of data quality, bias, and new clinical skill sets [10].