How Explainable AI in Healthcare Enhances Decision-Making

Disclaimer

Citrusbug Technologies publishes this document as a contribution to advancing explainable artificial intelligence (XAI) in healthcare. The findings, interpretations, and conclusions expressed herein are a result of collaborative insights facilitated by Citrusbug but do not necessarily represent the views of all stakeholders. © 2025 Citrusbug Technologies. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, including photocopying and recording, or by any information storage and retrieval system.

Contents

• Reading Guide
• Foreword
• Executive Summary
• 1 Why Transparency Matters in Healthcare
  • 1.1 The Trust Gap in AI Adoption
  • 1.2 Risks of Opaque AI Systems
  • 1.3 Benefits of Explainable AI
• 2 What Explainable AI Looks Like in Practice
  • 2.1 Key Methods and Tools
  • 2.2 Real-World Applications
• 3 Industry Pain Points
  • 3.1 The Black Box Problem
  • 3.2 Workflow and Integration Challenges
  • 3.3 Safety, Bias, and Equity Risks
• 4 How Citrusbug Builds Explainable AI for Healthcare
  • 4.1 Human-Centered Design Process
  • 4.2 Model Selection and Explanation Layers
  • 4.3 Presentation, Validation, and Governance
• 5 Case Study: Personalized Treatment Planning with Explainable AI
  • 5.1 The GenAI Solution
  • 5.2 Explaining Outputs to Doctors
  • 5.3 Impact
• Conclusion
• Contributors
• Endnotes

Reading Guide

Citrusbug Technologies’ AI in Healthcare Initiative seeks to catalyze responsible AI transformation by exploring the strategic implications, opportunities, and challenges of promoting explainable artificial intelligence (XAI) across healthcare operations and decision-making models. This white paper explores the transformative role of XAI in healthcare, providing insights through broad analyses and in-depth explorations of practical applications and challenges.

As AI continues to evolve at an unprecedented pace, this paper captures a unique perspective on XAI, including a detailed snapshot of the landscape as of October 2025. Recognizing that ongoing shifts and advancements are in motion, the aim is to continuously deepen and update the understanding of XAI’s implications and applications through collaboration with healthcare providers, clinicians, and technology stakeholders engaged in AI strategy and implementation.

This paper can be read stand-alone or alongside others in the series, with common themes emerging across healthcare domains.

Related Reports in the Series

• Leveraging Explainable AI for Clinical Decision Support: Scenarios, Case Studies, and a Framework for Action
  Insight Report, November 2024, in collaboration with Healthcare Partners
• XAI and Ethical AI: Balancing Transparency and Innovation
  White Paper, October 2025, in collaboration with Ethics Boards
• Blueprint to Action: Implementing XAI in Global Healthcare Systems
  White Paper, October 2025, in collaboration with Global Health Partners
• Explainable AI in Diagnostic Imaging
  White Paper, October 2025, in collaboration with Radiology Experts
• Upcoming Reports:
  Personalized Medicine, Telehealth

Impact Areas

• Cross-Industry:
  Healthcare ecosystems
• Industry-Specific:
  Hospitals, diagnostic imaging, chronic disease management, intensive care units
• Regional Focus:
  Global healthcare systems, emerging markets

Foreword

The global healthcare landscape is undergoing a profound transformation, driven by the rapid integration of artificial intelligence (AI). In hospitals, clinics, and diagnostic centers, AI promises to enhance decision-making, streamline operations, and improve patient outcomes. However, the opaque nature of many AI systems—often referred to as “black box” models—has created a significant barrier to trust and adoption among clinicians, patients, and administrators. Explainable AI (XAI) addresses this challenge by providing transparent, human-understandable insights into AI decisions, fostering trust and enabling safer, more effective healthcare delivery.

Healthcare teams demand AI that is smart, safe, and trustworthy. The primary challenge is not merely accuracy but trust. Clinicians hesitate to use black box AI because they cannot understand the reasoning behind its decisions. XAI solves this by clearly explaining the “why” behind results in simple, human terms. This white paper outlines the problem, the solution, and Citrusbug’s practical approach to building XAI that doctors can understand, use, and trust.

Citrusbug Technologies is committed to advancing XAI to bridge the trust gap in healthcare. This white paper, part of a broader series on AI in healthcare, explores how XAI can transform clinical decision-making by prioritizing transparency, safety, and usability. Through collaboration with healthcare providers, technology experts, and policy-makers, we aim to drive the responsible adoption of XAI, ensuring that AI serves as a trusted partner in delivering equitable, high-quality care globally.

The journey to transparent AI in healthcare requires innovation, collaboration, and a steadfast commitment to patient-centered outcomes. This paper outlines practical strategies, real-world applications, and a vision for how XAI can reshape healthcare delivery for the better, addressing challenges such as rising demand, resource constraints, and health inequities.

Ishan vyas
CEO & Head of AI Innovation, Citrusbug Technologies

Executive Summary

Artificial intelligence (AI) holds immense potential to revolutionize healthcare by enhancing clinical decision-making, improving operational efficiency, and personalizing patient care. However, the lack of transparency in traditional “black box” AI models hinders widespread adoption, as clinicians struggle to trust outputs they cannot understand or verify. Explainable AI (XAI) addresses this by providing clear, human-readable explanations of AI decisions, fostering trust, reducing risks, and enabling seamless integration into clinical workflows.

The lack of transparency prevents AI from being widely adopted in hospitals. Clinicians resist black box tools because they cannot verify or challenge them. XAI offers clear, visual, and textual explanations that build trust and reduce risk. Tools like SHAP, LIME, and Grad-CAM transform hidden model logic into simple, actionable insights for clinicians, radiologists, and administrators.

This white paper identifies three key challenges to adopting XAI in healthcare:

1. The Trust Gap: Clinicians hesitate to rely on opaque AI outputs, leading to underutilization and missed opportunities.
2. Workflow Misalignment: Explanations that are too technical or poorly integrated into existing systems fail to meet clinical needs.
3. Safety and Equity Risks: Opaque models can perpetuate biases or errors, disproportionately affecting underserved populations.

To overcome these challenges, Citrusbug proposes six critical strategies:

1. Prioritize Explainability from Design: Build AI with transparency as a core principle, focusing on operational applications to demonstrate near-term benefits.
2. Tailor Explanations to Roles: Deliver role-specific insights for clinicians, nurses, and patients, aligning with public-private ecosystems for shared objectives.
3. Integrate with Workflows: Ensure explanations fit seamlessly into electronic medical records (EMR) and picture archiving and communication systems (PACS), prioritizing shared digital public infrastructures (DPIs).
4. Validate for Safety and Fairness: Use robust testing to detect biases and ensure equitable outcomes, with leaders making responsible technical decisions.
5. Foster Continuous Learning: Incorporate clinician feedback to refine models and explanations, proactively building trust through post-market surveillance and ethical principles.
6. Build Trust Through Transparent Data: Advocate for locally controlled, globally connected datasets to ensure privacy, safety, and innovation.

By implementing these strategies, XAI can drive transformative improvements in healthcare, including faster decision-making, reduced errors, and enhanced trust. A case study illustrates how Citrusbug’s XAI approach improved treatment planning for heart failure patients in a midsize hospital, reducing errors by 30% and increasing adoption.

Realizing XAI’s potential requires collaboration among healthcare providers, technology developers, and regulators. By prioritizing transparency and human-centered design, XAI can empower clinicians and patients, paving the way for smarter, safer, and more equitable healthcare.

1. Why Transparency Matters in Healthcare

Doctors make high-stakes decisions daily. When AI suggests “disease present,” clinicians need to know why to trust and act on the recommendation. Without clear explanations:

• Clinicians distrust outputs, leading to underutilization and wasted investments.
• Errors are harder to detect, increasing patient risks.
• AI remains stuck in pilot phases, never reaching widespread hospital use.

Explainable AI (XAI) addresses these issues by showing which factors drove the decision. In imaging, heatmaps highlight which parts of an X-ray influenced the result. In lab data, feature contributions reveal which values increased or decreased risk. This transparency builds confidence, enabling better collaboration between humans and AI.

1.1. The Trust Gap in AI Adoption

The lack of transparency creates a significant trust gap. A 2024 BCG survey found that 60% of healthcare professionals distrust AI due to unclear reasoning, leading to low adoption rates. Clinicians, responsible for high-stakes decisions, hesitate to rely on “black box” outputs they cannot verify, resulting in missed opportunities to improve care.

1.2. Risks of Opaque AI Systems

Opaque AI models pose significant risks:

1.3. Benefits of Explainable AI

XAI mitigates these risks by providing transparent, human-readable explanations. Key benefits include:

2. What Explainable AI Looks Like in Practice

XAI transforms complex AI outputs into actionable, understandable insights for healthcare professionals and patients. By leveraging proven methods and tools, XAI ensures transparency across various applications, from diagnostics to chronic disease management.

2.1. Key Methods and Tools

Several established methods make AI explainable:

2.2. Real-World Applications

XAI is already transforming healthcare tasks:

3. Industry Pain Points

Despite its potential, XAI faces significant challenges that hinder its adoption in healthcare.

3.1. The Black Box Problem

Opaque AI models prevent clinicians from understanding decision rationales, leading to distrust and low adoption. A 2023 study found that 70% of clinicians ignored AI recommendations due to lack of explainability, stalling pilot projects and limiting real-world impact.

3.2. Workflow and Integration Challenges

Even effective XAI models fail if explanations are too technical or poorly integrated into existing systems like EMR or PACS. Clinicians need concise, role-specific insights delivered within their workflows, not standalone reports that disrupt their processes.

3.3. Safety, Bias, and Equity Risks

Opaque models can amplify biases in training data, leading to unfair outcomes across patient groups. Without explanations, these biases are hard to detect, increasing risks for underserved populations. Additionally, one-size-fits-all explanations fail to meet the diverse needs of radiologists (needing visual focus maps), emergency doctors (needing fast top factors), and patients (needing plain language).

4. How Citrusbug Builds Explainable AI for Healthcare

Citrusbug Technologies follows a human-centered, “explainability-first” approach to design, develop, and deploy XAI solutions that meet the needs of healthcare stakeholders.

4.1. Human-Centered Design Process

• Step 1: Discovery with Clinicians
  • Map clinical decisions: Identify what information doctors need, when, and in what format.
  • Define “who needs what”: Radiologists need heatmaps, intensivists need top risk factors, nurses need simple alerts with reasons, and patients need plain-language summaries.
• Step 2: Choose the Right Model for the Job
  • Start simple where possible: Use interpretable models (e.g., logistic regression, decision trees) for low-risk tasks.
  • Use powerful deep learning for complex tasks (e.g., imaging, ECG analysis), but add robust explainability layers like Grad-CAM and SHAP.

4.2. Model Selection and Explanation Layers

• Step 3: Build Explanation Layers
  • Imaging: Grad-CAM heatmaps overlayed on scans, highlighting only clinically relevant regions, with adjustable thresholds and comparisons to prior scans.
  • Tabular/Time-Series: SHAP provides global and local explanations, clearly showing top contributors and their direction (e.g., increases vs. reduces risk).
  • NLP and Notes: Attention maps or token highlights show which words influenced summaries or classifications.

4.3. Presentation, Validation, and Governance

• Step 4: Human-Friendly Presentation
  • Role-based views: Clinician view shows detailed factors; patient view uses simple language; admin view shows model stability and fairness metrics.
  • One-screen summaries: Display “Prediction,” “Why,” and “What to check next” in a concise, readable panel within EMR or workflow tools.
  • Usability tests: Conduct tests with real clinicians to measure adoption, trust, and time-to-decision improvements.
• Step 5: Safety, Fairness, and Validation
  • Bias checks: Compare explanations across age, sex, and ethnicity to detect unfair patterns.
• Step 6: Continuous Learning and Governance
  • Feedback buttons: Include “Agree,” “Disagree,” and “Missing Info” options to refine models.
  • Monitoring: Track explanation consistency and model drift over time.
  • Documentation: Provide clear model cards and layman-language explanation guides for clinical teams.

5. Case Study: Personalized Treatment Planning with Explainable AI

A midsize hospital aimed to improve care for heart failure patients using generative AI (GenAI) to create daily treatment plans. Clinicians were concerned about trusting AI outputs for such a critical condition. Citrusbug developed an XAI solution to address these concerns, focusing on transparency and usability.

The GenAI Solution

• Training Data: GenAI was trained on thousands of patient records, recent clinical guidelines, and hospital best practices.
• Daily Plans: Each morning, GenAI generated patient-specific treatment plans covering medications, tests, and nursing instructions.
• Explanation: Clear rationales were provided for each recommendation, e.g., “Increased diuretic due to 2kg weight gain and lung fluid visible on X-ray” or “Ordered kidney function test due to rising creatinine trend and new cough.”

Explaining Outputs to Doctors

• Interface: A panel integrated into the EMR displayed each GenAI recommendation with a “Why” link. Clicking “Why” revealed a bulleted list:
  • “Recent weight gain (+2kg in 2 days)”
  • “New lung crackles found”
  • “Rising creatinine trend”
  • “Clinical guidelines recommend dose increase in similar cases”
• Confidence Meter: A confidence score (e.g., “85%”) accompanied each suggestion. Low-confidence outputs flagged missing or unclear data, prompting manual review.

Impact

• Clinician Efficiency: Doctors reported faster, easier decision-making, as they could quickly verify the evidence behind each recommendation.
• Nursing Clarity: Nurses received patient-specific instructions, improving care delivery.
• Error Reduction: Treatment errors due to missed data dropped by 30% over six months.
• Adoption: Clinician trust increased, with 80% regularly using the system by month six.

Key Takeaways

6. Conclusion

Explainable AI represents a crucial step forward in making artificial intelligence truly useful and trustworthy in healthcare. While AI can achieve impressive accuracy, its value is limited if doctors, nurses, and patients cannot understand the reasoning behind its decisions. The lack of transparency leads to distrust, reduced adoption, and missed opportunities to improve patient care.

Citrusbug’s human-centered, explainability-first approach—prioritizing transparent design, role-specific insights, and seamless workflow integration—addresses these challenges, as demonstrated in the heart failure case study. By implementing the six strategies outlined (prioritizing explainability, tailoring explanations, integrating with workflows, validating for fairness, fostering continuous learning, and building trust through transparent data), XAI can drive transformative improvements in healthcare.

Realizing XAI’s potential requires collaboration among healthcare providers, technology developers, and regulators. By focusing on transparency and patient-centered outcomes, XAI can empower clinicians and patients, paving the way for smarter, safer, and more equitable healthcare globally.