AI for empathy: Leveraging AI-driven analytics to enhance compassionate care

Editor’s note: This is the first installment of a two-article series, powered by DHI and sponsored by Neurealm, looking at how AI-based technology can be leveraged and guided to improve empathy, equity, and access in healthcare for improved outcomes and experience. 

Empathy and compassion form the soul of health “care” and are especially crucial when AI tools provide clinical support. The pandemic reinforced the significance of emotional connections in healthcare, as clinicians and staff went beyond medical care to offer support, reassurance, and a human touch to patients. However, maintaining empathy in high-pressure environments is challenging, particularly for clinicians and contact center staff managing overwhelming workloads and complex patient interactions.

AI-driven technologies, including sentiment analysis, voice analytics, and ambient listening, promise to support human caregivers by detecting emotions, behaviors, and stress levels through unstructured data. Yet, AI is not a substitute for the human traits of experience, empathy, and compassion. Instead, AI serves as a partner in care, enabling more personalized and emotionally intelligent healthcare.

AI as a partner, not a replacement

Humans remain essential in diagnosis, treatment decisions, and AI tool oversight. While AI-powered chatbots can facilitate patient communication, sensitive and emotionally charged interactions require human involvement.

AI excels at processing and analyzing vast amounts of data rapidly, making it valuable for handling routine administrative tasks—such as scheduling appointments, managing medication refills, or providing basic patient information—freeing clinicians to focus on complex care interactions.

However, AI systems are not infallible. Misinterpretations, data biases, and errors in recommendation algorithms can occur, making human oversight critical. With interconnected healthcare systems, a single AI error can impact thousands of patients, emphasizing the need for a human-in-the-loop approach to ensure safe, ethical, and effective AI applications.

Addressing bias, security, and liability in AI-driven care

AI’s potential to enhance compassionate care must be balanced against concerns related to bias, security, and liability.

• Bias: AI models are only as good as the data they are trained on. If training datasets lack diversity, AI may introduce biases that affect patient outcomes, such as misdiagnosing conditions in underrepresented populations or misunderstanding speech patterns. Ensuring diverse and representative datasets is crucial for equitable AI performance.
• Security: AI systems process highly sensitive data, including voice and sentiment analytics. Privacy concerns are paramount, particularly as AI tools analyze unstructured patient data. The novel snap-and-send solutions Neurealm developed for remote urine analysis (we’ll explore this later in more detail) highlights the importance of robust security measures—ensuring data protection, secure transmission, and compliance with healthcare regulations. This case study demonstrates how AI can be used to analyze urine test strip images remotely while safeguarding patient confidentiality through localized data storage, anonymized analytics, and strict access controls.
• Liability: When AI-generated insights contribute to errors, accountability remains a gray area. Should liability fall on the technology provider, the healthcare institution, or the individual clinician? Clear governance frameworks and human oversight are necessary to navigate these challenges and establish trust in AI-driven care.

AI-driven personalization and patient insights

AI’s ability to analyze vast datasets—including electronic health records, clinical trials, and social determinants of health (SDOH)—enables deeper personalization in patient care. Among these data sources, voice analytics stands out as a promising innovation.

Neurealm has developed AI-powered voice analysis tools for its customer that detect subtle vocal variations, offering insights into mental and physical health conditions. By capturing vocal biomarkers via mobile apps and applying machine learning models, healthcare providers can proactively monitor patient well-being, enabling early intervention and personalized treatment strategies. This approach exemplifies how AI can bridge the gap between advanced analytics and compassionate care.

Similarly, AI-powered patient profiles aggregate diverse data points—medical history, lifestyle factors, SDOH, and real-time health data—to create comprehensive patient insights. This personalization fosters improved communication, anticipates patient needs, and enhances overall care experiences.

Enhancing staff support and monitoring burnout

AI’s benefits extend beyond patient care to supporting healthcare workers. By analyzing patterns in voice tone, response times, and interaction frequencies, AI tools can help identify signs of clinician burnout.

For example, Neurealm’s MediAssist utilizes ambient listening and AI-powered documentation to automate clinical notetaking, reducing administrative burdens and allowing clinicians to focus on direct patient care. AI-driven solutions such as real-time clinical support, speech diarization, and advanced EHR integration contribute to smoother workflows and improved job satisfaction, ultimately benefiting both providers and patients.

Case study: remote urine analysis and privacy considerations

A snap-and-send approach to remote urine analysis, developed by Neurealm for a Medtech company, demonstrates AI’s potential to enhance patient convenience while maintaining data privacy. Designed for cancer patients undergoing chemotherapy, the solution allows users to upload test strip images via a mobile app, where AI-powered image processing analyzes diagnostic parameters instantly.

Beyond convenience, this AI-driven innovation highlights essential privacy and compliance considerations. Data protection strategies—such as localized data storage, anonymized analytics, and secure transmission—ensure regulatory adherence while enabling healthcare advancements. The case study underscores the necessity of designing AI solutions that prioritize both efficiency and patient confidentiality.

The future of AI in compassionate healthcare

AI-driven tools are reshaping healthcare by enhancing patient insights, supporting staff well-being, and streamlining operations. However, for AI to truly foster compassionate care, human oversight, ethical considerations, and robust governance frameworks must remain central.

Looking ahead, the integration of AI and human expertise holds immense potential for healthcare transformation. By leveraging AI as a supportive tool—rather than a replacement—healthcare organizations can create a future where technology enhances, rather than diminishes, empathy in patient care.

Healthcare leaders must embrace AI with a focus on ethical implementation, data privacy, and human-centered design. By prioritizing responsible AI adoption, organizations can harness technology’s potential while preserving the essential human elements of empathy and compassion.

About Neurealm

Neurealm (formerly GS Lab | GAVS) is the right-sized partner for Engineering, Modernization, and RunOps, blending human intelligence with the latest technologies to help businesses across industries such as Healthcare, Technology, and others, make smart progress.

With offerings in Digital Platform Engineering, Data, AI, Cybersecurity, and Technology Operations, and delivery centers in India and the US, we empower 250+ global enterprises. Driven by an engineering mindset and powered by Neurealm Labs—our innovation engine—we transform ideas into real-world impact through new-age offerings, solutions, frameworks, and accelerators. Our strong technology alliances and academic partnerships further power the future-ready ecosystems we build for our clients.