Adopting AI-driven healthcare systems and IoT devices has transformed clinical and administrative processes, leading to efficient data collection & analysis, improved patient care, and enhanced disease detection accuracy. However, in a critical industry like healthcare, where patient treatment outcomes are directly impacted by the decisions of the stakeholders, relying solely on AI is not a wise approach. Even advanced AI systems require human oversight for their ethical, secure, and efficient usage. In this blog, we will explore why and how human intervention enhances the performance of AI-powered systems in healthcare.
Applications of AI in Healthcare – Transforming Patient Care
Before exploring the need for human oversight in AI-driven healthcare systems, let’s understand how this advanced technology is being utilized in this sector. Here are some primary use cases of AI in healthcare.
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AI in Healthcare Data Management
Currently, 30% of the world’s total data is generated by the healthcare industry alone, and by 2025, this figure is projected to grow at a compound annual rate of 36% [Source]. Managing such a vast amount of structured (medical records) and unstructured (e.g., physician notes, lab reports) data is both time-consuming and resource-intensive for healthcare organizations.
Delays in processing medical invoices, clinical data, and other documents can hinder timely treatment, disrupt research, and negatively impact financial outcomes. By rapidly processing and organizing such massive healthcare data, AI addresses the challenge of data silos and enhances operational efficiency for improved patient care.
Diagnostic error is another major concern for healthcare providers, resulting in 40,000 to 80,000 deaths annually [Source]. Many patient surveys highlight that in the US, one out of three people have first-hand experience with diagnostic errors (missed, delayed, and wrong diagnoses).
The issue of delayed or missed diagnosis can be addressed with AI-powered diagnostic systems. These tools can analyze medical scans such as X-rays, MRIs, and CT scans, identifying anomalies with greater speed and precision than traditional methods. This not only speeds up diagnosis but also reduces the margin of error, which is crucial in life-saving scenarios.
Artificial intelligence is transforming how surgeries can be done. Robots equipped with AI can process vast amounts of real-time data during surgery and assist surgeons in performing complex procedures that require high levels of dexterity, accuracy, and control. In robotic-assisted surgery, the surgeon provides guidance, and the robot executes the movements with a higher degree of precision, often translating the surgeon’s larger hand movements into much smaller, controlled actions.
For example, in laparoscopic surgery, the AI-powered robot can enhance precision by eliminating hand tremors and making fine, controlled movements that go beyond the natural precision of human hands. This level of precision leads to smaller incisions, reduces blood loss, minimizes postoperative pain, and shortens patient recovery time.
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AI in Drug Research and Discovery
Developing a new drug takes around 10-15 years (involving various stages like drug discovery, preclinical trials, and clinical trials). AI speeds up this process by analyzing large datasets from research and clinical trials to quickly identify potential drug candidates and genomic & patient response patterns that would take humans years to find. Additionally, AI systems aid in repurposing existing drugs, allowing for faster approvals. This has proven critical in situations like the COVID-19 pandemic, where rapid drug discovery was essential.
Challenges/Concerns that Can Be Addressed through the Human-in-the-Loop Approach
Undoubtedly, AI systems are transforming healthcare operations by facilitating improved, faster, and more efficient patient care. However, there are still valid concerns about its ability to function independently in complex medical environments. The reality is that AI-driven healthcare systems, despite their strengths, require human oversight to overcome several critical challenges, such as:
1. Privacy and Data Security
AI/ML models need to access plenty of healthcare data to optimize their performance and work efficiently without direct instructions. This training data comprises sensitive patient information, which healthcare providers hesitate to share with AI systems as they are susceptible to data breaches. In the past, the personal data of millions of patients was stolen from centralized healthcare systems in popular cyberattacks like SingHealth Data Breach and Atrium Data Breach.
Human oversight is crucial for AI models to use patients’ sensitive data responsibly and securely. By implementing robust data governance frameworks and employing data stewards, healthcare professionals can maintain strict privacy standards. They oversee data usage, enforce security measures, and ensure compliance with regulations such as HIPAA and GDPR, mitigating the risk of compromising patient data.
2. Relevant Data Accessibility
The foundation of any AI model is its training data. Without access to relevant and reliable training data, large language models can be useless. This became evident during the COVID-19 pandemic when numerous predictive tools were developed to assist healthcare providers with diagnosis and triage. None of those tools were effective – not because of the lack of effort but because of a lack of relevant training data. The most useful data, which originated in China, wasn’t utilized widely. If machine learning algorithms had been trained on this data, healthcare providers might have made more accurate decisions related to patient care and treatment plans, potentially saving millions of lives.
Challenges involved in accessing relevant data for AI model training and ways to overcome them
One of the major challenges is the fragmented nature of data. Patient information is often dispersed across multiple institutions, stored in various formats, and managed by different systems, making it difficult to create comprehensive training datasets. To overcome these challenges, data must be collected, cleaned, and standardized by subject matter experts. While healthcare organizations can develop in-house teams for healthcare data management, outsourcing these tasks to a reliable third-party provider is a more cost-effective option. These providers have a vast team of subject matter experts to handle healthcare data mining and processing. They can not only gather the necessary data but also address any gaps or inconsistencies, ensuring the data is enriched and ready for AI training.
3. Bias, Fairness, and Accountability
AI systems are only as unbiased as the data on which they are trained. When AI models are trained on biased or incomplete datasets, they can perpetuate and even amplify existing inequalities in healthcare.
Real-world example:
In the US, a healthcare risk prediction algorithm was designed to assist hospitals and insurance providers in identifying patients who would benefit from enhanced medical care. However, the algorithm exhibited racial bias. It relied on healthcare spending as an indicator of medical need, which turned out to be a problematic metric, favoring white patients over black patients. Since black patients, due to systemic inequalities such as income disparity and access to healthcare, generally spend less on healthcare than their white counterparts, the algorithm misinterpreted this lower spending as an indication of less need for care.
How the human-in-the-loop approach can mitigate AI bias:
Incorporating human oversight at key stages of AI development and deployment can help identify and rectify such biases. In the above-stated real-world example, subject matter experts could have flagged the healthcare spending metric as problematic and worked to develop more equitable criteria. By regularly monitoring, reviewing, and adjusting AI model outcomes, human experts can identify biases, flag discrepancies, and ensure fair decisions. This approach enhances the AI model’s adaptability, ensuring it reflects a more nuanced understanding of the complexities in healthcare data.
4. Informed Decision-Making and Ethical Oversight
In healthcare, where stakes are high, AI models face limitations in making critical decisions. They can only handle the situations for which they have been trained. Human oversight is required to handle complex scenarios or where ethical judgment is necessary. For instance, AI may suggest a treatment plan based on efficiency, but it’s up to healthcare providers to balance clinical benefits with patient preferences, cultural values, and ethical considerations. Human oversight is crucial to navigating the ethical gray areas in healthcare where AI lacks the emotional intelligence and moral reasoning required for sensitive decisions. For instance, AI might suggest pain management protocols based on numerical data, such as reported pain levels and medication history. However, chronic pain can be influenced by psychological, emotional, and environmental factors. A healthcare provider can consider these subjective elements while devising patient treatment plans.
End Note
As AI continues to reshape healthcare, the need for human oversight becomes even more crucial. While AI accelerates processes and uncovers new possibilities, it lacks the ethical judgment, empathy, and contextual understanding that only human experts can provide. The future of healthcare will not be about choosing between AI and humans but finding the right balance where both complement each other. Human oversight will serve as the guiding force that ensures AI remains accountable, fair, and safe—enabling a future where technology enhances care without compromising humanity at its core.
