Avoid These 8 Mistakes When Using AI in Healthcare

All pharmaceutical companies developing new drugs must meet intense regulatory guidelines and submit thousands of pages of clinical study reports and safety summaries to get closer to getting their drug on the market. Imagine, to expedite this drawn-out process, a pharmaceutical company decides to leverage a Large Language Model to help create this mass of content. The team conducts pilots, testing the LLM on smaller, less complex datasets. These pilots incorporate human reviewers using a human-in-the-loop approach, focusing primarily on speed, basic formatting, and ensuring initial accuracy. Early results are promising—speed improves dramatically, and the drafts appear well-organized. However, the pilots miss a critical flaw: despite the involvement of human reviewers, the LLM still struggles with context-sensitive clinical interpretations during more complex tasks. During pilot reviews, the errors seem minor and are easily fixed by the team, leading to a false sense of security. But when the model is scaled up to assist in the full submission, these issues compound. During a crucial review meeting, it becomes evident that several sections contain outdated references, and key clinical data is misinterpreted in ways that were not obvious during smaller-scale tests. The oversight happens because the pilots didn’t thoroughly stress-test the LLM’s ability to handle the full scope and complexity of the regulatory documents. The result? Weeks of rework, delays in submission, and potential regulatory penalties, all stemming from insufficient validation and an overestimation of the LLM’s capabilities. This scenario isn’t just hypothetical. The healthcare industry, including pharma, medical devices and patient care, have all increased their reliance on AI, especially LLMs. But while new tech brings immense promise—as demonstrated in this example —it’s also increasing risk. When AI is not properly integrated and validated, it can jeopardize not only timelines but also compliance, patient safety, and trust in the technology. Although it’s true that within the healthcare industry organizations must adhere to strict validation processes, of multiple levels of data review and established regulatory guidelines, the integration of LLMs present new and unique challenges that need to be understood and addressed. Let’s explore the common pitfalls healthcare organizations encounter when trying to implement LLMs into workflows and how to address them to build a safer, more effective future for AI in healthcare. Over-Reliance on AI Without Domain Expertise Integration Pitfall: When pharmaceutical companies or healthcare enterprises deploy LLMs to streamline decision-making processes, the temptation to let the AI take the reins can be strong. LLMs can process large amounts of data much quicker than humans. But when it comes to data processing, enterprise-level decision-making involves multiple stakeholders, contextual nuances, and regulatory considerations that require more than just speed. Even with rigorous validation frameworks in place, over-relying on AI without human domain expertise can be a critical mistake, especially in an industry as high-stakes as healthcare. For instance, IMB’s Watson, a visionary tool meant to revolutionize oncology, turned out to be biased and unreliable. Its recommendations often failed to align with expert oncologists’ decisions as the system failed to understand the nuances of individual patient cases and the complexities of human emotions involved in treatment choices. Lessons Learnt: The key lesson here is that AI systems should augment, not replace, human expertise, particularly in medicine. Healthcare organizations should adopt a hybrid model for AI integration. This model leverages AI for its strengths—speed, pattern recognition, and processing of large data sets—while keeping humans in the loop for tasks requiring contextual understanding, regulatory knowledge, and critical thinking. Unresolved Data Quality Issues Across Fragmented Systems Pitfall: One of the biggest operational challenges in healthcare and pharma is the fragmented nature of data. From patient records in clinical trials to operation datasets for regulatory submissions, or commercial systems data, information is often stored in various systems with many using different formats and standards. To list a few: the Clinical Data Interchange Standards Consortium for clinical data organization, Health Level 7 for interoperability in health data exchange, and The Fast Healthcare Interoperability Resources for standardized data sharing across healthcare applications. LLMs can produce inaccurate or incomplete outputs without proper data harmonization and adherence to these standards, which negatively impacts decision-making. Lessons Learnt: A rigorous data management strategy must be put in place to ensure reliable AI outputs. This includes data standardization, transformation, and conformance to regulatory data formats. Enterprise-level data governance protocols and data interoperability solutions can help ensure that the LLM is working with uniform data, further reducing the risk of misinterpretation. Ethical and Compliance Risks in Handling Sensitive Healthcare Data Pitfall: Medical history and treatment records are considered protected health information (PHI). Organizations risk significant legal fines and reputation damage if such data is mishandled through unauthorized access, inadequate consent, or weak security, so inputting PHI into LLMs raises concerns about ethical and compliance risks. Lesson Learnt: Within a healthcare setting, LLMs must have robust data governance frameworks that prioritize patient consent, data security, and compliance. This involves implementing data encryption, data anonymization and de-identification techniques, and requiring a data ethics committee to review all AI-related projects. Poor Integration with Existing Organizational Workflows Pitfall: When little planning has been put into an AI model’s integration into existing workflows, they can disrupt operations, slow down productivity, and fail to deliver the intended benefits. For instance, for an AI system to successfully analyze patient data and optimize trial protocols, it may require extensive staff retraining, which needs to be arranged and delivered prior to the AI integration. Lessons Learnt: Organizations must prioritize workflow compatibility and ensure alignment with enterprise decision-making structures when deploying LLMs. Organizations must identify key areas where the LLM will interact with other systems, processes, and personnel. It’s best to adopt a modular integration and create user-friendly interfaces that allow staff to interact with the AI without needing to learn entirely new systems to significantly reduce friction and improve user adoption. Inadequate Model Validation and Monitoring Post-Deployment Pitfall: LLMs, like any machine learning model, can experience “model drift” over time. This is where outputs become less accurate as the underlying data or medical knowledge ages. For example, a predictive model developed during the COVID-19 pandemic may struggle to assimilate well to post-crisis scenarios, continuing to provide outputs based on the data it was trained on, leading to inappropriate care recommendations. Lessons Learnt: Although healthcare organizations already have continuous data review and adherence to regulatory standards, the deployment of LLMs requires an additional layer of model monitoring to ensure alignment with evolving data and medical knowledge. Healthcare organizations must establish robust post-deployment monitoring and model validation processes. This involves setting up a continuous feedback loop where the LLM’s outputs are regularly reviewed against new data and evolving medical knowledge. Implementing real-time validation, regular model retraining, and continuous learning systems are critical operational steps. Neglecting Regulatory and Compliance Requirements Pitfall: AI systems have a maze of healthcare regulations they must adhere to. From the Good Machine Learning Practice (GMLP) by the FDA, to the ISO/IEC 27001 standards for information security, and General Data Protection Regulation (GDPR) for data privacy within the European Union. Neglecting these standards can lead to costly delays, non-approval of submissions, or even legal penalties. Lessons Learnt: Regulatory compliance must be embedded into every phase of AI design and development. This involves building compliance checkers into the LLM workflow itself, maintaining detailed audit trails, and prioritizing explainable AI technology that allow human reviewers to understand how the LLM arrived at specific conclusions. Overpromising AI Capabilities to Stakeholders Pitfall: Overestimating AI’s potential, without clear guardrails for expectations, can lead to disillusionment, misalignment with business goals, and wasted resources. Lessons Learnt: Realistic expectations of what LLMs can and cannot do must be shared and understood by all stakeholders. This involves taking into account the complexities of enterprise decision-making, regulatory adherence, and operational integration. Adopting a phased implementation strategy allows the AI to prove its test capabilities incrementally and setting clear KPIs and metrics that define success in measurable terms are crucial. Ignoring the Need for Customization in Model Development Pitfall: Off-the-shelf AI models will never be able to meet the specific needs of healthcare organizations. Their implementation can not only lead to substandard results and decreased accuracy, but can even risk patient safety. For example, a study from MIT found that an AI model trained predominantly on images from one demographic group performed poorly when analyzing images from a more diverse population, leading to missed diagnoses and potential harm to patients Lessons Learnt: Customization is the only way to successfully deploy LLMs in healthcare settings. This involves fine-tuning the LLM with specialized datasets and adjusting the model’s workflows to fit into established organizational processes. Collaboration between AI experts and healthcare professionals can help create widespread and enforceable output validation processes. Conclusion It’s clear that using LLMs in healthcare can transform operations and allow organizations to enjoy new levels of efficiency and insight. But this success will depend on the amount of thoughtful planning and smart execution each organization puts in. Over-reliance on AI to poor integration, lack of personnel training and inadequate customization are common pitfalls that the healthcare industry should avoid. By learning from these the healthcare industry can ensure that their AI deployments deliver real value. Santosh Shevade, Principal Data Consultant, Gramener All pharmaceutical companies developing new drugs must meet intense regulatory guidelines and submit thousands of pages of clinical study reports and safety summaries to get closer to getting their drug on the market. Imagine, to expedite this drawn-out process, a pharmaceutical company decides to leverage a Large Language Model to help create this mass of content. The team conducts pilots, testing the LLM on smaller, less complex datasets. These pilots incorporate human reviewers using a human-in-the-loop approach, focusing primarily on speed, basic formatting, and ensuring initial accuracy. Early results are promising—speed improves dramatically, and the drafts appear well-organized. However, the pilots miss a critical flaw: despite the involvement of human reviewers, the LLM still struggles with context-sensitive clinical interpretations during more complex tasks. During pilot reviews, the errors seem minor and are easily fixed by the team, leading to a false sense of security. But when the model is scaled up to assist in the full submission, these issues compound. During a crucial review meeting, it becomes evident that several sections contain outdated references, and key clinical data is misinterpreted in ways that were not obvious during smaller-scale tests. The oversight happens because the pilots didn’t thoroughly stress-test the LLM’s ability to handle the full scope and complexity of the regulatory documents. The result? Weeks of rework, delays in submission, and potential regulatory penalties, all stemming from insufficient validation and an overestimation of the LLM’s capabilities. This scenario isn’t just hypothetical. The healthcare industry, including pharma, medical devices and patient care, have all increased their reliance on AI, especially LLMs. But while new tech brings immense promise—as demonstrated in this example —it’s also increasing risk. When AI is not properly integrated and validated, it can jeopardize not only timelines but also compliance, patient safety, and trust in the technology. Although it’s true that within the healthcare industry organizations must adhere to strict validation processes, of multiple levels of data review and established regulatory guidelines, the integration of LLMs present new and unique challenges that need to be understood and addressed. Let’s explore the common pitfalls healthcare organizations encounter when trying to implement LLMs into workflows and how to address them to build a safer, more effective future for AI in healthcare. Over-Reliance on AI Without Domain Expertise Integration Over-Reliance on AI Without Domain Expertise Integration Pitfall: When pharmaceutical companies or healthcare enterprises deploy LLMs to streamline decision-making processes, the temptation to let the AI take the reins can be strong. LLMs can process large amounts of data much quicker than humans. But when it comes to data processing, enterprise-level decision-making involves multiple stakeholders, contextual nuances, and regulatory considerations that require more than just speed. Even with rigorous validation frameworks in place, over-relying on AI without human domain expertise can be a critical mistake, especially in an industry as high-stakes as healthcare. For instance, IMB’s Watson, a visionary tool meant to revolutionize oncology, turned out to be biased and unreliable. Its recommendations often failed to align with expert oncologists’ decisions as the system failed to understand the nuances of individual patient cases and the complexities of human emotions involved in treatment choices. Lessons Learnt: The key lesson here is that AI systems should augment, not replace, human expertise, particularly in medicine. Healthcare organizations should adopt a hybrid model for AI integration. This model leverages AI for its strengths—speed, pattern recognition, and processing of large data sets—while keeping humans in the loop for tasks requiring contextual understanding, regulatory knowledge, and critical thinking. Unresolved Data Quality Issues Across Fragmented Systems Unresolved Data Quality Issues Across Fragmented Systems Pitfall: One of the biggest operational challenges in healthcare and pharma is the fragmented nature of data. From patient records in clinical trials to operation datasets for regulatory submissions, or commercial systems data, information is often stored in various systems with many using different formats and standards. To list a few: the Clinical Data Interchange Standards Consortium for clinical data organization, Health Level 7 for interoperability in health data exchange, and The Fast Healthcare Interoperability Resources for standardized data sharing across healthcare applications. LLMs can produce inaccurate or incomplete outputs without proper data harmonization and adherence to these standards, which negatively impacts decision-making. Lessons Learnt: A rigorous data management strategy must be put in place to ensure reliable AI outputs. This includes data standardization, transformation, and conformance to regulatory data formats. Enterprise-level data governance protocols and data interoperability solutions can help ensure that the LLM is working with uniform data, further reducing the risk of misinterpretation. Ethical and Compliance Risks in Handling Sensitive Healthcare Data Ethical and Compliance Risks in Handling Sensitive Healthcare Data Pitfall: Medical history and treatment records are considered protected health information (PHI). Organizations risk significant legal fines and reputation damage if such data is mishandled through unauthorized access, inadequate consent, or weak security, so inputting PHI into LLMs raises concerns about ethical and compliance risks. Pitfall: Lesson Learnt: Within a healthcare setting, LLMs must have robust data governance frameworks that prioritize patient consent, data security, and compliance. This involves implementing data encryption, data anonymization and de-identification techniques, and requiring a data ethics committee to review all AI-related projects. Lesson Learnt: Poor Integration with Existing Organizational Workflows Poor Integration with Existing Organizational Workflows Pitfall: When little planning has been put into an AI model’s integration into existing workflows, they can disrupt operations, slow down productivity, and fail to deliver the intended benefits. For instance, for an AI system to successfully analyze patient data and optimize trial protocols, it may require extensive staff retraining, which needs to be arranged and delivered prior to the AI integration. Pitfall: Lessons Learnt: Organizations must prioritize workflow compatibility and ensure alignment with enterprise decision-making structures when deploying LLMs. Organizations must identify key areas where the LLM will interact with other systems, processes, and personnel. It’s best to adopt a modular integration and create user-friendly interfaces that allow staff to interact with the AI without needing to learn entirely new systems to significantly reduce friction and improve user adoption. Lessons Learnt: Inadequate Model Validation and Monitoring Post-Deployment Inadequate Model Validation and Monitoring Post-Deployment Pitfall: LLMs, like any machine learning model, can experience “model drift” over time. This is where outputs become less accurate as the underlying data or medical knowledge ages. For example, a predictive model developed during the COVID-19 pandemic may struggle to assimilate well to post-crisis scenarios, continuing to provide outputs based on the data it was trained on, leading to inappropriate care recommendations. Pitfall: Lessons Learnt: Although healthcare organizations already have continuous data review and adherence to regulatory standards, the deployment of LLMs requires an additional layer of model monitoring to ensure alignment with evolving data and medical knowledge. Healthcare organizations must establish robust post-deployment monitoring and model validation processes. This involves setting up a continuous feedback loop where the LLM’s outputs are regularly reviewed against new data and evolving medical knowledge. Implementing real-time validation, regular model retraining, and continuous learning systems are critical operational steps. Lessons Learnt: Neglecting Regulatory and Compliance Requirements Neglecting Regulatory and Compliance Requirements Pitfall: AI systems have a maze of healthcare regulations they must adhere to. From the Good Machine Learning Practice (GMLP) by the FDA, to the ISO/IEC 27001 standards for information security, and General Data Protection Regulation (GDPR) for data privacy within the European Union. Neglecting these standards can lead to costly delays, non-approval of submissions, or even legal penalties. Pitfall: Lessons Learnt: Regulatory compliance must be embedded into every phase of AI design and development. This involves building compliance checkers into the LLM workflow itself, maintaining detailed audit trails, and prioritizing explainable AI technology that allow human reviewers to understand how the LLM arrived at specific conclusions. Lessons Learnt: Overpromising AI Capabilities to Stakeholders Overpromising AI Capabilities to Stakeholders Pitfall: Overestimating AI’s potential, without clear guardrails for expectations, can lead to disillusionment, misalignment with business goals, and wasted resources. Pitfall: Lessons Learnt: Realistic expectations of what LLMs can and cannot do must be shared and understood by all stakeholders. This involves taking into account the complexities of enterprise decision-making, regulatory adherence, and operational integration. Adopting a phased implementation strategy allows the AI to prove its test capabilities incrementally and setting clear KPIs and metrics that define success in measurable terms are crucial. Lessons Learnt: Ignoring the Need for Customization in Model Development Ignoring the Need for Customization in Model Development Pitfall: Off-the-shelf AI models will never be able to meet the specific needs of healthcare organizations. Their implementation can not only lead to substandard results and decreased accuracy, but can even risk patient safety. For example, a study from MIT found that an AI model trained predominantly on images from one demographic group performed poorly when analyzing images from a more diverse population, leading to missed diagnoses and potential harm to patients Pitfall: Lessons Learnt: Customization is the only way to successfully deploy LLMs in healthcare settings. This involves fine-tuning the LLM with specialized datasets and adjusting the model’s workflows to fit into established organizational processes. Collaboration between AI experts and healthcare professionals can help create widespread and enforceable output validation processes. Lessons Learnt: Conclusion Conclusion It’s clear that using LLMs in healthcare can transform operations and allow organizations to enjoy new levels of efficiency and insight. But this success will depend on the amount of thoughtful planning and smart execution each organization puts in. Over-reliance on AI to poor integration, lack of personnel training and inadequate customization are common pitfalls that the healthcare industry should avoid. By learning from these the healthcare industry can ensure that their AI deployments deliver real value. Santosh Shevade, Principal Data Consultant, Gramener Santosh Shevade, Principal Data Consultant, Gramener Santosh Shevade , Principal Data Consultant, Gramener Santosh Shevade Gramener