Addressing Artificial Intelligence Challenges in Healthcare

AI in healthcare is not simply another technology upgrade. It is a matter of trust, safety, and ultimately, human life. In many sectors, an AI error might lead to inconvenience or financial loss. In healthcare, an AI error can mean a missed diagnosis, an inappropriate treatment pathway, or avoidable harm. That is why AI adoption in healthcare must be held to a higher standard than in almost any other industry. It requires deeper validation, stricter governance, and human guardrails at every stage. A framework I find particularly helpful is 𝐀𝐈 + 𝐑𝐀𝐂𝐓⁣, strengthened through a Human-Centred AI lens. 𝐑 = 𝐑𝐞𝐚𝐝𝐢𝐧𝐞𝐬𝐬⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣The risk begins long before deployment. If clinical data is incomplete, biased, or unrepresentative, AI systems can fail quietly, often affecting the most vulnerable populations first. Readiness must include: →Data integrity and provenance →Regulatory compliance →Clear clinical problem definition →Ethical and patient safety accountability 𝐀 = 𝐀𝐝𝐨𝐩𝐭𝐢𝐨𝐧⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣ ⁣⁣⁣⁣⁣⁣⁣⁣In healthcare, adoption is not about installing a tool, it is about integrating it into clinical judgment. The risk is over-reliance, alert fatigue, or the introduction of friction into already pressured workflows. Human-centred adoption means: →Clinicians remain firmly in the loop →AI outputs are explainable and challengeable →Training supports human-AI collaboration, not replacement 𝐂 = 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐲⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣Healthcare AI is not static. Models drift, populations change, and clinical practice evolves. The risk is that a system that appears safe today may not remain safe tomorrow. Capability requires: →Continuous monitoring and evaluation →Governance structures spanning clinicians, data, ethics and risk →Ongoing validation, not one-off approval 𝐓 = 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣True transformation is not automation for its own sake. The risk of scaling without safeguards is amplified inequity, diminished patient trust, and decision-making that feels outsourced. Transformation must prioritise: →Better patient outcomes and experience →Equity across communities →Shared decision-making, supported, not replaced, by AI The central truth is this: 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐀𝐈 𝐢𝐬 𝐧𝐨𝐭 𝐜𝐨𝐧𝐬𝐮𝐦𝐞𝐫 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲. 𝐈𝐭 𝐢𝐬 𝐬𝐚𝐟𝐞𝐭𝐲-𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥.⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣Progress must be ambitious, but responsibility must be uncompromising. The question is not whether AI will shape the future of care. It is whether we shape it with the rigour, humility, and human focus that patients deserve. What is the single most important gate check you insist on before scaling AI in clinical environments? ♻️ Share if this resonates ➕ Follow (Jyothish Nair) for reflections on AI, change, and human-centred AI #ResponsibleAI #AI #DigitalTransformation #HumanCentredAI

OpenAI's GPT-5 launch positioned healthcare as "one of the preeminent uses of ChatGPT," emphasizing its complex medical reasoning capabilities. But clinical diagnosis transcends pattern recognition—it requires knowing what questions to ask and reasoning through the answers. AI models excel at responding to prompts but struggle with the meta-cognitive skills clinicians use daily: spotting red flags, recognizing missing context, and distinguishing urgent intervention from watchful waiting. The challenge isn't technical accuracy—it's clinical appropriateness, diagnostic reasoning, and understanding when to act. What concerns me about OpenAI's positioning is emphasizing broad accessibility over clinical governance. "Expert-level intelligence in everyone's hands" could be profoundly democratizing, but we must ensure these tools reflect true healthcare expertise—judgment, ethics, and accountability that algorithms cannot provide. Healthcare leaders must act now: Audit AI governance frameworks: Every patient-facing AI tool needs explicit clinical oversight, not just technical validation. This includes GPT-5 deployments in patient portals or telehealth platforms. Define institutional policies: Establish clear guidelines for staff and patient use of generative AI, covering decision support and education, with escalation protocols when AI outputs conflict with clinician judgment. Understand patient behavior: Patients will increasingly use AI to understand health conditions and prepare for visits. We should embrace this trend while studying its impact—does it improve clinical conversations and access, or delay care-seeking and introduce inappropriate care? AI's healthcare potential is enormous, but patient safety remains our north star. If tech companies won't embed transparent clinical governance, healthcare leaders must fill that gap. We cannot let the race to deploy generative AI erode the clinical rigor that protects patients. What clinical governance frameworks are you implementing for generative AI in your health system? https://lnkd.in/eBRdvqCH #HealthcareAI #AIGovernance #PatientSafety #ClinicalDecisionMaking #GPT5 #DigitalHealth #HealthTech #MedicalAI #HealthcareLeadership #AIEthics #HealthSystemStrategy #ClinicalOversight

💡 AI in healthcare has a data problem—EHRs weren’t built for it. Electronic Health Records (EHRs) are often seen as a goldmine for AI, particularly in clinical decision support. ❗ But there’s a fundamental issue: EHRs were never designed for AI. As Louis Agha-Mir-Salim and colleagues recently pointed out [1], these systems impose a rigid structure optimized for billing and administration, often at the expense of medical usefulness, research, and learning. The data they collect is incidental, shaped primarily by workflow constraints—not by medical needs and certainly not by the requirements for building accurate AI models. The “Invisible Success” Problem A recent piece by Hugh Logan Ellis and colleagues [2] highlights this perfectly: Take non-AI Early Warning Scores (EWS) in the ICU. These scores are designed to predict patient deterioration, and when they work—when doctors intervene early—the event effectively disappears from the data. For AI models trained on EHRs, these cases where early action made a difference are invisible, distorting the dataset and reinforcing bias. This isn’t just an isolated issue—it applies to every EHR dataset where an effective intervention already exists. And that’s just the beginning. Other Critical Problems with EHR Data for AI ⚠️ Messy and incomplete data – Human entry errors, missing values, and inconsistencies degrade model performance. ⚠️ Interoperability issues – Different hospitals and vendors use non-standardized formats, making data integration and harmonization difficult. ⚠️ Temporal biases – Data isn’t collected at consistent intervals, making patient trajectory modeling unreliable. ⚠️ Ethical and privacy risks – AI models must balance data utility with patient rights and transparency. We Need to Get Rid of the “Kaggle Mentality” Too often, AI in healthcare is driven by data availability rather than clinical need. This “Kaggle mentality” leads to models being built simply because a dataset exists—not because it actually solves a well-defined clinical problem. We Need a New Mindset 🔹 AI development should start with a clearly defined clinical need and a priori data characteristics—not just whatever data happens to be available. 🔹 Datasets should be characterized for specific use cases and labeled accordingly. Not all data is suitable for all AI applications, and pretending otherwise leads to misleading claims. 🔹 Hot take: It’s a waste of resources to let individual researchers and startups decide which data is useful—especially when their work is often backed by unscrutinized claims to funders like VCs who lack the expertise to evaluate these complexities. We need to move beyond opportunistic, dataset-driven development and instead align data strategy with real clinical needs. #AIinHealthcare #MachineLearning #EHR #DataBias #ClinicalAI #HealthTech 🔗 References: [1] https://lnkd.in/dTTCWJnM [2] https://lnkd.in/dzKTSxtA

🌟 New Blueprint for Responsible AI in Healthcare! 🌟 Explore insights from Mass General Brigham's AI Governance Committee on implementing ethical AI in healthcare. This comprehensive study offers a detailed framework for integrating AI tools, ensuring fairness, safety, and effectiveness in patient care. Key Takeaways: 🔍 Core Principles for AI: The framework emphasizes nine key pillars—fairness, equity, privacy, safety, transparency, explainability, robustness, accountability, and patient benefit. 🤝 Multidisciplinary Collaboration: A team of experts from diverse fields established and refined these guidelines through literature review and hands-on case studies. 💡 Case Study: Ambient Documentation: Generative AI tools were piloted to streamline clinical note-taking, enhancing efficiency while addressing privacy and usability challenges. 📊 Continuous Monitoring: Dynamic evaluation metrics ensure tools adapt effectively to changing clinical practices and patient demographics. 🌍 Equity in Focus: The framework addresses bias by leveraging diverse training datasets and focusing on equitable outcomes for all patient demographics. This framework is a vital resource for healthcare institutions striving to responsibly adopt AI while prioritizing patient safety and ethical standards. #AIInHealthcare #ResponsibleAI #DigitalMedicine #GenerativeAI #EthicalAI #PatientSafety #HealthcareInnovation #AIEquity #HealthTech #FutureOfMedicine https://lnkd.in/gJqRVGc2

This paper discusses priorities for advancing the safe, equitable, and effective integration of AI into health and healthcare systems, emphasizing its transformative potential under the National Academy of Medicine’s Vital Directions for Health and Health Care: Priorities for 2025 initiative. 1️⃣ Identifies four key policy domains: ensuring safe and trustworthy AI, developing an AI-competent healthcare workforce, investing in AI-related research, and clarifying AI liability and responsibilities. 2️⃣ Reviews historical AI milestones and highlights current challenges such as inequity, bias, limited diffusion, and the need for real-world validation and monitoring ("algorithmovigilance"). 3️⃣ Recommends governance structures balancing fixed regulations and adaptive frameworks to address the dynamic nature of AI. 4️⃣ Advocates for targeted research in areas like disease characterization, AI-enabled drug discovery, and precision medicine to enhance diagnosis, care delivery, and operational efficiency. 5️⃣ Stresses workforce development by integrating AI education across healthcare roles, promoting interdisciplinary collaboration, and addressing clinician burnout through streamlined learning. 6️⃣ Calls for clear policies on data ownership, interoperability, and privacy to ensure diverse and representative datasets while fostering equity. 7️⃣ Highlights unresolved liability issues, urging clarification of responsibilities among developers, clinicians, and institutions to encourage responsible AI adoption. ✍🏻 Michael Matheny, Jennifer Goldsack, Suchi Saria, Nigam Shah, Jackie Gerhart, I. Glenn Cohen, W. Nicholson Price II, Bakul Patel, Philip Payne, Peter Embí, M.D., M.S., Brian Anderson, MD, Eric Horvitz. Artificial Intelligence in Health and Health Care: Priorities for Action. Health Affairs. 2025. DOI: 10.1377/hlthaff.2024.01003

AI in healthcare isn’t as neutral as you think.  AI could harm the very patients it’s meant to help. Without addressing the bias, we will never be able to benefit from the good. Here’s how we can fix it.  1. 𝗜𝗺𝗽𝗿𝗼𝘃𝗲 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 AI models are only as good as the data they are trained on. Unfortunately, many datasets lack diversity, often overrepresenting patients from certain regions or demographics. Ensuring datasets are inclusive of all populations is key to reducing bias. 2. 𝗥𝗶𝗴𝗼𝗿𝗼𝘂𝘀 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 AI tools must be tested across diverse populations before deployment. Studies have highlighted how biased algorithms can worsen health disparities at every stage of development. Rigorous validation ensures that these tools perform equitably for all patients. 3. 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆 𝗮𝗻𝗱 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆 Healthcare professionals need to understand how AI models make decisions. Lack of transparency can lead to mistrust and misuse. Explainable AI not only builds trust but also helps identify and correct biases in the system. 4. 𝗠𝘂𝗹𝘁𝗶-𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 Bias mitigation requires collaboration between AI developers, clinicians, policy makers, and patient advocates. Diverse perspectives help identify blind spots and create solutions that work for everyone. 5. 𝗢𝗻𝗴𝗼𝗶𝗻𝗴 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 Bias doesn’t stop at deployment. Continuous monitoring is needed to ensure AI tools adapt to new data and evolving healthcare needs. For instance, algorithms trained on outdated or incomplete data may maintain errors over time. Only by addressing these areas, can we see the benefits of AI in healthcare, such as reducing errors, aiding diagnoses, and personalizing treatments for all. What steps are your organization taking to ensure fairness in AI healthcare tools?

My latest article, Protecting Patient Care In The Age Of Algorithms: An AI Governance Model For Healthcare, is live on Forbes Technology Council. The use of artificial intelligence in healthcare demands a robust governance model to protect patient care. While AI offers significant opportunities for enhanced diagnostics, treatment planning, and operational efficiency, it also introduces risks related to transparency, bias, and accountability. A well-structured governance model is essential not only for leveraging AI’s potential but also for ensuring that patient care remains safe, ethical, and effective. To address these challenges, I propose a comprehensive AI governance framework that includes: Rigorous Validation and Testing: Ensuring that AI systems are thoroughly tested and validated in clinical settings to prevent harm due to inaccuracies or unanticipated behaviors. Transparency and Accountability: Requiring clear documentation of algorithmic processes so that healthcare providers understand how decisions are made and can trust the tools they use. Continuous Monitoring: Establishing mechanisms for ongoing oversight and real-time evaluation of AI performance, enabling prompt responses to any emerging issues. Collaborative Oversight: Advocating for a partnership among regulators, healthcare providers, and technology developers to create standards and best practices that balance innovation with patient safety. #RiskNeverSleeps https://lnkd.in/eW7PPkkA

AI in healthcare does not fail because of algorithms. It fails because organisations confuse technology adoption with transformation. After working on AI initiatives across clinical and operational settings, one lesson has become very clear to me: most AI projects stall not because the model is weak, but because the system around it is unprepared. That insight shaped our recently published open-access education article on AI adoption in healthcare. We argue that successful AI adoption is fundamentally a leadership and organisational challenge, not a technical one. In the paper, we propose a simple but rigorous five-frame transformation approach: Aspire – Be explicit about why AI matters. What clinical or system problem are we truly trying to solve, and how should AI support (not override) clinician judgement? Assess – Look honestly at readiness. Beyond data and infrastructure, this includes governance, workflows, trust, and mindsets on the ground. Architect – Design a balanced portfolio of initiatives, paired with behavioural levers such as role modelling, incentives, and capability building. Act – Execute with discipline. Embed AI into real workflows, define accountability clearly, and measure what truly matters: safety, cognitive load, and outcomes. Advance – Institutionalise learning, ethics, and continuous improvement so AI becomes part of a learning health system rather than a one-off pilot. A core theme we emphasise is this: performance and organisational health must improve together. A technically “successful” AI tool that erodes trust or autonomy will not scale. A positive culture without measurable impact will not last. While the framework applies broadly, we place particular emphasis on emergency medicine, where decisions are time-critical and poorly designed AI can increase, rather than reduce, complexity. The question for healthcare leaders today is no longer “Can this AI model work?” It is “Can our organisation adopt it responsibly, sustainably, and in service of patients and clinicians?” AI adoption is not the end of transformation. It is the beginning of a more intentional, human-centred way of delivering care. I would be interested to hear from others: What has been the biggest non-technical barrier to AI adoption in your organisation? #HealthcareLeadership #ClinicalAI #DigitalHealth #EmergencyMedicine #HealthSystemTransformation #LearningHealthSystems https://lnkd.in/gw6ju8c8

The VA Inspector General’s recent advisory on the use of AI in clinical environments is a reminder of how rapidly technology is reshaping healthcare operations. As reported in Task & Purpose, clinicians are already using AI tools to support documentation and workflows—often ahead of formal review and governance processes. As I shared in the article, this should not be interpreted as a reason to slow or stop AI adoption. Veterans deserve access to the best technologies available. But innovation in healthcare must always be matched with clear governance, clinical validation, and accountability. This balance—driving innovation while safeguarding quality and trust—is exactly the challenge facing health systems today. AI has enormous potential to reduce clinician burden and improve quality and access to care, but we cannot underestimate the need for disciplined evaluation and transparent implementation. The real policy challenge is not whether to use AI—that decision has already been made by clinicians on the front lines—but how to deploy it responsibly at scale, including: • Clinical oversight and testing • Strong data privacy protections • Transparency around model limitations • Ongoing monitoring for bias and safety AI is quickly becoming part of the core operating model of healthcare. Leaders who treat it as an IT issue will fall behind; leaders who treat it as a clinical and organizational transformation will shape the future. If we get this right, AI can meaningfully improve care delivery for veterans and support the healthcare workforce. If we get it wrong, we risk eroding trust in both technology and our institutions. #HealthcareLeadership #BoardGovernance #DigitalStrategy #AIinMedicine #VeteransCare #AI #Veterans https://lnkd.in/e5mj_d-F

This editorial provides a crucial, actionable framework for healthcare organizations to navigate the mandatory requirements of the new EU AI Act, ensuring the safe and compliant integration of AI into critical care settings. • What? It proposes a checklist-based methodology for the structured implementation of Artificial Intelligence (AI) policies within high-acuity healthcare settings like anaesthesia and intensive care units. • Why? To address the upcoming European Union AI Act, which imposes binding legal obligations on healthcare organizations to ensure AI is used in a safe, transparent, and governable manner, and to navigate the complex ethical and operational challenges of integrating AI into patient care . • How? By providing a comprehensive, two-part checklist that guides healthcare professionals in systematically evaluating AI systems. The checklist covers Clinical/Technical Validation (e.g., performance, safety, regulatory compliance) and Governance/Compliance (e.g., adherence to the AI Act, GDPR, and establishing clear organizational responsibility). #AIinHealthcare #HealthTech #AIpolicy #DigitalHealth #PatientSafety #EUAIAct #MedicalAI #ClinicalAI