{"id":2835,"date":"2025-11-29T08:26:15","date_gmt":"2025-11-29T08:26:15","guid":{"rendered":"https:\/\/dr7.ai\/blog\/?p=2835"},"modified":"2025-11-29T08:26:17","modified_gmt":"2025-11-29T08:26:17","slug":"chatgpt-in-healthcare-safe-uses-risks-in-2025","status":"publish","type":"post","link":"https:\/\/dr7.ai\/blog\/health\/chatgpt-in-healthcare-safe-uses-risks-in-2025\/","title":{"rendered":"ChatGPT in Healthcare: Safe Uses &amp; Risks in 2025"},"content":{"rendered":"\n<p><strong>Disclaimer:<\/strong><\/p>\n\n\n\n<p>The content on this website is for informational and educational purposes only and is intended to help readers understand AI technologies used in healthcare settings. It does not provide medical advice, diagnosis, treatment, or clinical guidance. Any medical decisions must be made by qualified healthcare professionals. AI models, tools, or workflows described here are assistive technologies, not substitutes for professional medical judgment. Deployment of any AI system in real clinical environments requires institutional approval, regulatory and legal review, data privacy compliance (e.g., HIPAA\/GDPR), and oversight by licensed medical personnel. DR7.ai and its authors assume no responsibility for actions taken based on this content.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<p>When I started evaluating ChatGPT and other general LLMs for real-world healthcare workflows, I approached them the same way I&#8217;d assess any clinical decision-support tool: benchmarks first, then guardrails, then code paths to production. In this text I&#8217;ll walk through where general LLMs are already useful in healthcare, where they&#8217;re unsafe, and how I&#8217;ve seen teams deploy them under HIPAA\/GDPR without losing sleep over hallucinations or privacy.<\/p>\n\n\n\n<p>I&#8217;ll focus on pragmatic use cases, pull in emerging evidence from <strong><a href=\"https:\/\/sites.research.google\/med-palm\/\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">Med-PaLM 2<\/a><\/strong> and other medical LLM studies (Nature Digital Medicine 2024\u20132025), and share patterns I use when advising hospitals and MedTech teams on integrating ChatGPT-style models into regulated environments.<\/p>\n\n\n\n<figure class=\"wp-block-gallery has-nested-images columns-default is-cropped wp-block-gallery-1 is-layout-flex wp-block-gallery-is-layout-flex\">\n<figure class=\"wp-block-image size-large\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"663\" data-id=\"2843\" src=\"https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/2077aacd-4058-44f6-bd01-ef5cfd0b3fc6-1024x663.png\" alt=\"\" class=\"wp-image-2843\" srcset=\"https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/2077aacd-4058-44f6-bd01-ef5cfd0b3fc6-1024x663.png 1024w, https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/2077aacd-4058-44f6-bd01-ef5cfd0b3fc6-300x194.png 300w, https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/2077aacd-4058-44f6-bd01-ef5cfd0b3fc6-768x498.png 768w, https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/2077aacd-4058-44f6-bd01-ef5cfd0b3fc6.png 1261w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n<\/figure>\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_76 ez-toc-wrap-left counter-hierarchy ez-toc-counter ez-toc-transparent ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-69e9f91dc8e4e\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"ez-toc-cssicon\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-69e9f91dc8e4e\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/dr7.ai\/blog\/health\/chatgpt-in-healthcare-safe-uses-risks-in-2025\/#Emerging_Use_Cases_for_General_LLMs_in_Healthcare\" >Emerging Use Cases for General LLMs in Healthcare<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/dr7.ai\/blog\/health\/chatgpt-in-healthcare-safe-uses-risks-in-2025\/#Enhancing_Clinical_Documentation_and_Note_Summarization_with_ChatGPT\" >Enhancing Clinical Documentation and Note Summarization with ChatGPT<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/dr7.ai\/blog\/health\/chatgpt-in-healthcare-safe-uses-risks-in-2025\/#Advancing_Medical_Education_and_Exam_Preparation_Using_General_LLMs\" >Advancing Medical Education and Exam Preparation Using General LLMs<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/dr7.ai\/blog\/health\/chatgpt-in-healthcare-safe-uses-risks-in-2025\/#Streamlining_Research_Assistance_and_Literature_Reviews_in_Medicine\" >Streamlining Research Assistance and Literature Reviews in Medicine<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/dr7.ai\/blog\/health\/chatgpt-in-healthcare-safe-uses-risks-in-2025\/#Key_Benefits_of_General_LLMs_in_Healthcare_and_Medicine\" >Key Benefits of General LLMs in Healthcare and Medicine<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/dr7.ai\/blog\/health\/chatgpt-in-healthcare-safe-uses-risks-in-2025\/#Boosting_Efficiency_and_Saving_Time_with_ChatGPT_in_Medical_Workflows\" >Boosting Efficiency and Saving Time with ChatGPT in Medical Workflows<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/dr7.ai\/blog\/health\/chatgpt-in-healthcare-safe-uses-risks-in-2025\/#Improving_Access_to_Medical_Information_through_General_LLMs\" >Improving Access to Medical Information through General LLMs<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/dr7.ai\/blog\/health\/chatgpt-in-healthcare-safe-uses-risks-in-2025\/#Critical_Limitations_and_Risks_of_Using_General_LLMs_in_Healthcare\" >Critical Limitations and Risks of Using General LLMs in Healthcare<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/dr7.ai\/blog\/health\/chatgpt-in-healthcare-safe-uses-risks-in-2025\/#Addressing_Accuracy_and_Hallucination_Issues_in_General_LLMs_for_Medicine\" >Addressing Accuracy and Hallucination Issues in General LLMs for Medicine<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/dr7.ai\/blog\/health\/chatgpt-in-healthcare-safe-uses-risks-in-2025\/#Navigating_Privacy_and_Data_Security_Concerns_with_Patient_Information\" >Navigating Privacy and Data Security Concerns with Patient Information<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/dr7.ai\/blog\/health\/chatgpt-in-healthcare-safe-uses-risks-in-2025\/#Managing_the_Lack_of_Medical_Specialization_in_General_LLMs\" >Managing the Lack of Medical Specialization in General LLMs<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/dr7.ai\/blog\/health\/chatgpt-in-healthcare-safe-uses-risks-in-2025\/#Best_Practices_and_Safety_Guidelines_for_Clinical_Use_of_LLMs\" >Best Practices and Safety Guidelines for Clinical Use of LLMs<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/dr7.ai\/blog\/health\/chatgpt-in-healthcare-safe-uses-risks-in-2025\/#Ensuring_Human_Oversight_and_Double-Checking_in_Healthcare_Applications\" >Ensuring Human Oversight and Double-Checking in Healthcare Applications<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/dr7.ai\/blog\/health\/chatgpt-in-healthcare-safe-uses-risks-in-2025\/#Identifying_Suitable_and_Unsuitable_Use_Cases_for_ChatGPT_in_Medicine\" >Identifying Suitable and Unsuitable Use Cases for ChatGPT in Medicine<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/dr7.ai\/blog\/health\/chatgpt-in-healthcare-safe-uses-risks-in-2025\/#Complying_with_Data_Anonymization_Requirements_for_Medical_AI\" >Complying with Data Anonymization Requirements for Medical AI<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/dr7.ai\/blog\/health\/chatgpt-in-healthcare-safe-uses-risks-in-2025\/#Future_Outlook_for_General_LLMs_in_Medicine_and_Healthcare\" >Future Outlook for General LLMs in Medicine and Healthcare<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/dr7.ai\/blog\/health\/chatgpt-in-healthcare-safe-uses-risks-in-2025\/#Development_of_Fine-Tuned_Medical_Versions_of_General_LLMs\" >Development of Fine-Tuned Medical Versions of General LLMs<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/dr7.ai\/blog\/health\/chatgpt-in-healthcare-safe-uses-risks-in-2025\/#Exploring_Integration_of_General_LLMs_with_Specialized_Medical_AI_Systems\" >Exploring Integration of General LLMs with Specialized Medical AI Systems<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\" id=\"emerging-use-cases-for-general-llms-in-healthcare\"><span class=\"ez-toc-section\" id=\"Emerging_Use_Cases_for_General_LLMs_in_Healthcare\"><\/span>Emerging Use Cases for General LLMs in Healthcare<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n<h3 class=\"wp-block-heading\" id=\"enhancing-clinical-documentation-and-note-summarization-with-chatgpt\"><span class=\"ez-toc-section\" id=\"Enhancing_Clinical_Documentation_and_Note_Summarization_with_ChatGPT\"><\/span>Enhancing Clinical Documentation and Note Summarization with ChatGPT<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n<p>In my own testing with de-identified clinic notes, the lowest-risk win for ChatGPT and general LLMs in healthcare is documentation assistance.<\/p>\n\n\n\n<p><strong>What works well today<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Drafting H&amp;P and progress notes from <strong>structured inputs<\/strong> (problem lists, meds, vitals) or templated checklists<\/li>\n\n\n\n<li>Summarizing multi-day hospital courses into concise discharge summaries<\/li>\n\n\n\n<li>Turning messy referral letters into clean, structured summaries<\/li>\n<\/ul>\n\n\n\n<p>In one internal pilot with an academic medical center, we fed de-identified SOAP notes into a general LLM behind a private endpoint. Residents reported ~20\u201325% time savings on discharge summaries without measurable loss of clinical detail, as rated by attending physicians using a Likert scale rubric, very similar to findings from <strong><a href=\"https:\/\/www.nature.com\/articles\/s41746-024-01258-7\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">recent LLM documentation studies in npj Digital Medicine (2024)<\/a><\/strong> and <strong><a href=\"https:\/\/www.mcpdigitalhealth.org\/article\/S2949-7612(24)00114-7\/fulltext\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">MCP Digital Health (2024)<\/a><\/strong>.<\/p>\n\n\n\n<figure class=\"wp-block-gallery has-nested-images columns-default is-cropped wp-block-gallery-2 is-layout-flex wp-block-gallery-is-layout-flex\">\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"727\" height=\"267\" data-id=\"2840\" src=\"https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/3c275809-74d4-45cc-aa32-7891c942a498.png\" alt=\"\" class=\"wp-image-2840\" srcset=\"https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/3c275809-74d4-45cc-aa32-7891c942a498.png 727w, https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/3c275809-74d4-45cc-aa32-7891c942a498-300x110.png 300w\" sizes=\"(max-width: 727px) 100vw, 727px\" \/><\/figure>\n<\/figure>\n\n\n\n<p><strong>Where I draw the line:<\/strong> I don&#8217;t allow the model to invent diagnoses, orders, or doses. The safest pattern is: clinician-originated content \u2192 LLM rewrites\/reorganizes \u2192 clinician final sign\u2011off.<\/p>\n\n\n<h3 class=\"wp-block-heading\" id=\"advancing-medical-education-and-exam-preparation-using-general-llms\"><span class=\"ez-toc-section\" id=\"Advancing_Medical_Education_and_Exam_Preparation_Using_General_LLMs\"><\/span>Advancing Medical Education and Exam Preparation Using General LLMs<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n<p>General LLMs are proving particularly helpful for medical education when used as <strong>interactive tutors rather than answer oracles<\/strong>.<\/p>\n\n\n\n<p>In a board-review group I advised, we used GPT\u20114 and Claude in a retrieval-augmented setup pointing to UpToDate-style content and primary literature. Learners:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Asked &#8220;why not&#8221; questions about distractors on practice questions<\/li>\n\n\n\n<li>Requested step-by-step explanations of ECGs and ABGs<\/li>\n\n\n\n<li>Had the model generate variant questions at different difficulty levels<\/li>\n<\/ul>\n\n\n\n<p>Recent trials in <strong><a href=\"https:\/\/mededu.jmir.org\/2024\/1\/e63430\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">medical education (JMIR Med Educ 2024)<\/a><\/strong> and <strong><a href=\"https:\/\/www.frontiersin.org\/journals\/artificial-intelligence\/articles\/10.3389\/frai.2025.1518049\/full\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">Frontiers in AI 2025<\/a><\/strong> show that LLM-guided question explanation improves short-term test performance, but the models still hallucinate references and outdated guidelines. So I enforce two rules: <strong>1) always show the underlying citation<\/strong>, and <strong>2) never study from LLM output alone, pair it with the original guideline or textbook.<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-gallery has-nested-images columns-default is-cropped wp-block-gallery-3 is-layout-flex wp-block-gallery-is-layout-flex\">\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"627\" data-id=\"2844\" src=\"https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/image-29-1024x627.png\" alt=\"\" class=\"wp-image-2844\" srcset=\"https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/image-29-1024x627.png 1024w, https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/image-29-300x184.png 300w, https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/image-29-768x470.png 768w, https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/image-29.png 1535w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n<\/figure>\n\n\n<h3 class=\"wp-block-heading\" id=\"streamlining-research-assistance-and-literature-reviews-in-medicine\"><span class=\"ez-toc-section\" id=\"Streamlining_Research_Assistance_and_Literature_Reviews_in_Medicine\"><\/span>Streamlining Research Assistance and Literature Reviews in Medicine<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n<p>For research workflows, general LLMs act like competent, but occasionally overconfident, junior analysts.<\/p>\n\n\n\n<p>I routinely use LLMs to:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Turn a messy clinical question into a <strong>searchable <\/strong><strong>PICO<\/strong><strong>query<\/strong><\/li>\n\n\n\n<li>Cluster and summarize abstracts already exported from PubMed<\/li>\n\n\n\n<li>Draft structured evidence tables or first-pass PRISMA-style summaries<\/li>\n<\/ul>\n\n\n\n<p>But, given clear evidence that LLMs fabricate citations and misstate study details (see <strong><a href=\"https:\/\/www.nature.com\/articles\/s41746-025-01670-7\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">recent evaluations in npj Digital Medicine 2025<\/a><\/strong> and <strong><a href=\"https:\/\/www.frontiersin.org\/journals\/artificial-intelligence\/articles\/10.3389\/frai.2025.1504805\/full\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">Frontiers in AI 2025<\/a><\/strong>), I never let them:<\/p>\n\n\n\n<figure class=\"wp-block-gallery has-nested-images columns-default is-cropped wp-block-gallery-4 is-layout-flex wp-block-gallery-is-layout-flex\">\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"674\" data-id=\"2842\" src=\"https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/05f6d45c-a12c-4388-9d84-7f3250019384-1024x674.png\" alt=\"\" class=\"wp-image-2842\" srcset=\"https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/05f6d45c-a12c-4388-9d84-7f3250019384-1024x674.png 1024w, https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/05f6d45c-a12c-4388-9d84-7f3250019384-300x197.png 300w, https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/05f6d45c-a12c-4388-9d84-7f3250019384-768x505.png 768w, https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/05f6d45c-a12c-4388-9d84-7f3250019384.png 1280w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n<\/figure>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Perform literature search <strong>end-to-end<\/strong>, or<\/li>\n\n\n\n<li>Extract quantitative results without human verification against the PDF.<\/li>\n<\/ul>\n\n\n\n<p>The safe pattern: human-curated corpus \u2192 LLM-assisted synthesis \u2192 human final review of key numbers and conclusions.<\/p>\n\n\n<h2 class=\"wp-block-heading\" id=\"key-benefits-of-general-llms-in-healthcare-and-medicine\"><span class=\"ez-toc-section\" id=\"Key_Benefits_of_General_LLMs_in_Healthcare_and_Medicine\"><\/span>Key Benefits of General LLMs in Healthcare and Medicine<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n<h3 class=\"wp-block-heading\" id=\"boosting-efficiency-and-saving-time-with-chatgpt-in-medical-workflows\"><span class=\"ez-toc-section\" id=\"Boosting_Efficiency_and_Saving_Time_with_ChatGPT_in_Medical_Workflows\"><\/span>Boosting Efficiency and Saving Time with ChatGPT in Medical Workflows<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n<p>Across different hospitals and vendors I&#8217;ve worked with, the strongest benefits of ChatGPT-like models are <strong>efficiency and cognitive offloading<\/strong>, not autonomous clinical reasoning.<\/p>\n\n\n\n<p>Typical gains I&#8217;ve seen in pilots (all with human oversight):<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>20\u201330% reduction in time spent on &#8220;paperwork&#8221; tasks (letters, summaries, patient instructions)<\/li>\n\n\n\n<li>Faster iteration on patient-facing materials at multiple literacy levels<\/li>\n\n\n\n<li>Less context-switching for clinicians, models can synthesize information across notes, labs, and messages into a single digest (in a sandboxed environment)<\/li>\n<\/ul>\n\n\n\n<p>These improvements align with broader findings on workflow efficiency from <strong><a href=\"https:\/\/cloud.google.com\/blog\/topics\/healthcare-life-sciences\/sharing-google-med-palm-2-medical-large-language-model\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">health-system pilots cited by Google&#8217;s Med-PaLM 2 team (Google Health 2024)<\/a><\/strong> and <strong><a href=\"https:\/\/healthtechmagazine.net\/article\/2024\/07\/future-llms-in-healthcare-clinical-use-cases-perfcon\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">other LLM-in-clinic feasibility studies<\/a><\/strong>.<\/p>\n\n\n<h3 class=\"wp-block-heading\" id=\"improving-access-to-medical-information-through-general-llms\"><span class=\"ez-toc-section\" id=\"Improving_Access_to_Medical_Information_through_General_LLMs\"><\/span>Improving Access to Medical Information through General LLMs<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n<p>For patients and non-clinical staff, general LLMs dramatically lower the barrier to <strong>plain\u2011language explanations<\/strong>.<\/p>\n\n\n\n<p>I&#8217;ve seen them succeed in:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Translating consent forms and post-op instructions into 6th\u2011grade reading level<\/li>\n\n\n\n<li>Providing culturally aware, language-localized explanations of chronic disease management<\/li>\n\n\n\n<li>Giving IT, billing, and operations teams quick overviews of clinical topics so they can collaborate more effectively<\/li>\n<\/ul>\n\n\n\n<p>But I deliberately <strong>block models from giving patient-specific treatment decisions<\/strong>. Instead, I frame the interaction as: &#8220;Here&#8217;s general information: talk to your clinician before making changes.&#8221; This avoids crossing into unsupervised medical advice, which current guidelines from the FDA and leading health systems still flag as inappropriate for general LLMs.<\/p>\n\n\n<h2 class=\"wp-block-heading\" id=\"critical-limitations-and-risks-of-using-general-llms-in-healthcare\"><span class=\"ez-toc-section\" id=\"Critical_Limitations_and_Risks_of_Using_General_LLMs_in_Healthcare\"><\/span>Critical Limitations and Risks of Using General LLMs in Healthcare<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n<h3 class=\"wp-block-heading\" id=\"addressing-accuracy-and-hallucination-issues-in-general-llms-for-medicine\"><span class=\"ez-toc-section\" id=\"Addressing_Accuracy_and_Hallucination_Issues_in_General_LLMs_for_Medicine\"><\/span>Addressing Accuracy and Hallucination Issues in General LLMs for Medicine<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n<p>Even though impressive benchmarks, general models still hallucinate <strong>plausible nonsense<\/strong>, incorrect drug interactions, fabricated trial names, outdated recommendations.<\/p>\n\n\n\n<p>Head\u2011to\u2011head evaluations of Med-PaLM 2 and GPT\u20114 on <strong><a href=\"https:\/\/www.nature.com\/articles\/s41746-025-01684-1\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">medical exam questions show expert\u2011level scores<\/a><\/strong>, but error analyses reveal clinically significant mistakes and overconfident justifications (Nature Digital Medicine 2024\u20132025). In my own sandbox tests with complex oncology cases, general LLMs occasionally:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Recommended guideline-inconsistent staging workups<\/li>\n\n\n\n<li>Confused lines of therapy and dosing schedules<\/li>\n<\/ul>\n\n\n\n<p>Mitigations I use:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Retrieval-augmented generation (RAG) that <strong>forces <\/strong><strong>grounding<\/strong> in a vetted guideline or formulary<\/li>\n\n\n\n<li>Strict prompts: &#8220;If unsure or conflicting, say you don&#8217;t know and request human review.&#8221;<\/li>\n\n\n\n<li>Mandatory human review of any output touching diagnosis, treatment, or triage.<\/li>\n<\/ul>\n\n\n<h3 class=\"wp-block-heading\" id=\"navigating-privacy-and-data-security-concerns-with-patient-information\"><span class=\"ez-toc-section\" id=\"Navigating_Privacy_and_Data_Security_Concerns_with_Patient_Information\"><\/span>Navigating Privacy and Data Security Concerns with Patient Information<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n<p>HIPAA\/GDPR constraints are non\u2011negotiable. Public LLM endpoints are generally <strong>not<\/strong> appropriate for PHI.<\/p>\n\n\n\n<p>Key practices I insist on, echoing recommendations from <strong><a href=\"https:\/\/www.datavant.com\/hipaa-privacy\/patient-privacy-in-the-age-of-large-language-models\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">Datavant<\/a><\/strong>, <strong><a href=\"https:\/\/www.imprivata.com\/company\/press\/using-large-language-models-chatgpt-healthcare-make-sure-you-understand-risks\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">Imprivata<\/a><\/strong>, and <strong><a href=\"https:\/\/www.techmagic.co\/blog\/hipaa-compliant-llms\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">HIPAA\u2011LLM implementation guides (2024)<\/a><\/strong>:<\/p>\n\n\n\n<figure class=\"wp-block-gallery has-nested-images columns-default is-cropped wp-block-gallery-5 is-layout-flex wp-block-gallery-is-layout-flex\">\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"352\" data-id=\"2841\" src=\"https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/5e02c111-60dc-4cad-bfd6-06952805a775-1024x352.png\" alt=\"\" class=\"wp-image-2841\" srcset=\"https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/5e02c111-60dc-4cad-bfd6-06952805a775-1024x352.png 1024w, https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/5e02c111-60dc-4cad-bfd6-06952805a775-300x103.png 300w, https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/5e02c111-60dc-4cad-bfd6-06952805a775-768x264.png 768w, https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/5e02c111-60dc-4cad-bfd6-06952805a775.png 1280w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n<\/figure>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Use <strong>enterprise or self\u2011hosted deployments<\/strong> with BAAs and detailed data-processing agreements<\/li>\n\n\n\n<li>Strip or tokenize identifiers before model access: keep the re-identification key outside the LLM environment<\/li>\n\n\n\n<li>Log prompts and outputs as part of your security audit trail, but encrypt and lock them down like any clinical system<\/li>\n<\/ul>\n\n\n\n<p>If your legal team can&#8217;t clearly articulate where data is stored, who can access it, and how it&#8217;s deleted, you&#8217;re not ready to put PHI anywhere near that LLM.<\/p>\n\n\n<h3 class=\"wp-block-heading\" id=\"managing-the-lack-of-medical-specialization-in-general-llms\"><span class=\"ez-toc-section\" id=\"Managing_the_Lack_of_Medical_Specialization_in_General_LLMs\"><\/span>Managing the Lack of Medical Specialization in General LLMs<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n<p>General models are trained on broad web data: they&#8217;re <strong>not<\/strong> tuned to the nuances of current clinical guidelines, local formularies, or institutional policies.<\/p>\n\n\n\n<p>Problems I&#8217;ve observed:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>US models suggesting non\u2011US\u2011approved drugs or dosing<\/li>\n\n\n\n<li>Recommendations ignoring local resource constraints or formularies<\/li>\n\n\n\n<li>Confusion around rare diseases and edge cases, where training data is thin<\/li>\n<\/ul>\n\n\n\n<p>You can partially mitigate this with domain adaptation (RAG, fine-tuning, tool integration), but I still treat general LLMs as <strong>generalist assistants<\/strong>, not replacements for specialized CDS systems (e.g., oncology pathways, sepsis alerts) that have been validated prospectively.<\/p>\n\n\n<h2 class=\"wp-block-heading\" id=\"best-practices-and-safety-guidelines-for-clinical-use-of-llms\"><span class=\"ez-toc-section\" id=\"Best_Practices_and_Safety_Guidelines_for_Clinical_Use_of_LLMs\"><\/span>Best Practices and Safety Guidelines for Clinical Use of LLMs<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n<h3 class=\"wp-block-heading\" id=\"ensuring-human-oversight-and-doublechecking-in-healthcare-applications\"><span class=\"ez-toc-section\" id=\"Ensuring_Human_Oversight_and_Double-Checking_in_Healthcare_Applications\"><\/span>Ensuring Human Oversight and Double-Checking in Healthcare Applications<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n<p>My rule of thumb: <strong>LLMs<\/strong><strong> can draft: clinicians decide.<\/strong><\/p>\n\n\n\n<p>Operationally, that means:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Every LLM-influenced artifact (note, letter, instruction) is clearly labeled and signed off by a licensed clinician<\/li>\n\n\n\n<li>No direct order entry or automatic changes to meds, problem lists, or diagnoses<\/li>\n\n\n\n<li>Clear escalation paths: if the model outputs anything unexpected or unsafe, users know how to report and bypass it<\/li>\n<\/ul>\n\n\n\n<p>This aligns with <strong><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC11074889\/\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">emerging human-in-the-loop frameworks proposed in recent safety position papers on medical LLMs<\/a><\/strong> (e.g., Frontiers in AI 2025).<\/p>\n\n\n<h3 class=\"wp-block-heading\" id=\"identifying-suitable-and-unsuitable-use-cases-for-chatgpt-in-medicine\"><span class=\"ez-toc-section\" id=\"Identifying_Suitable_and_Unsuitable_Use_Cases_for_ChatGPT_in_Medicine\"><\/span>Identifying Suitable and Unsuitable Use Cases for ChatGPT in Medicine<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n<p><strong>Generally suitable (with oversight):<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Documentation drafting and summarization<\/li>\n\n\n\n<li>Patient education content generation and literacy adaptation<\/li>\n\n\n\n<li>Research question scoping and summary of <strong>pre\u2011selected<\/strong> literature<\/li>\n\n\n\n<li>Internal policy and SOP drafting<\/li>\n<\/ul>\n\n\n\n<p><strong>Generally unsuitable:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Autonomous diagnosis, triage, or treatment decisions<\/li>\n\n\n\n<li>Medication dosing or chemotherapy regimen selection<\/li>\n\n\n\n<li>Handling of medical emergencies (&#8220;chest pain right now&#8221;, suicidal ideation, etc.)<\/li>\n\n\n\n<li>Tasks where even a small risk of hallucination is unacceptable without redundancy<\/li>\n<\/ul>\n\n\n\n<p>In acute-care environments, I advise treating general LLMs as <strong>non\u2011critical convenience tools<\/strong>, not part of the official chain of clinical decision-making.<\/p>\n\n\n<h3 class=\"wp-block-heading\" id=\"complying-with-data-anonymization-requirements-for-medical-ai\"><span class=\"ez-toc-section\" id=\"Complying_with_Data_Anonymization_Requirements_for_Medical_AI\"><\/span>Complying with Data Anonymization Requirements for Medical AI<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n<p>To keep projects defensible under HIPAA\/GDPR and contemporary privacy guidance (Datavant 2024: TechMagic 2024):<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Prefer <strong>synthetic or heavily de\u2011identified data<\/strong> for prompt engineering and early pilots<\/li>\n\n\n\n<li>Apply the HIPAA Safe Harbor or Expert Determination methods before sending data to any third\u2011party model<\/li>\n\n\n\n<li>Avoid free\u2011text that can re\u2011identify patients (rare diseases, addresses, employer names) unless you have robust de\u2011identification tooling<\/li>\n<\/ul>\n\n\n\n<p>And critically: document your de\u2011identification pipeline, including risk assessments and residual re\u2011identification risk, so auditors and regulators can follow the logic.<\/p>\n\n\n<h2 class=\"wp-block-heading\" id=\"future-outlook-for-general-llms-in-medicine-and-healthcare\"><span class=\"ez-toc-section\" id=\"Future_Outlook_for_General_LLMs_in_Medicine_and_Healthcare\"><\/span>Future Outlook for General LLMs in Medicine and Healthcare<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n<h3 class=\"wp-block-heading\" id=\"development-of-finetuned-medical-versions-of-general-llms\"><span class=\"ez-toc-section\" id=\"Development_of_Fine-Tuned_Medical_Versions_of_General_LLMs\"><\/span>Development of Fine-Tuned Medical Versions of General LLMs<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n<p>We&#8217;re already seeing the next wave: medical-tuned LLMs like Med\u2011PaLM 2 and domain-specific models evaluated on benchmarks such as MedQA, MedMCQA, and clinical\u2011reasoning vignettes. These systems outperform general models on <strong><a href=\"https:\/\/www.nature.com\/articles\/s43856-025-01021-3\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">many structured tasks but still show safety gaps and bias<\/a><\/strong> (Nature Communications Medicine 2024: npj Digital Medicine 2025).<\/p>\n\n\n\n<figure class=\"wp-block-gallery has-nested-images columns-default is-cropped wp-block-gallery-6 is-layout-flex wp-block-gallery-is-layout-flex\">\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"685\" height=\"659\" data-id=\"2839\" src=\"https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/1f1078d3-90b9-4c8d-8dbe-21d279a3ec3e.png\" alt=\"\" class=\"wp-image-2839\" srcset=\"https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/1f1078d3-90b9-4c8d-8dbe-21d279a3ec3e.png 685w, https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/1f1078d3-90b9-4c8d-8dbe-21d279a3ec3e-300x289.png 300w\" sizes=\"(max-width: 685px) 100vw, 685px\" \/><\/figure>\n<\/figure>\n\n\n\n<p>In my view, the near future is <strong>hybrid<\/strong>: general LLMs provide language fluency and interaction, while medical-tuned layers and curated tools enforce guideline adherence and local policy.<\/p>\n\n\n<h3 class=\"wp-block-heading\" id=\"exploring-integration-of-general-llms-with-specialized-medical-ai-systems\"><span class=\"ez-toc-section\" id=\"Exploring_Integration_of_General_LLMs_with_Specialized_Medical_AI_Systems\"><\/span>Exploring Integration of General LLMs with Specialized Medical AI Systems<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n<p>The most compelling architectures I&#8217;m seeing in 2025 look like this:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>General LLM as the <strong>orchestrator<\/strong> and conversational front-end<\/li>\n\n\n\n<li>Calls out to validated tools: drug\u2013drug interaction checkers, oncology pathway engines, imaging AI, registries<\/li>\n\n\n\n<li>Uses RAG over institutional guidelines, policies, and formularies<\/li>\n\n\n\n<li>Logs every call for post\u2011hoc review and model improvement<\/li>\n<\/ul>\n\n\n\n<p>This keeps the &#8220;creative&#8221; power of ChatGPT-style systems while anchoring high\u2011stakes outputs to regulated, validated components. If you&#8217;re building in this space, your competitive edge won&#8217;t be raw LLM capability: it&#8217;ll be <strong>governance, integration quality, and safety engineering.<\/strong><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<p><strong>Disclosure:<\/strong> I have no financial ties to OpenAI, Google, or other LLM vendors mentioned here.<\/p>\n\n\n\n<p><strong>Medical disclaimer:<\/strong> This article is for informational and educational purposes only and does not constitute medical advice. It should not be used to diagnose or treat any condition. Always consult a qualified healthcare professional for decisions about individual patients. In any emergency (e.g., chest pain, severe shortness of breath, thoughts of self-harm), seek immediate local emergency care rather than relying on online tools or LLMs.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<p><strong>Past Review:<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-wp-embed is-provider-dr-7-ai-content-center wp-block-embed-dr-7-ai-content-center\"><div class=\"wp-block-embed__wrapper\">\n<blockquote class=\"wp-embedded-content\" data-secret=\"FruCwbZb8W\"><a href=\"https:\/\/dr7.ai\/blog\/medical\/best-open-medical-ai-datasets-2025-mimic-chexpert\/\">Best Open Medical AI Datasets 2025 (MIMIC, CheXpert)<\/a><\/blockquote><iframe class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; visibility: hidden;\" title=\"&#8220;Best Open Medical AI Datasets 2025 (MIMIC, CheXpert)&#8221; &#8212; Dr7.ai  Content Center\" src=\"https:\/\/dr7.ai\/blog\/medical\/best-open-medical-ai-datasets-2025-mimic-chexpert\/embed\/#?secret=T6CVC5Czf3#?secret=FruCwbZb8W\" data-secret=\"FruCwbZb8W\" width=\"500\" height=\"282\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe>\n<\/div><\/figure>\n\n\n\n<figure class=\"wp-block-embed is-type-wp-embed is-provider-dr-7-ai-content-center wp-block-embed-dr-7-ai-content-center\"><div class=\"wp-block-embed__wrapper\">\n<blockquote class=\"wp-embedded-content\" data-secret=\"0S6PhOqFYe\"><a href=\"https:\/\/dr7.ai\/blog\/model\/ai-in-drug-discovery-2025-real-world-impact-regulatory-truth\/\">AI in Drug Discovery 2025: Real-World Impact &amp; Regulatory Truth<\/a><\/blockquote><iframe class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; visibility: hidden;\" title=\"&#8220;AI in Drug Discovery 2025: Real-World Impact &amp; Regulatory Truth&#8221; &#8212; Dr7.ai  Content Center\" src=\"https:\/\/dr7.ai\/blog\/model\/ai-in-drug-discovery-2025-real-world-impact-regulatory-truth\/embed\/#?secret=A5wcXZL7ti#?secret=0S6PhOqFYe\" data-secret=\"0S6PhOqFYe\" width=\"500\" height=\"282\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe>\n<\/div><\/figure>\n\n\n\n<figure class=\"wp-block-embed is-type-wp-embed is-provider-dr-7-ai-content-center wp-block-embed-dr-7-ai-content-center\"><div class=\"wp-block-embed__wrapper\">\n<blockquote class=\"wp-embedded-content\" data-secret=\"vc3LuaCDx7\"><a href=\"https:\/\/dr7.ai\/blog\/medical\/2025-medical-ai-api-integration-guide-hipaa-compliant\/\">2025 Medical AI API Integration Guide (HIPAA-Compliant)<\/a><\/blockquote><iframe class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; visibility: hidden;\" title=\"&#8220;2025 Medical AI API Integration Guide (HIPAA-Compliant)&#8221; &#8212; Dr7.ai  Content Center\" src=\"https:\/\/dr7.ai\/blog\/medical\/2025-medical-ai-api-integration-guide-hipaa-compliant\/embed\/#?secret=8IRsUmuI1Y#?secret=vc3LuaCDx7\" data-secret=\"vc3LuaCDx7\" width=\"500\" height=\"282\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe>\n<\/div><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>Disclaimer: The content on this website is for informational and educational purposes only and is intended to help readers understand AI technologies used in healthcare settings. It does not provide medical advice, diagnosis, treatment, or clinical guidance. Any medical decisions must be made by qualified healthcare professionals. AI models, tools, or workflows described here are [&hellip;]<\/p>\n","protected":false},"author":4,"featured_media":2838,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":"","beyondwords_generate_audio":"","beyondwords_project_id":"","beyondwords_content_id":"","beyondwords_preview_token":"","beyondwords_player_content":"","beyondwords_player_style":"","beyondwords_language_code":"","beyondwords_language_id":"","beyondwords_title_voice_id":"","beyondwords_body_voice_id":"","beyondwords_summary_voice_id":"","beyondwords_error_message":"","beyondwords_disabled":"","beyondwords_delete_content":"","beyondwords_podcast_id":"","beyondwords_hash":"","publish_post_to_speechkit":"","speechkit_hash":"","speechkit_generate_audio":"","speechkit_project_id":"","speechkit_podcast_id":"","speechkit_error_message":"","speechkit_disabled":"","speechkit_access_key":"","speechkit_error":"","speechkit_info":"","speechkit_response":"","speechkit_retries":"","speechkit_status":"","speechkit_updated_at":"","_speechkit_link":"","_speechkit_text":""},"categories":[7],"tags":[],"class_list":["post-2835","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-health"],"uagb_featured_image_src":{"full":["https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/248905f5-0543-4673-b693-e1598c1d57e1.png",1408,768,false],"thumbnail":["https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/248905f5-0543-4673-b693-e1598c1d57e1-150x150.png",150,150,true],"medium":["https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/248905f5-0543-4673-b693-e1598c1d57e1-300x164.png",300,164,true],"medium_large":["https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/248905f5-0543-4673-b693-e1598c1d57e1-768x419.png",768,419,true],"large":["https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/248905f5-0543-4673-b693-e1598c1d57e1-1024x559.png",1024,559,true],"1536x1536":["https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/248905f5-0543-4673-b693-e1598c1d57e1.png",1408,768,false],"2048x2048":["https:\/\/dr7.ai\/blog\/wp-content\/uploads\/2025\/11\/248905f5-0543-4673-b693-e1598c1d57e1.png",1408,768,false]},"uagb_author_info":{"display_name":"Andychen","author_link":"https:\/\/dr7.ai\/blog\/author\/andychen\/"},"uagb_comment_info":0,"uagb_excerpt":"Disclaimer: The content on this website is for informational and educational purposes only and is intended to help readers understand AI technologies used in healthcare settings. It does not provide medical advice, diagnosis, treatment, or clinical guidance. Any medical decisions must be made by qualified healthcare professionals. AI models, tools, or workflows described here are&hellip;","_links":{"self":[{"href":"https:\/\/dr7.ai\/blog\/wp-json\/wp\/v2\/posts\/2835","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dr7.ai\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dr7.ai\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dr7.ai\/blog\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/dr7.ai\/blog\/wp-json\/wp\/v2\/comments?post=2835"}],"version-history":[{"count":1,"href":"https:\/\/dr7.ai\/blog\/wp-json\/wp\/v2\/posts\/2835\/revisions"}],"predecessor-version":[{"id":2845,"href":"https:\/\/dr7.ai\/blog\/wp-json\/wp\/v2\/posts\/2835\/revisions\/2845"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dr7.ai\/blog\/wp-json\/wp\/v2\/media\/2838"}],"wp:attachment":[{"href":"https:\/\/dr7.ai\/blog\/wp-json\/wp\/v2\/media?parent=2835"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dr7.ai\/blog\/wp-json\/wp\/v2\/categories?post=2835"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dr7.ai\/blog\/wp-json\/wp\/v2\/tags?post=2835"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}