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PodcastDXAuthor: PodcastDX
PodcastDX is an interview based weekly series. Guests share experience based medical insight for our global audience. We have found that many people are looking for a platform, a way to share their voice and the story that their health journey has created. Each one is unique since even with the same diagnosis, symptoms and the way each person will react to a diagnosis, is different. Sharing what they have experienced and overcome is a powerful way our guests can teach others with similar ailments. Many of our guests are engaging in self-advocacy while navigating a health condition, many are complex and without a road-map to guide them along their journey they have developed their own. Sharing stories may help others avoid delays in diagnosis or treatment or just give hope to others that are listening. Sharing is empowering and has a healing quality of its own. Our podcast provides tips, hints, and support for common healthcare conditions. Our guests and our listeners are just like you- navigating the complex medical world. We hope to ease some tension we all face when confronted with a new diagnosis. We encourage anyone wanting to share their story with our listeners to email us at info@PodcastDX.com Language: en Genres: Alternative Health, Health & Fitness, Medicine Contact email: Get it Feed URL: Get it iTunes ID: Get it |
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Ai in Medicine Tool Partner or Problem
Episode 5
Monday, 19 January, 2026
AI in medicine is best understood as a powerful tool and a conditional partner that can enhance care when tightly supervised by clinicians, but it becomes a problem when used as a replacement, deployed without oversight, or embedded in biased and opaque systems. Whether it functions more as a partner or a problem depends on how health systems design, regulate, and integrate it into real clinical workflows. Where AI Works Well Decision support and diagnosis: AI can read imaging, ECGs, and lab patterns with very high accuracy, helping detect cancers, heart disease, and other conditions earlier and reducing some diagnostic errors. Workflow and documentation: Tools that draft visit notes, summarize records, and route messages can cut administrative burden and free up clinician time for patients. Patient monitoring and triage: Algorithms can watch vital signs or wearable data to flag deterioration, triage symptoms online, and guide patients through care pathways, which is especially valuable with clinician shortages. Risks and Problems Errors, over-reliance, and "automation bias": Studies show clinicians sometimes follow incorrect AI recommendations even when the errors are detectable, which can lead to worse decisions than if AI were not used. Bias and inequity: If training data underrepresent certain groups, AI can systematically misdiagnose or undertreat them, amplifying existing health disparities. Trust, explainability, and liability: Black-box systems can undermine shared decision-making when neither doctor nor patient can understand or challenge a recommendation, and they raise hard questions about who is responsible when harm occurs. Impact on the Doctor–Patient Relationship Potential partner: By handling routine documentation and data crunching, AI can give clinicians more time for conversation, empathy, and shared decisions, supporting more person-centered care. Potential barrier: If AI outputs dominate visits or generate long lists of differential diagnoses directly to patients, it can increase anxiety, fragment communication, and weaken relational trust. How To Keep AI a Partner, Not a Problem Keep humans in the loop: Use AI as a second reader or coach, not a final decision-maker; clinicians should retain authority to accept, modify, or reject suggestions. Demand transparency and evaluation: Health systems should validate tools locally, monitor performance across different populations, and disclose AI use to patients in clear language. Align incentives with patient interests: Regulation, reimbursement, and malpractice rules should reward safe, equitable use of AI—not just speed, volume, or commercial uptake. In practice, AI in medicine becomes a true partner when it augments human judgment, enhances relationships, and improves outcomes; it becomes a problem when it is opaque, biased, or allowed to replace clinical responsibility.
