Description
AI plays an important role in dose optimization and adverse medication event prediction, providing major improvements to patient safety and treatment outcomes. Healthcare providers can use AI algorithms to adjust prescription dosages for specific patients and forecast potential adverse drug events, lowering risks and enhancing patient care.
Summary Listen
- AI is increasingly integrated into healthcare for enhanced diagnostics, personalized treatment plans, and improved efficiency. Common applications include predictive analytics, clinical decision support, AI chatbots, and image-based diagnosis. Several Indian companies are pioneering AI in healthcare, such as Niramai for early breast cancer detection, Cure AI for analyzing chest x-rays, and Mfine for telemedicine through AI-powered chatbots.
- Personalized care, tailoring treatments to each patient's unique profile, leads to higher efficacy, fewer adverse effects, and increased patient satisfaction. AI tools like Hugging Face summarize complex patient histories, saving doctors time. AI analyzes diverse data types like medical history, genetic profiles, lifestyle factors, and real-time monitoring of lab values to create holistic treatment plans.
- Machine learning identifies patterns and makes predictions, computer vision interprets medical images, and natural language processing extracts details from clinical notes. Generative AI creates synthetic data to enhance model training, especially for rare cases. Niramai detects early-stage breast cancer using thermal imaging and AI with high sensitivity and specificity.
- IBM Watson for Oncology tailors chemotherapy plans using patient genetic data. Mayo Clinic's enhanced ECG detects asymptomatic heart diseases early. Viero uses smart glucometers for real-time blood sugar monitoring and personalized coaching. FDA-cleared AI tools like IAD for breast cancer and Viz.ai for stroke are gaining prominence.
- AI in surgical workflow is divided into pre-surgical planning, intraoperative assistance, and postoperative monitoring. Davinci surgical system, an AI-assisted robotic surgery platform, guides surgeons with precision. Visual force feedback helps surgeons assess tissue tension during surgery, while surgical intelligence platforms analyze intraoperative video and data for real-time feedback.
- Postoperative monitoring includes devices like Mimosa Pro for assessing tissue oxygenation and Wound Vision Scout for monitoring wound healing. AI algorithms reconstruct patient images based on CT and MRI scans to create 3D models. VR goggles enhance surgical planning and patient engagement by visualizing the patient's anatomy.
- AI models, such as the breast cancer prediction model created using machine learning and EHR data, aids clinicians in early diagnosis and personalized treatment decisions. AI cannot replace healthcare professionals but serves as a partner. The primacy of a doctor's expertise is paramount, with the doctor's experience and intuition being critical.
- Practical steps to incorporate AI into clinical workflows include identifying use cases, conducting pilot testing, integrating AI into hospital systems, ensuring data privacy and compliance, and monitoring and refining AI applications. Data privacy and security are of utmost importance, requiring robust encryption, data anonymization, and adherence to healthcare regulations. Algorithmic bias can be mitigated by using diverse datasets and conducting bias audits.
- Explainable AI helps clinicians understand how AI models reach conclusions, addressing the "black box" problem. Emerging trends include AI-enhanced gene editing and AI in imaging for precision diagnostics. The future of AI in healthcare involves a holistic approach to patient care, personalized treatments, predictive care, and bridging inequalities in healthcare access.
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About the Speakers
Dr. Ajit Audipudi
Healthcare AI Engineer, General Surgeon, AI Innovator, Hyderabad
Financial Disclosure
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