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AIHC 2025 Recollections-1

 


AIHC 2025 Recap

The future of healthcare is no longer just digital—it's intelligent.

My biggest takeaway fromthe AIHC 2025 Conference at NIT Calicut is how AI is moving from research novelty to critical clinical tool in nearly every specialty. We are seeing real-world, patient-facing integration.

Here are the 5 major shifts highlighted by the Keynote Lectures and Oral Presentations:

✅ Precision & Prevention: AI is moving beyond diagnostics to personalized prediction, like risk scoring for obesity and predicting cancer recurrence by analyzing complex image data (radiomics) .

✅ AI for Trust (XAI): A major focus is on building explainable deep learning models (XAI) that show doctors why a brain tumor was classified a certain way, building critical trust in the technology .

✅ Surgical & Interventional Robotics: AI and robotics are fundamentally rewiring fields like Interventional Radiology and Plastic Surgery, enabling faster, more precise, and minimally invasive procedures.

✅ Microsystems & Diagnostics: Innovations like microfluidic "lab-on-a-chip" technology and new AI-guided screening devices (e.g., for Cervical Cancer) are making diagnostics rapid and highly accessible.

✅ New Interfaces: Research is actively turning non-verbal communication into actionable data, such as using EEG-based Brain-Computer Interfaces (BCI) to recognize the needs of critical care patients.

Thank you to the brilliant speakers, including Dr. Ashwini N S, Prof. Vivek Kanhangad, Dr. Jassim Koya, and many more for an inspiring final day!

Call to Action

Which of these AI integration points do you believe will have the greatest impact on patient outcomes in the next 5 years? Share your thoughts below! 👇

#AIinHealthcare #DigitalHealth #MedTech #ArtificialIntelligence #HealthcareInnovation #AIHC2025 #FutureOfMedicine


Keynote Lectures (S.No. 1 - 26)

  1. Dr. Ashwini N S: Integrating AI into Hepatopathology: Current landscape and future directions

    • Explanation: AI tools are being developed to automatically diagnose liver diseases from tissue slides.

  2. Prof. Vivek Kanhangad: AI-Powered Assisted Navigation for the Visually Impaired Using Vision–Language Models

    • Explanation: AI uses language to guide visually impaired users through environments using real-time spatial awareness.

  3. Dr. Ashok Srinivasan: Linking Simulations and Data To Mitigate Infection Risk in Crowds

    • Explanation: Modeling human movement and data helps predict and prevent the spread of infectious diseases in crowded spaces.

  4. Prof. Dr. Biju Pottekkatt: Artificial intelligence and liver imaging

    • Explanation: AI algorithms analyze medical scans (MRI, CT) to detect and characterize liver lesions and tumors.

  5. Prof. Sanjib Senapati: Mechanistic Insights and Fragment-Based Evolutionary Design: Next-Generation CETP Inhibitors for Cardiovascular Therapeutics

    • Explanation: Computational design is used to discover new drug molecules that regulate cholesterol to treat heart disease.

  6. Dr. S. Harikrishnan: AI in Cardiovascular Disease

    • Explanation: Machine learning models are deployed to predict heart attack risk, analyze ECGs, and optimize cardiac care.

  7. Dr. John Thomas: AI-Driven Biomarkers to Personalized Phenotype-Based Healthcare

    • Explanation: AI finds unique biological patterns in patient data to customize treatments, exemplified by epilepsy surgery outcomes.

  8. Prof. Narayanan C. Krishnan: Trustworthy Deep Learning for Medical Image Analysis

    • Explanation: Research focusing on building transparent AI models that can express their certainty or uncertainty when making medical diagnoses.

  9. Dr Anup SS: Transformational Healthcare: AI Powered Diagnostics and Precision Care Across Specialties

    • Explanation: Overview of how AI is driving comprehensive diagnostic support and personalized medicine throughout healthcare.

  10. Dr. Jose Joseph: Shrinking the Lab: MicroTAS Technology as a Paradigm Shift to Conventional Analysis

    • Explanation: Discussion on microfluidic "lab-on-a-chip" devices that miniaturize and accelerate complex medical diagnostics.

  11. Dr. Anil Prahladan: Radiology & AI

    • Explanation: The role of AI in assisting radiologists by automating image analysis, prioritizing critical cases, and improving workflow.

  12. Dr. Vishnu Prasad: Transforming Urology Through Artificial Intelligence

    • Explanation: Applications of AI in optimizing surgical planning, predicting post-operative recovery, and aiding in urological disease diagnosis.

  13. Dr. Sunu Lazar Cyriac: Changing landscape of clinical trials in AI Era

    • Explanation: How AI is streamlining trial processes, from patient recruitment to data analysis, to accelerate drug development.

  14. Dr. Manu K Aryan: ML & HealthCare - III

    • Explanation: A specific research talk on ongoing machine learning applications relevant to healthcare.

  15. Prof. Poonam Goyal: Multimodal learning systems & their use in healthcare

    • Explanation: AI systems that combine multiple data sources—such as images, electronic health records (EHRs), and sensor data—for holistic patient assessment.

  16. Prof. Aditya Bhaskara: TBD

    • Explanation: The specific topic for this keynote lecture has not yet been announced.

  17. Mr. Arun Krishna: Intelligent Anaesthesia: The role of AI in perioperative Medicine

    • Explanation: AI systems monitor patient vitals during surgery and assist anesthesiologists in precisely controlling drug administration.

  18. Dr. Hannah Mary Thomas: Perspectives from the first-of its kind Quantitative Imaging and AI Research Lab in an Indian hospital

    • Explanation: Experience and challenges of establishing a research unit focused on extracting measurable data from medical images using AI in a clinical setting.

  19. Dr. Jassim Koya: How AI & Robotics are rewiring Interventional Radiology

    • Explanation: The integration of AI-guided robotics for precise, minimally invasive procedures performed inside the body by interventional radiologists.

  20. Dr Krishnapriya Sudersanan: AI in Peadiatric Neurology

    • Explanation: Using AI and machine learning to analyze EEG data for diagnosing and monitoring neurological conditions, such as seizures, in children.

  21. Dr. Sheeja Rajan T M: When AI Meets Scalpel in Plastic Surgery

    • Explanation: AI algorithms used to aid surgical planning, predict aesthetic outcomes, and guide complex procedures in reconstructive and plastic surgery.

  22. Dr. Dinesh M: Role of AI in augmenting Radiation Oncology practice

    • Explanation: AI automates the critical step of contouring (outlining tumors and organs) on medical scans for faster and safer radiotherapy treatment planning.

  23. Dr. Jayadevan E. R.: Opening the black box: Explainable deep learning framework for brain tumour classification.

    • Explanation: Developing transparent AI models that provide understandable justifications for classifying brain tumors.

  24. Dr. Swathy Shanker: Where Algorithms Meet the Abdomen: AI Innovations in H. pylori

    • Explanation: AI technology applied to analyze endoscopic images or pathology slides for the quick and accurate detection of H. pylori bacteria.

  25. Dr. K. Sunil Kumar: AI in Gastroenterology

    • Explanation: AI models that analyze endoscopy videos and pathology images to detect polyps, inflammation, and early-stage cancers in the gastrointestinal tract.

  26. Dr. Santhosh Kuriakose: Design of a AI-Guided Biomedical Device for the Primary Screening of Cervical Cancer

    • Explanation: Presentation on a new device that uses AI analysis for immediate, low-cost screening and early detection of cervical cancer.

Oral Presentations Day 3 (S.No. 27 - 36)

  1. Prasant Mohanty: An Explainable Counterfactual Machine Learning Framework for Personalized Obesity Risk Prediction

    • Explanation: An AI system that not only predicts obesity risk but suggests precise, actionable lifestyle changes to reduce that risk.

  2. Prasant Mohanty: Bridging Local Interpretability: Analyzing SHAP Importance and Counterfactual Sensitivity for Model Transparency

    • Explanation: Research focused on improving the trustworthiness and understanding of complex AI predictions for end-users (e.g., clinicians).

  3. Saran Nallasamy: Artificial Intelligence Tools for Image Simulation in Rhinoplasty: A Meta-Analysis

    • Explanation: A review of AI software used to simulate and preview the results of nose surgery for patient consultation.

  4. Rahul G Nath: Low-Latency Edge Computing Framework for Human Activity Recognition

    • Explanation: Developing fast, local computing systems that monitor patient movement in real-time to detect falls or critical changes in activity.

  5. Hannah Thomas: Metaheuristic-Driven Machine Learning Pipelines for Radiomics-Based Prediction of Locoregional Recurrence in Head and Neck Cancer

    • Explanation: AI uses complex data extracted from medical images (radiomics) to predict if a head and neck cancer is likely to return locally.

  6. Shahana Muneer: A Study on comparing the accuracy of Conventional Predictor Model versus Artificial Intelligence in predicting difficult intubation in anaesthesia

    • Explanation: Comparing the accuracy of traditional risk scores against new AI models for predicting challenging airway management during anesthesia.

  7. Akshay Kumar K.K: Diagnostic Accuracy of Obstetricians versus Open-Source AI in Predicting Neonatal Acidosis

    • Explanation: A comparative study assessing whether publicly available AI tools can accurately predict high-acidity blood in newborns as well as obstetricians can.

  8. Dr.Bhuvaneswari Arunachalan: Assistive Clinical Communications in Healthcare Environment Using Intelligent Agents Systems

    • Explanation: Software agents designed to help healthcare staff communicate efficiently, accurately, and quickly, especially in high-stress situations.

  9. Binu PJ: EEG-Based BCI Decision Support System for Non-Verbal Patient Needs Recognition in Critical Care

    • Explanation: Using brain signals (EEG) combined with deep learning to enable critically ill patients who cannot speak to communicate their basic needs.

  10. Keethimala Aduvala: Parkinson's Disease Detection Using Hybrid CNN-BiGRU Model

    • Explanation: A new diagnostic model that combines two types of neural networks (CNN and BiGRU) to analyze patient data for the early detection of Parkinson's disease.

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