Assess Your AI in Medicine Expertise in 5 Minutes

Clustering groups data points based on similarity without labels, while classification and regression require labeled data.

Reinforcement learning differs from supervised and unsupervised learning where it involves an agent interacting with an environment via a set of actions and learning based on feedback or reward.  Unsupervised learning solely relies on understanding the data distribution and does not depend on any reward or feedback.

Structured data is organized in fixed fields like tables or spreadsheets, such as lab results; unstructured data includes free text like clinical notes, voice recordings or images.

AI in patient management primarily helps by analyzing patient data to predict outcomes such as the likelihood of hospital readmission. This enables healthcare providers to intervene proactively, prepare personalized treatment plans, and improve recovery. This results in better patient outcomes and reduction of avoidable readmissions. AI also supports continuous patient monitoring and personalized treatment adherence, but its key benefit in patient management is improving prediction and management of patient recovery and readmission risks.

Reducing patient monitoring is incorrect because AI often enhances monitoring through continuous data analysis rather than reducing it.

Increasing interactions with patients is partially true as AI chatbots can support engagement, but this is a secondary benefit, not the key one.

Increasing paperwork is incorrect; AI aims to reduce administrative burdens, not increase them

Reference: IBM blog

Healthcare data comes from diverse sources and formats, making standardization critical but challenging.

Data abundance is an opportunity, not a challenge.

Missing data issue is a derivative of data standardization issue as different source collect different types of data leading to sparse datasets when aggregated together. For example, for LRRK2 Parkinson Disease cohort, data was collected from 10 hospitals or medical centers. Some centers did not collect genomic data, while others did not collect imaging data.

Data provenance captures the history and origin of a data point, tracking the changes made over course of time. In healthcare, changes made to patient data is heavily regulated and usually, comes from a single medical center without intermediaries. This is an important issue which is currently solved.

Screening focuses on early detection and monitoring, often using wearables and chatbots.

Niramai Health Analytix is a medical startup based in Bangalore to develop AI-based radiological imaging solutions for breast cancer to facilitate early detection. Read more about it in this blog.

Haptik is a medical startup based in Bangalore, India that develops HIPAA-compliant chatbots for healthcare use-cases. Read more about it in this blog.

Prognosis relates to predicting disease outcomes and survival, not initial detection.

Treatment involves interventions like surgery or drug delivery after diagnosis.

Hospital resource management focuses on administrative tasks such as pharmacy, insurance, and fraud detection, not disease detection.

LangChain is an orchestration framework that works in the backend to process user input, retrieve data from an external knowledge base, construct prompts and interact with the LLM.

Streamlit manages the frontend where it creates the user-facing web interface and controls the user interactions with the elements like text boxes, buttons.

Reference: Streamlit tutorial

Dimensionality reduction is a technique used in machine learning to transform a dataset with many features (dimensions) into a dataset with fewer features while preserving the essential properties of the original data. Learn more at this blog.

It is typically used for data visualization. For example, you will find tSNE plots in representation learning papers where they show meaning clustering of learnt embeddings in terms of one or two distinctive features like gender, article subjects/topics.

The NITI Aayog (National Institution for Transforming India) is the premier policy think tank of the Government of India. In 2018, it released the "National Strategy for Artificial Intelligence," which outlined a vision for using AI to drive economic growth and social inclusion, with healthcare being one of the five designated sectors for application.

Why others are wrong:

NASSCOM is a trade association for the Indian IT industry.

C-DAC is an R&D organization focused on advanced computing.

ICMR is the apex body for biomedical research. While all are involved in technology and health, NITI Aayog is responsible for the national strategy paper.

Reference: NITI Aayog. (2018). "National Strategy for Artificial Intelligence."

The Ayushman Bharat Digital Mission (ABDM) aims to create a national digital health ecosystem. A core component is the creation of Ayushman Bharat Health Accounts (ABHA) and the standardization of electronic health records (EHRs). This provides a foundational layer of structured, interoperable health data, which is essential for training and deploying effective and large-scale AI models in the Indian context.

Reference: Official website of ABDM

AI for personalized medicine helps to achieve treatment response optimization and improve drug response prediction. It takes into consideration the unique characteristics of each patient at molecular, physiological, ecological and behavioral levels. It is quite the opposite of the one-size-fits-all approach, i.e., relying solely on clinical guidelines after determining the disease stage.

Reference: Journal paper