• Credits
  • Section Writer: Dr. Om J Lakhani
  • Section Editor: Dr. Om J Lakhani

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• • Q. What is the definition of Artificial intelligence ?
    • "a system’s ability to correctly interpret external data and to use those learnings to achieve specific goal and tasks through flexible adaption”
  • Q. Summarize how Artificial intelligence helps doctors and patients ?
    • 
  • Q. What percentage of deaths can be attributed to diagnostic errors ?
    • 10% of deaths
  • Q. Enlist the ethical principles governing the use of AI technology in healthcare ?
    • Autonomy: AI technology should respect the patient's right to make decisions about their own healthcare.
    • Beneficence: AI technology should be used to promote the well-being and health of patients.
    • Non-maleficence: AI technology should not cause harm to patients and should minimize the risk of harm.
    • Justice: AI technology should be used fairly and equitably, without discrimination or bias.
    • Veracity: AI technology should be truthful and accurate in its diagnosis, treatment, and communication of medical information.
    • 
  • Q. What is the Human in the loop model ?
    • The Human in the loop model is an AI system that involves human input to enhance its accuracy.
    • It is a type of AI system that integrates human expertise to improve its performance.
    • This model is commonly used in medical diagnosis and treatment planning.
    • It allows doctors to provide their knowledge and experience to the AI system, which can then make more accurate predictions and recommendations.
    • The Human in the loop model is particularly useful in complex medical cases where multiple factors need to be considered.
    • It can help doctors to make more informed decisions and improve patient outcomes.
    • The model is designed to be a collaborative effort between humans and machines, with each contributing their unique strengths to the process.
  • Q. What is the explainable AI ?
    • Explainable AI is a type of AI that can provide clear and understandable explanations for its decisions and predictions to humans.
    • It is designed to increase transparency and trust in AI systems, particularly in medical applications.
    • In healthcare, explainable AI can help doctors and clinicians understand how an AI system arrived at a particular diagnosis or treatment recommendation.
    • This can improve the accuracy and reliability of AI-assisted medical decision-making, as well as help doctors identify potential biases or errors in the AI system.
    • Explainable AI can also help patients understand the reasoning behind their medical diagnoses and treatment plans, which can improve patient engagement and satisfaction.
  • Q. What is DISHA ?
    • Digital Information Security in Healthcare Act (DISHA) Bill
  • Q. What are the salient features of the DISHA bill with regards to AI ?
    • The DISHA bill establishes a National Artificial Intelligence Portal, which will serve as a centralized platform for the development and deployment of AI-based solutions in healthcare.
    • The bill promotes the development of AI-based solutions for various sectors, including healthcare, with a focus on improving patient outcomes and reducing healthcare costs.
    • The bill encourages the use of AI in medical research and development, with a view to accelerating the discovery of new treatments and therapies for various diseases.
    • The bill emphasizes the need for ethical and responsible use of AI in healthcare, with a focus on ensuring patient privacy and data security.
    • The bill also calls for the establishment of a regulatory framework for the use of AI in healthcare, with a view to ensuring that AI-based solutions are safe, effective, and reliable.
  • Q. What is "data bias" with regards to AI ?
    • Data bias in AI is a potential threat to data-driven technologies such as AI for health.
    • Data bias occurs when the data used to train and test the AI is skewed and not sufficiently large, which can lead to errors, discrimination, and other issues.
    • To mitigate data bias, researchers must ensure data quality by ensuring that the training data is free from known biases and represents a large section of the target population.
    • Data sets used in AI technologies should adequately represent the population in which the technologies are intended to be used.
    • The existence of bias in the data set must be identified and scrutinized to avoid errors and discrimination in AI for health.
  • Q. What frameworks are suggested for validation of AI technology in healthcare ?
    • SPIRIT-AI
    • CONSORT-AI
  • Q. What is CONSORT-AI ?
    • CONSORT-AI is a new consensus statement that sets standards for reporting on artificial intelligence (AI) in clinical trials. It was developed by an international working group and published in Nature Medicine 1. The statement includes 14 new items for researchers to routinely include in their manuscripts when reporting on AI interventions. The idea is to promote transparent reporting of AI interventions and to build on the checklist outlined in the CONSORT 2010 statement, which provides minimum guidelines for reporting randomized trials
  • Q. What is the "right to be forgotten" ?
    • The "right to be forgotten" is a legal concept
    • It allows individuals to request the removal of personal information
    • The information is removed from online databases
    • This concept is related to privacy and data protection
    • It can be relevant in the medical field, where personal health information is often stored in online databases
    • Patients may request the removal of their personal health information from these databases
    • This can be important for protecting patient privacy and confidentiality.
  • Q. What is "Deep learning" ?
    • Deep learning is a type of machine learning that uses algorithms to model high-level abstractions in data.
    • It is a powerful tool for analyzing complex data sets and making predictions.
    • In healthcare, deep learning can be used to analyze medical images, such as X-rays and MRIs, to detect abnormalities and diagnose diseases.
    • It can also be used to analyze electronic health records to identify patterns and predict patient outcomes.

• Important quote:

  • 

• Q. Is the use of Artificial intelligence in Medicine a new thing ?

  • No
  • In 1959 Keeve Broadman wrote and I quote:
    • "the making of correct diagnostic interpretations of symptoms can be a process in all aspects logical and so completely defined that it can be carried out by a machine."

• Q. What is the Moore's law ?

  • Moore's Law: the observation that the number of transistors on a microchip doubles approximately every two years.
  • Predicted by Gordon Moore in 1965
  • He was right and now the size of the storage drives is becoming smaller and smaller

• Q. Give a comparsion of how storage spaces have evolved in computer field ?

  • 

• Q. What are transformers ?

  • Transformers: a type of deep-learning model
  • Introduced in 2017 by Vaswani et al.
  • Key features: self-attention mechanism, parallel processing, and multi-head attention
  • Utilized for natural language processing tasks
  • Can process and understand contextual relationships in text data
  • Enabled significant advancements in machine translation, text generation, and question-answering systems
  • Examples: BERT, GPT-2, GPT-3, and RoBERTa

• Q. Give some early examples of interest in the field of Artificial intelligence in Medicine

  • Interpretation of symptoms with a data-processing machine: In 1959, Brodman et al. used a machine to interpret symptoms for diagnosis (AMA Arch Intern Med 1959; 103: 776-82).
  • Simulation of clinical cognition: Pauker et al. developed a computer program in 1976 to simulate clinical cognition for taking a present illness history (Am J Med 1976; 60: 981-96).
  • Artificial intelligence in medicine in the 1980s: In 1987, Schwartz et al. discussed the state of AI in medicine and its potential applications (N Engl J Med 1987; 316: 685-8).

• Q. Give some examples of present use of Artificial intelligence in Medicine ?

  • Image analysis: AI assists in interpreting medical images, such as radiology scans, pathology slides, and retinal scans.
  • Electronic medical record (EMR) decision support: AI algorithms can analyze patient data to generate alerts, reminders, and recommendations for clinicians.
  • Identification of outbreaks by monitoring internet traffic: AI can track and predict disease outbreaks by analyzing online search trends and social media posts.
  • Tracking cases, outcomes, and relationships to local factors: AI helps in analyzing large-scale health data to identify patterns, correlations, and potential causal links in public health research.
  • Serving as a teacher and assessor in medical education: AI can create realistic simulations for medical training and evaluate student performance in clinical scenarios.
  • Operational organization: AI streamlines tasks, such as operating-room scheduling, billing and collections, and patient follow-up.
  • 

• Q. What are the application of Artificial intelligence in ClinicalTrials ?

  • Decision support in trial design: AI can assist in selecting appropriate study designs, endpoints, and statistical analyses.
  • Patient identification, recruitment, and retainment: AI can help identify potential participants, facilitate outreach, and improve retention in clinical trials.
  • Outcome and side-effect monitoring: AI can analyze large datasets to track clinical trial outcomes and monitor side effects in real-time.
  • Use of multiple information sources about a patient to make a diagnosis: AI can integrate various data sources, such as electronic medical records and genomic data, to aid in precision diagnosis in clinical trials.
  • Internet search engines and electronic medical record (EMR) decision support: AI can assist in retrieving relevant medical information and help in decision-making based on patient data.
  • Creating realistic "flight simulators" for simple and complex patient encounters: AI can simulate clinical scenarios to train investigators in managing patients during clinical trials.
  • Provision of real-time coaching about specific questions to ask in the medical history or physical findings to check: AI can guide investigators on gathering relevant information for the clinical trial, improving the quality of data collected.

• Q. Which was the first medical chatbot to be developed ?

  • ELIZA was the first medical chatbot to be developed back in the 1960s
  • 

• Artificial intelligence in Radiology

• Q. What is continual learning ?

  • Continual learning is a process in which an AI model learns from new data over time while retaining previously acquired knowledge. This approach allows the model to adapt to changes in the data set and improve its performance over time. By continually updating the model with new data, it can maintain its accuracy and reliability in real-world settings.

• Q. An AI model does well in training, but poorly in real world setting. What is this known as ?

  • This is called Data Set shift

• Q. What are foundation models ?

  • Foundation models are like building blocks for creating more advanced AI models. They are trained on lots of data and can be customized for specific tasks, like finding tumors in medical images. By using foundation models, developers can create more accurate and reliable AI models that can perform specific tasks with high precision.

• Q. What are large language models ?

  • Large language models are like super-smart computers that can understand and produce human language. They are trained on huge amounts of data and have billions of little parts that work together to understand and generate language. These models can be used to do things like write medical reports or answer questions about health. They are really powerful tools that can help us do things faster and more accurately.

• Q. What are the multimodal models ?

  • Multimodal models are AI models that can understand and combine different types of medical data, such as medical images and electronic health records. These models are particularly useful in medicine for tasks that require a comprehensive understanding of the patient, such as diagnosis and individualized treatment planning. In simpler terms, multimodal models are like super-smart computers that can look at different types of medical information, like pictures and patient records, and use that information to help doctors make better decisions about how to treat their patients.

• Q. What is zero shot learning in simple words ?

  • Zero-shot learning is a type of artificial intelligence (AI) that allows a computer to perform a task or solve a problem without being explicitly trained on that specific task or problem. Instead, the computer uses its existing knowledge and understanding to make educated guesses and predictions. This is particularly useful in medicine when there is a shortage of labeled data available for a specific medical task. In simpler terms, zero-shot learning is like a student taking a test on a subject they haven't studied for, but using their general knowledge and reasoning skills to make educated guesses and still do well on the test.

• Q. What are the functions of Radiologic AI algorithms ?

  • Quantification: performing segmentation and measurements of anatomical structures or abnormalities, such as measuring breast density, identifying anatomical structures in the brain, quantitating cardiac flow, and assessing local lung-tissue density.
  • Workflow triage: flagging and communicating suspected positive findings, including intracranial hemorrhage, intracranial large-vessel occlusion, pneumothorax, and pulmonary embolism.
  • Detection, localization, and classification of conditions such as pulmonary nodules and breast abnormalities.
  • Enhancing preinterpretive processes, including image reconstruction, image acquisition, and mitigation of image noise.
  • Predicting clinical outcomes on the basis of CT data in cases of traumatic brain injury and cancer.
  • Providing imaging biomarkers to quickly and objectively assess structures and pathological processes related to body composition, such as bone mineral density, visceral fat, and liver fat, which can be used to screen for various health conditions.
  • Predicting future adverse events when applied to routine CT imaging.
  • Determining coronary-artery calcium scores, which are typically obtained on the basis of CT scanning, by means of cardiac ultrasonography.

• Q. Tell me about Cardiac ultrasound for Coronary artrey calcium scores ?

  • cardiac ultrasound can be used to determine coronary artery calcium scores, which are typically obtained on the basis of CT scanning. This is done by using a specialized ultrasound probe that emits high-frequency sound waves to create images of the heart and its blood vessels.
  • The ultrasound images can be used to identify and measure the amount of calcium deposits in the coronary arteries, which are a marker of atherosclerosis and an increased risk of heart disease. This information can be used to assess a patient's risk of developing cardiovascular disease and to guide treatment decisions.
  • One advantage of using cardiac ultrasound for coronary artery calcium scoring is that it is a non-invasive and radiation-free imaging technique, which can be particularly beneficial for patients who are at increased risk of radiation exposure or who have contraindications to CT scanning.
  • However, it is important to note that cardiac ultrasound may not be as accurate as CT scanning for determining coronary artery calcium scores, particularly in patients with high levels of calcification. Therefore, it is important to consider the individual patient's clinical situation and risk factors when deciding which imaging modality to use.

• Q. What are Swoop portable MRI systems ?

  • Swoop portable MRI systems are point-of-care imaging devices
  • Designed to provide accessibility and maneuverability for clinical applications
  • Address limitations of existing imaging technology
  • Useful in settings where traditional MRI machines are unavailable or impractical
  • Compact and lightweight, easily transported and set up
  • Plugs into standard electrical outlet
  • Controlled by an Apple iPad for easy use and operation
  • Provides high-quality images without sedation or anesthesia
  • Beneficial for pediatric patients and those unable to tolerate traditional MRI
  • Has the potential to revolutionize access to medical imaging
  • May improve patient outcomes
  • Further research needed to evaluate safety, efficacy, and clinical utility
  • 
  • 

• Q. What is "data set shift" ?

  • "Data set shift" is a phenomenon observed in the performance of many radiologic Artificial Intelligence (AI) models.
  • It refers to the decrease in performance of these models when they are applied to patients who differ from those used for model development.
  • This shift can occur due to various factors such as differences in health care systems, patient populations, and clinical practices.
  • Changes in disease prevalence, advances in medical technology, and alterations in clinical practices can also contribute to data set shift.
  • It can significantly affect the sensitivity and false positive rates of AI models, limiting their clinical usefulness and raising concerns about their generalization across different clinical environments.

• Q. What is generalist medical AI model ?

  • This takes in data from multiple inputs- including patient history, clinician input etc and not just the image data
  • This is more effective than just image based AI model
  • 

• Q. What is the concept of Multimodal AI in healthcare ?

  • Multimodal AI in healthcare refers to the development of AI models that incorporate data across various modalities. These modalities can include biosensors, genetic, epigenetic, proteomic, microbiome, metabolomic, imaging, text, clinical, social determinants, and environmental data.
  • The aim of these models is to provide a more comprehensive and continuous view of a patient's health, as opposed to one-off snapshots.
  • These models can enable broad applications in healthcare, including individualized medicine, real-time pandemic surveillance, digital clinical trials, and virtual health coaches.
  • The successful development of multimodal data-enabled applications requires the collection, curation, and harmonization of well-phenotyped and large annotated datasets.
  • The use of multimodal AI in healthcare also presents several challenges, including technical challenges, data-related challenges, and privacy challenges.
  • Despite these challenges, the potential of AI to deliver accurate and personalized health coaching is expected to be embraced by future digital health applications

• Q. What is unlearn.ai ?

  • This is an initiative to reinvent medical care mainly in the concept of creating Digital Twins
  • Unlearn's ambitious plan to revolutionize medicine using artificial intelligence (AI). Unlearn's mission is to advance AI to eliminate trial and error in medicine, with the ultimate goal of turning medicine into a computational science.
  • The plan is broken down into several stages:
    • Clinical Trials: The first step is to build an AI that can create digital twins of patients in clinical trials, which will help design more efficient, ethical, and reliable clinical studies.
    • Comparative Effectiveness Research: The profits from the first stage will be reinvested into R&D to improve the AI, enabling it to simulate the effects of existing treatments. This will power an in silico comparative effectiveness platform.
    • Personalized Medicine: Further investment will be made to improve the AI's predictive accuracy at the individual patient level, allowing it to guide treatment decisions.
    • Scaling: The final step is to scale these solutions until most of medicine can be turned into a computational science.

• Q. What is Digital Twin technology ?

  • Digital twin technology is a concept borrowed from engineering that uses computational models of complex systems, such as cities, airplanes, or patients, to develop and test different strategies or approaches more quickly and economically than in real-life scenarios.
  • In healthcare, digital twins are a promising tool for drug target discovery and for modeling and predicting with high precision how a certain therapeutic intervention would benefit or harm a particular patient.
  • The development of accurate and useful digital twin technology in medicine depends on the ability to collect large and diverse multimodal data, ranging from omics data and physiological sensors to clinical and sociodemographic data.
  • This technology requires large collaborations across health systems, research groups, and industry. An example of such a collaboration is the Swedish Digital Twins Consortium.
  • Companies like Unlearn.AI have developed and tested digital twin models that leverage diverse sets of clinical data to enhance clinical trials for diseases like Alzheimer's and multiple sclerosis.

• Q. Expain digital twin in simple words ?

  • Digital twin technology is a concept where a virtual model of a physical object, process, or system is created. This virtual model, known as a "digital twin," is an exact replica of its physical counterpart in the digital space.
  • Here's a simple way to understand it:
    • Imagine you have a toy car. Now, imagine you create an exact copy of this toy car in a computer game. This digital copy of the toy car in the game is the "digital twin" of the real toy car.
    • In the digital world, you can drive the digital twin car around, crash it, paint it different colors, and do all sorts of things without affecting the real toy car. You can also use it to predict what might happen to the real car if you were to do the same things in real life.
    • In a more complex scenario, digital twins are used in industries like manufacturing, healthcare, and urban planning. For example, a digital twin of a jet engine can help engineers run simulations to predict failures, optimize performance, and understand how the engine will behave under different conditions.
    • In healthcare, a digital twin of a human heart could help doctors test different treatments and predict how a patient's heart might respond, without any risk to the patient.
    • So, in essence, a digital twin is a digital copy that helps us understand, predict, and optimize the physical world without having to experiment on the real thing.

• Q. What are ambient sensors ?

  • Ambient sensors are devices located within the environment, such as a room, a wall, or a mirror. They can range from video cameras and microphones to depth cameras and radio signals.
  • These sensors are designed to collect valuable data about the environment and the activities taking place within it, without requiring direct interaction from the individuals being monitored.
  • In healthcare, ambient sensors can potentially improve remote care systems at home and in healthcare institutions by providing continuous, unobtrusive monitoring of patients' activities and behaviors.
  • The integration of data from ambient sensors with other types of data, such as wearable sensor data and electronic health records, represents a promising opportunity to improve remote patient monitoring.
  • For example, the combination of ambient sensors with wearables data has the potential to improve the reliability of fall detection systems and gait analysis performance, and to enable early detection of impairments in physical functional status.
  • Beyond management of chronic or degenerative disorders, ambient sensors could also be useful in the setting of acute disease, as demonstrated by a recent program conducted by the Mayo Clinic for remote monitoring of people with COVID-19

• Q. Can you use a band-aid to diagnose atrial fibrillation ?

  • Yes
  • The Scripps Translational Science Institute (STSI) has initiated a home-based clinical trial using wearable sensor technology to identify individuals with asymptomatic atrial fibrillation (AFib), a condition that significantly increases the risk of stroke. The study, named mHealth Screening To Prevent Strokes (mSToPS), aims to determine if this method of screening can identify AFib more efficiently than routine care.
  • 

• Q. What is a biobank ?

  • A biobank is a facility or organization that stores and manages biological samples for research purposes.
  • It collects samples like blood, tissue, DNA, and cells from humans and other organisms.
  • The primary goal is to provide researchers with high-quality samples for studies in genetics, genomics, medicine, and disease research.
  • Biobanks operate under ethical and legal guidelines to protect donor rights, privacy, and confidentiality.
  • Samples are stored in secure environments to maintain integrity and viability.
  • Biobanks may also collect accompanying data, such as medical records and genetic profiles.

• Q. What is CLIP ?

  • CLIP, or Contrastive Language Image Pretraining, is an architecture developed by OpenAI.
  • It is trained on millions of image-text pairs.
  • It is basically as image is paired with an associated text- which generates reports in radiology, for example
  • The goal of CLIP is to match the performance of competitive, fully supervised models without the need for fine-tuning.
  • The architecture is inspired by a similar approach in the medical imaging domain known as Contrastive Visual Representation Learning from Text (ConVIRT).
  • In this approach, an image encoder and a text encoder are trained to generate image and text representations by maximizing the similarity of correctly paired image and text examples and minimizing the similarity of incorrectly paired examples, a process known as contrastive learning.
  • This method of paired image-text co-learning has been used to learn from chest X-rays and their associated text reports, outperforming other self-supervised and fully supervised methods.

• Q. What is contrastive learning ?

  • Contrastive learning is a method used in machine learning, which is a type of artificial intelligence.
  • The main idea is to teach a computer model to recognize and differentiate between different types of data.
  • This is done by training the model to make similar representations for data that are supposed to be similar, and different representations for data that are supposed to be different.
  • For example, if we have an image of an apple and the word 'apple', the model should recognize that these two are related and create similar representations for them. But if we have an image of an apple and the word 'banana', the model should recognize that these two are not related and create different representations for them.
  • This method is used in advanced computer models like CLIP and ConVIRT, which can understand both images and text.
  • It has been particularly useful in medical settings, such as understanding chest X-rays and their associated medical reports.

• Q. What are transformers ?

  • Transformers are a type of advanced computer models used for understanding and generating human language.
  • They are especially good at tasks like translation, answering questions, and classifying text.
  • Transformers can understand the meaning of words by looking at the relationships between them in a sentence or sequence.
  • They use a special mechanism called "self-attention" to figure out which words are important and how they relate to each other.
  • Transformers have two parts: an encoder, which understands the input, and a decoder, which generates the output.
  • These models are trained using large amounts of data and mathematical techniques to improve their performance.
  • Transformers have greatly improved our ability to understand and work with human language, and they're used in many applications today.

• Q. What are the different types of transformers ?

  • Notable transformer models include BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pre-trained Transformer), and T5 (Text-To-Text Transfer Transformer).

• Q. What is perceiver and perceiver IO ?

  • Perceiver and Perceiver IO are advanced machine learning models developed by DeepMind, a company under Alphabet.
  • The Perceiver model is designed to handle different types of data like images, sound, and text using a single structure.
  • It converts all input data into a common format called byte arrays.
  • These byte arrays go through an attention bottleneck, which helps the model focus on important information and saves memory.
  • After processing, the Perceiver model uses a final classification layer to determine the likelihood of each possible output.
  • The Perceiver IO model expands on the Perceiver model and can produce a wider range of outputs.
  • It can transform the processed data into outputs like images, sound, and classification labels.
  • The model uses a query vector to specify the task, such as predicting a brain tumor's appearance in medical imaging or the likelihood of successful treatment.
  • Perceiver IO is more versatile in handling different tasks and generating various types of outputs.

• Q. What are CycleGANs ?

  • This is something that can help generate synthetic images for training datasets and radiologists
  • CycleGANs, or Cycle Generative Adversarial Networks, are a type of Generative Adversarial Network (GAN) introduced by Zhu et al. in 2017.
  • They are designed to learn to translate from one domain to another in the absence of paired examples, a process known as unsupervised image-to-image translation.
  • The "cycle" in CycleGAN refers to the cycle consistency loss, a key component of the model that encourages the learned mappings to be cycle-consistent.
    • This means that if an image from domain A is translated to domain B and then translated back to domain A, the final image should be the same as the original image.
  • CycleGANs have been used in a variety of applications, including style transfer, object transfiguration, season transfer, and photo enhancement.
  • In the biomedical field, CycleGANs have been used to generate synthetic medical images, such as contrast and non-contrast CT scans.
    • This approach has shown improvements in tasks such as COVID-19 diagnosis.
  • 

• Q. What is the curse of dimensionality ?

  • The curse of dimensionality is a term used to describe the challenges and problems that arise when dealing with high-dimensional data.
  • As the number of dimensions (or variables or features) in a dataset increases, the volume of the space increases exponentially, making the data become sparse.
    • This sparsity is problematic because most statistical methods assume that all observations are equally representative of the space, which is not the case in high-dimensional spaces.
  • The curse of dimensionality can lead to overfitting, where a model learns the noise in the training data instead of the underlying pattern, resulting in poor performance on unseen data.
  • It can also lead to computational challenges, as the time and resources needed to process high-dimensional data can be substantial.
  • Furthermore, it can result in 'dataset blind spots', where certain combinations of features have no observations, which can negatively impact model performance.

• Q. What is multimodal fusion ?

  • Multimodal fusion is a process used in machine learning to increase prediction performance by combining data from different modalities, rather than simply inputting several modalities separately into a model.

• Q. What is 'Gato' developed by Deep mind from Google ?

  • Gato is a generalist agent developed by DeepMind, a subsidiary of Google.
  • It is designed to perform a wide variety of tasks, demonstrating the potential of artificial general intelligence.
  • The agent is trained using a diverse range of tokens created from text, images, and button presses, among other inputs.

• Q. What is Artificial general intelligence (AGI) ?

  • Artificial General Intelligence (AGI) refers to a type of artificial intelligence that has the ability to understand, learn, adapt, and implement knowledge across a wide range of tasks at a level equal to or beyond human capabilities.
  • Unlike narrow AI, which is designed to perform specific tasks, AGI can transfer learning from one domain to another, demonstrating a form of cognitive flexibility similar to human intelligence.

• Q. Give an example of deep learning and multimodal fusion in cancer pathology ?

  • Deep learning has been extensively applied in cancer pathology to make predictions that go beyond typical pathologist interpretation tasks with Hematoxylin and Eosin (H&E) stains.
  • For instance, deep learning models have been used to predict genotype and gene expression, response to treatment, and survival using only pathology images as inputs.
  • This represents a form of multimodal fusion where the model is trained on different types of data (in this case, pathology images and clinical data) to make more accurate predictions.

• Q. What is reidentification ? What are some of the concerns regarding the same with regards to patient privacy ?

  • Reidentification refers to the process of matching anonymized data with the actual identities of the individuals from whom the data was collected.
  • This process poses significant privacy concerns, particularly in the context of healthcare data, as it can potentially expose sensitive patient information.

• Q. What is Edge computing ? Can it solve the issue of data privacy for patients ?

  • Edge computing refers to the concept of bringing computation and data storage closer to the location where it's needed, to improve response times and save bandwidth.
  • It involves processing data at the "edge" of the network, near the source of the data. This contrasts with traditional cloud computing, where data is sent to centralized servers for processing.
  • In the context of healthcare, edge computing can provide more security by avoiding the transmission of sensitive patient data to centralized servers. This can help to address some aspects of data privacy concerns.
  • For instance, some healthcare systems now run optimized versions of deep learning models directly in their hardware, instead of transferring images to cloud servers for identification of life-threatening conditions.

References:

1. Ethical Guidelines for Application of Artificial Intelligence in Biomedical Research and Healthcare (https://main.icmr.nic.in/content/ethical-guidelines-application-artificial-intelligence-biomedical-research-and-healthcare)
2. Haug CJ, Drazen JM. Artificial intelligence and machine learning in clinical medicine, 2023. New England Journal of Medicine. 2023 Mar 30;388(13):1201-8.
3. Rajpurkar P, Lungren MP. The Current and Future State of AI Interpretation of Medical Images. N Engl J Med. 2023 May 25;388(21):1981-1990. doi: 10.1056/NEJMra2301725. PMID: 37224199.
4. Acosta JN, Falcone GJ, Rajpurkar P, Topol EJ. Multimodal biomedical AI. Nature Medicine. 2022 Sep;28(9):1773-84.
5. Meskó B, Topol EJ. The imperative for regulatory oversight of large language models (or generative AI) in healthcare. npj Digital Medicine. 2023; 6(1). DOI: 10.1038/s41746-023-00873-0.