AI products in Healthcare, Hard fitment

 

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Prediction - 🔍 Anticipating AI's Big Shift in 2025: OpenAI’s Focus on Domain-Centric Solutions

Prediction:

OpenAI is set to shift towards domain-centric solutions, making 2025 a transformative year for AI. This transition is based on the data collected and learned from APIs serving different domains, focusing on context window improvements, reasoning patterns, and cross-modal integration. This will significantly enhance decision-making in critical sectors like FinTech and healthcare. By tackling technical challenges and integrating user feedback, these advancements will result in more powerful, tailored AI applications that will reshape entire industries.

Expanding Beyond Language Models

Today, OpenAI is primarily recognized as a leading provider of large language models, but its true capabilities extend much further. Its question-answering abilities, for instance, are exceptionally powerful and evolving rapidly. As clients integrate this technology into critical sectors like FinTech and healthcare, they will unlock new levels of context window improvements, and cross-modal integration and reasoning by adopting techniques like tree of thought, chain of thought, and graph-based approaches, enabling AI to think and deduce more effectively. Feedback from users will be pivotal in this journey, guiding organizations on how best to structure information flows and assess when to fine-tune models, use Retrieval-Augmented Generation (RAG), or determine the optimal use of short-term and long-term memory. This constant feedback loop will allow AI to achieve unprecedented levels of contextual understanding and adaptive reasoning, creating models that align more closely with complex real-world needs. 

"OpenAI's journey is no longer just about language—it's about thought and contextual adaptation."

Building Resilient and Adaptive Systems

These advancements will likely lead to the development of more resilient and adaptable systems. Future systems will not only enhance decision-making but also push reasoning capabilities into new territories, setting the stage for increasingly sophisticated agents and refined RAG architectures. These improved architectures are expected to reduce hallucinations, boost accuracy, and lead to products that are more responsive to real-world challenges. Overcoming issues like catastrophic forgetting, hallucinations, and knowledge manipulation will be critical, positioning these systems as robust, reliable solutions across industries. 

"Resilient, adaptive AI systems will transform decision-making and redefine industry standards."

Addressing Technical Challenges

Currently, accuracy challenges remain in areas such as domain-relevant embedding, balancing retrieval techniques against accuracy and latency, chunking methods based on usage or query types, contextualization, and routing or re-ranking processes. Yet, these elements are essential for advancing the capabilities of AI models. Despite these ambiguities, ongoing data processing and analysis are paving the way for more focused, domain-specific AI products. Within the next six to eight months, we’re likely to see a new wave of AI-driven applications, from highly specialized agents to RAG applications and APIs crafted for specific industries.

 "Technical hurdles are simply steps toward the next wave of AI-driven, domain-specific innovation."

The Transformative Potential of 2025

The year 2025 is set to be a pivotal moment in AI, marking the dawn of domain-centric solutions that will reshape how AI interacts with our world. As more industry-specific applications emerge, OpenAI’s technologies will bring powerful, tailored solutions closer to reality. 

"2025: The year AI becomes truly domain-centric, reshaping industries with precision, customized models, and highly accurate agents and RAG systems."

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#AI #OpenAI #DomainSpecificAI #Innovation #MachineLearning #FinTech #Healthcare #FutureOfAI

Healthcare and AI

Text extracted with anthiago

Talk #1

What's Next For AI In Healthcare In 2023? - The Medical Futurist

Extract key summary as oneliners. Each line with a max length upto 15 words. Summary in below format

1.

2.

3.

• The FDA has approved many AI healthcare applications.
• The FDA is developing a database for AI-based healthcare tech.
• AI poses a regulatory challenge as it learns and evolves.
• Radiologists now have guidelines to utilize AI Technologies.
• Regulatory bodies are working on governing AI's large language models.
• Misuse of AI could spread misinformation or breach patient privacy.
• Despite challenges, AI's benefits to healthcare far outweigh the difficulties.
• Companies are integrating AI services like GPT into their business.
• AI is predicted to breakthrough in drug discovery this year.
• AI Solutions usage will increase in established areas like oncology.
• AI will start emerging in new fields like mental health.
• AI will lead to increased efficiency and adoption in healthcare.
• AI will help in revealing solutions that were overlooked before. 

Talk #2

Medical Uses of ChatGPT - The Medical Futurist

• GPT is used widely for academic and coding purposes.
• GPT, the latter version, is being studied for medical usage.
• GPT can help in generating and analyzing clinical notes. 
• It can also answer general health-related queries.
• GPT can pass the U.S. medical exam without specialized training.
• It can help doctors and nurses create accurate clinical notes.
• Medical chatbot, a potential use case, can address a doctor shortage.
• GPT's current limitation includes not citing references and sources.
• GPT's utilization in healthcare implies users being fact-checkers.
• Google's DeepMind has released Medpalm to answer healthcare queries.
• As AI improves, risk of lack of healthcare personnel threatens.
• GPT and AI tech will profoundly change the future of healthcare.

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AI in Healthcare

• Toilets that monitor your vitals - Withings, 

            Ref - Link

• Selfie that monitors your face/changes - i-Virtual

• Self Screening cancer detection - Dotplot

• Medical Monitor Wearables - Aidmed, Relay

• Hearing Aid - JLab

• AI-Enabled Drug Discovery - Chroma

• AI Chatbot - Botco
• Robots Nanobots 
• X-Ray Vision - Orcana
• Stressless experiences - Calmwave

Ref - Link

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Health Care - Age of GPT

Two posts' key perspectives

Alexa to Doctor Evolution

• The year is 2070. You walk into an urgent care clinic feeling unwell, and an Alexa device asks you to describe your symptoms. 
• The computer takes down your information, retrieves details from your past electronic health records, and suggests diagnostic tests for a human technician to perform. 
• After getting the test results, the software program prescribes a medication to treat your condition.

Key Lines

• “Doctors will not be replaced by AI, but they may not directly profit from it either,”
• “You can’t outperform a physician based on reams and reams of data if you don’t have lots and lots of patients on which to train the computer,”
• "in a few decades, patients will be comfortable interacting with computers and even trust them as their main source of medical guidance"

Interesting Read - Link

BioGPT (Link)

Here is a summary of what you need to know about #biogpt.

• It is trained on domain-specific data taken from PubMed, a database of biomedical articles
• It is trained on 15 million pieces of content
• The data used is updated before 2021
• It is trained for relation-extraction, content generation, question-answering and document classification
• It is a form of #evolved ai that outperforms other generally-trained GPT-2 models such as Flan-PaLM.

It may be sooner than earlier - a software program prescribes a medication to treat your condition.

Ref - Link

• Technology will continue to change the world – we should all make sure that it changes it for the better
• We all should strive to gain the knowledge we need to contribute to an intelligent debate about the world we want to live in
• To a large part, this means gaining knowledge, and wisdom, on the question of which technologies we want.

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Study - Wearables - Impact

 

Ref - Link

• We’ll monitor physiological parameters, such as heart rate, blood oxygen level, steps and calories using a smartwatch
• We’ll monitor the food intake using the Cronometer App.
• Environmental exposures, including both chemical and biological exposures, are monitored using a phone-sized device
• Metabolites and immune markers in the blood are monitored as well.

"We believe consumer health technologies, apps, wearables, and self-diagnosis tools have the potential to strengthen the patient-physician connection and improve health outcomes."
~Dr. Glen Stream, Chairman, Family Medicine for America’s Health#wearables #healthtech pic.twitter.com/6a86LfIqMX

— Nextwear Technologies (@Nextweartech) August 22, 2022

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The challenges to put ML models in production (Healthcare)

 Very good thread, Summarizing insights

Can you detect COVID-19 using Machine Learning? 🤔

You have an X-ray or CT scan and the task is to detect if the patient has COVID-19 or not. Sounds doable, right?

None of the 415 ML papers published on the subject in 2020 was usable. Not a single one!

Let's see why 👇 pic.twitter.com/Vrd91ZpXy3

— Vladimir Haltakov (@haltakov) June 9, 2021

Observations from papers ?

• None of the 415 ML papers published on the subject in 2020 was usable. Not a single one!
• Black small square 2212 papers, Black small square 415 after initial screening, Black small square 62 chosen for detailed analysis, Black small square 0 with potential for clinical use
• Many papers were using very small datasets often collected from a single hospital - not enough for real evaluation
• Some papers used a dataset that contained non-COVID images from children and COVID images from adults. These methods probably learned to distinguish children from adults
• Training and testing on the same data 
• Many papers failed to disclose the amount of data they were tested or important aspects of how their models work leading to poor reproducibility and biased results
• Many papers didn't even consult with radiologists.
• Rushing to publish results based on small and bad quality datasets undermines the credibility of ML
• At some point people start figuring out how to fine tune on the test set
• Dataset is not diverse enough and bias-free
• Authors find that covid-19 detectors often attend to the position of the shoulders and not the lungs. Models can easily learn shortcuts as opposed to robust features

Take everything with a pinch of salt. Real world data is not kaggle data. Kaggle does not reflect the reality or quality or the challenges we spot on data.

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MIT 6.S191: AI in Healthcare

Key Notes

AI in healthcare 

Maturation of DL / GPU / Open source labeled public datasets

• Need in healthcare
• Automation
• Avoid human error

Lung cancer signs detection from images

Tissue architecture

Differentiate types of tumors

Breast cancer detection from pathology images

Genome sequencing

Healthcare is fragmented, distributed

Get multiple opinions / collate results

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