#ExpertSystem

[drchristophedelongsblog]

The Hybrid Power of Bayesian Networks in Medical Diagnosis

In essence, a pre-built Bayesian Network (BN) for medical diagnosis is a powerful tool that hinges on a sophisticated blend of human expertise and vast patient data. This hybrid approach is crucial for building robust and reliable AI diagnostic systems.

Building the Network's Structure

First, consider the network's structure. This is the fundamental blueprint of the BN, defining the relationships between various medical variables like symptoms, diseases, risk factors, and test results. Think of it as the logical and causal framework of the model.

This structure is primarily constructed by human expertise. Top-tier medical professionals, with their deep understanding of anatomy, physiology, pathology, and clinical reasoning, are indispensable here. They meticulously define which symptoms are linked to which diseases, what risk factors influence specific conditions, and how different tests impact diagnostic probabilities.

While Large Language Models (LLMs) might offer some potential for assistance in the future—perhaps by extracting relevant causal relationships from vast medical literature—the final validation and refinement of this intricate structure will always demand the discerning eye and deep clinical acumen of highly experienced doctors. This ensures the network's logical coherence and clinical relevance.

Populating with Conditional Probabilities

Second, once the structure is in place, the network needs to be populated with conditional probabilities. These are the "numbers" that breathe life into the structure, indicating the likelihood of one variable given the state of another (e.g., the probability of a specific symptom appearing if a certain disease is present).

These probabilities are predominantly derived from large quantities of real patient data. This means utilizing anonymized patient records, extensive epidemiological studies, and statistical insights from clinical trials. These vast datasets allow the BN to learn the actual likelihoods of various conditions and their associated manifestations.

However, human expertise remains crucial here as well. In cases where real data is scarce or nonexistent (ee.g., for rare diseases, novel conditions, or specific patient subgroups), expert medical opinion is used to fill these probabilistic gaps. Physicians can provide educated estimates based on their clinical experience, which can then be refined as more data becomes available.

Why Top-Tier Medical Professionals Are Essential for AI in Medicine

The promising nature of Bayesian Networks in diagnostic assistance lies precisely in this synergy: the explicit knowledge of human experts combines with the machine's capacity to process immense datasets. This powerful combination allows BNs to:

Model uncertainty: They effectively handle the inherent ambiguities in medical diagnosis by expressing probabilities.

Provide explainability: Unlike "black box" AI models, BNs can show the dependencies between variables, allowing clinicians to understand why a particular diagnosis is suggested. This transparency is paramount in medicine.

This leads directly to why AI-powered medicine critically needs top-tier medical professionals:

To build and validate the AI's "medical brain": The core logic, the fundamental relationships, and the initial probabilities of sophisticated diagnostic AI models like Bayesian Networks must be meticulously crafted and continuously validated by clinicians with deep domain expertise. Without this, the AI could make illogical or unsafe inferences.

To interpret and contextualize AI outputs: An AI might suggest a diagnosis, but a highly skilled doctor is needed to weigh that suggestion against the patient's unique history, social context, ethical considerations, and any subtle clinical signs that the AI might miss. They can also identify when the AI might be "hallucinating" or misinterpreting data.

To handle the nuances of human interaction: The empathetic patient interview, the subtle art of palpation, and the overall human connection are skills that AI cannot replicate. Top doctors bring invaluable intuition and judgment to the diagnostic process that goes beyond data points.

To adapt to evolving knowledge and new challenges: Medical science is constantly advancing. Experienced physicians are at the forefront of this evolution, understanding new diseases, treatments, and research. They are essential for updating and refining AI models to ensure they remain current and relevant.

For ultimate responsibility and accountability: Even with advanced AI, the ultimate responsibility for a patient's diagnosis and care rests with the human clinician. Their expert judgment is the final arbiter, especially in complex or ambiguous cases.

In conclusion, while AI like Bayesian Networks offers groundbreaking potential for enhancing diagnostic efficiency and accuracy, it functions best as a powerful co-pilot. It extends the reach and analytical capabilities of medical professionals, but it absolutely does not replace their indispensable expertise, critical thinking, and profound human judgment. The future of medicine is a collaborative one, where the most advanced AI tools empower, rather than sideline, the most skilled human clinicians.

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[saurabhcontentwriter]

The Journey of Artificial Intelligence

The journey of artificial intelligence (AI) is a fascinating and ongoing tale that spans several decades. It all began as a concept in the 1940s when researchers started exploring the idea of creating machines that could simulate human intelligence. Over the years, the field of AI has witnessed significant milestones, breakthroughs, and challenges.

Here's a general overview of the key stages in the journey of AI

The Birth of AI (1950s - 1960s)

The term "artificial intelligence" was coined by John McCarthy in 1956 during the Dartmouth Conference. Early AI research focused on solving symbolic problems and developing logic-based systems.

The AI Winter (1970s - 1980s)

Initial optimism about AI led to high expectations that couldn't be met with the available technology at the time. Funding and interest in AI research declined, leading to a period known as the "AI winter."

Expert Systems and Knowledge-based AI (1980s)

Researchers shifted their focus to expert systems, which used large knowledge bases to emulate human expertise in specific domains. These systems found applications in various fields, but their limitations became apparent as they struggled to handle uncertainty and real-world complexity.

Rise of Machine Learning (1990s - early 2000s)

AI research saw a resurgence with the advent of machine learning algorithms, such as neural networks, support vector machines, and decision trees. These approaches allowed AI systems to learn from data and improve their performance over time.

Big Data and Deep Learning (mid-2000s - present)

The explosion of big data and the availability of powerful computational resources led to significant advancements in deep learning. Deep learning, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), revolutionized fields like computer vision, natural language processing, and speech recognition.

Narrow AI Applications (present)

AI is now prevalent in various industries and applications, including virtual assistants, recommendation systems, autonomous vehicles, and fraud detection. Narrow AI, also known as weak AI, refers to AI systems that are designed for specific tasks and lack general intelligence.

Ethical and Social Challenges

The rapid progress in AI has raised ethical and societal concerns, such as bias in AI systems, job displacement, data privacy, and AI's impact on human decision-making.

Progress Towards AGI

Artificial General Intelligence (AGI) refers to AI systems with the ability to understand, learn, and apply knowledge in a manner comparable to humans. Achieving AGI remains a significant challenge and a subject of ongoing research.

The journey of AI continues with researchers and developers striving to push the boundaries of what AI can achieve while addressing the associated ethical implications. As technology advances, AI is expected to play an increasingly influential role in shaping various aspects of our lives and society

[meowymeowzers]

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[infodump-matchmaking]

ID: tags by @expertsystems​ “PLEASE. [people] making dinosaur games you want to talk to me so badly” end ID.

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@expertsystems

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[zero2046-blog]

Goplus 团队在智能合约与 DeFi 安全领域的综述论文正式被中科院一区 TOP 期刊录用

Comprehensive Review of Smart Contract and DeFi Security: Attack, Vulnerability Detection, and Automated Repair 近日，Goplus团队在智能合约与去中心化金融（DeFi）安全领域的综述论文《Comprehensive Review of Smart Contract and DeFi Security: Attack, Vulnerability Detection, and Automated Repair》正式被中科院一区TOP期刊《Expert Systems With Applications》录用！ 《ExpertSystems With…

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@expertsystems

To be living

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AI Prompt Ace Bundle Review – AI Prompt Ace 2023

Introduction Of AI Prompt Ace Bundle Review

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[techpathway]

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[aroallow]

you fucking get it.

(reblog by @expertsystems)

a little tired of aro rep being about prioritizing friendship. me personally? i would like to be a slut. thanks

[ayeshavohra-eprnews-posts]

SuperAnnotate Announces Strategic Partnership With OpenCV to Improve Annotation for Computer Vision Workflows #ArtificialIntelligence #ExpertSystems #ComputerVision #CVPR #DataLabeling #ImageAnnotation #MachineLearning #ePRNews @OpenCV @SuperAnnotate

https://eprnews.com/superannotate-announces-strategic-partnership-with-opencv-to-improve-annotation-for-computer-vision-workflows-459217/

[carcinocommander]

TAGGED BY @weirdgirlfag YIIPPPEEE

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Currently working on: SO MANY FUCKING THINGS I CAN'T STOP CREATING EVER. I'M STILL TRYING TO DRAW A PFP FOR MY CANNIBALISM/DARKER (?) BLOG, I'M WORKING ON A WEBSITE SO I DON'T HAVE TO DEAL WITH THE LIMITATIONS OF A CARRD, THERE'S MORE STUFF I WANNA DRAW, UGH THERE'S SO MUCH.

Tagging: MY GAWD. I DON'T REMEMBER ALL MY MUTUALS. UUHHH APOLOGIES IF YOU'VE ALREADY BEEN TAGGED, AND YOU DON'T HAVE TO DO THIS SO NO PRESSURE AT ALL. ANYWAYS, @expertsystems @luxtea-07 @infectbait HIII HELLO HELLO

EDIT: @crazy-ai I'M SORRY I DIDN'T INCLUDE YOU IN THE ORIGINAL TAGGING MY BRAIN IS SLOW. ALSO ANY MUTUALS WHO I DIDN'T TAG BUT WANNA DO IT YOU CAN DO IT AND TAH ME AT THE TOP