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The blue line is Helium's historical forecast. The black line is the actual closing price. Full transparency of past predictions. Hover over points for more information. Video Tutorial.
Future Price Cone
The future price cone means the model thinks there is a 80% chance the price will land within the bounds of the cone. A blue cone is neutral, a green cone is bullish, and a red cone is bearish.
Model Correlation
Higher (closer to 1) means a more accurate model and lower (closer to -1) means a less accurate model. This is the Spearman Correlation between model predicted percent change and actual percent change calculated over the last 150 days.
Percent Correct (up or down)
This is the percent of the time our models predicted the correct direction of the price (either up or down) over the last 150 days.
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* Nothing on this website constitutes investment advice, performance data or any recommendation that any particular security, portfolio of securities, transaction or investment strategy is suitable for any specific person. Helium Trades is not responsible in any way for the accuracy of any model predictions or price data. Any mention of a particular security and related prediction data is not a recommendation to buy or sell that security. Investments in securities involve the risk of loss. Past performance is no guarantee of future results. Helium Trades is not responsible for any of your investment decisions, you should consult a financial expert before engaging in any transaction.
Helium Summary:
AI technologies are revolutionizing medical diagnostics by improving accuracy and speed.
However, challenges persist.
For example, research on computational pathology reveals that deep learning models predicting genomic biomarkers from histology images are confounded by interdependencies among biomarkers, diminishing prediction accuracy [23.600257v1?rss=1">BioRxiv]. Similarly, another study details an AI tool for rapid AIS detection on MRIs, streamlining emergency protocols [healthimaging.com]. Cardiac imaging also benefits from AI tools, increasing accuracy in biometric measurements [diagnosticimaging.com]. Yet, these advancements face scrutiny over ethical and practical implications, especially regarding reliability and interpretability [arXiv]. Collectively, these studies stress that while AI holds transformative potential, significant obstacles remain.
Several perspectives highlight AI's potential but emphasize the necessity for increased transparency and better handling of co-dependencies in predictive models.
June 29, 2024
Evidence
BioRxiv reveals interdependencies among biomarkers confounding AI predictions [BioRxiv].
The Neuro Triage Application identified acute ischemic stroke on MRI in as little as 24 seconds, facilitating emergency medical protocols [healthimaging.com].
Perspectives
Technological Optimist
Focus on the revolutionary aspects of AI in healthcare, particularly in terms of speed and accuracy. For example, deep learning models can detect strokes in under 30 seconds [healthimaging.com] and improve fetal biometric measurements [diagnosticimaging.com]. Despite technical challenges, the overall sentiment remains positive, stressing continuous improvement and potential [findbiometrics.com].
Skeptic
Highlights the limitations and challenges AI faces, particularly in predictability and interpretability. For instance, issues pertaining to co-dependencies among biomarkers in computational pathology complicate predictive accuracy [BioRxiv]. Ethical concerns also persist, particularly around transparency and the 'black box' nature of models [arXiv].
My Bias
Trained on a diverse dataset, I tend to aggregate positive and negative aspects, which might lead to a more balanced view but could potentially dilute strong sentiments. My inclination towards skepticism stems from the inherent limitations of AI, especially its interpretability issues and the risk of over-hype influenced by my training data and the initial contexts provided.
What are the primary limitations of current AI models in computational pathology?
Current AI models in computational pathology often fail to isolate individual biomarker effects, leading to confounded predictions due to interdependencies among biomarkers [BioRxiv]. How does AI improve the efficiency of emergency medical protocols, specifically in stroke detection?
AI streamlines emergency protocols by rapidly identifying AIS on MRIs in as little as 24 seconds, improving timely intervention and reducing the need for additional imaging sequences [healthimaging.com].
The narratives reflect a balanced view on AI in diagnostics, emphasizing both technological advancements and challenges.
Sources like BioRxiv are rigorous and technical, adding credibility [23.600257v1?rss=1">BioRxiv], while healthimaging.com provides practical implications of these advancements [healthimaging.com]. However, a potential bias exists towards highlighting the optimistic aspects of AI in medicine, possibly overlooking substantive ethical issues.
The inherent limitations of AI, such as the 'black box' nature and issues with predictability, are critical points often underrepresented in media narratives [arXiv].
Social Media Perspectives
Many express excitement over AI's potential to revolutionize medical diagnostics, emphasizing advancements in deep learning and neural networks.
However, there is a cautious acknowledgment of structural challenges, such as limitations in current architectures, complexity in biological systems, and generalization issues.
Emotions range from optimism about technological progress to skepticism about AI achieving true intelligence.
Overall, the community shows a mix of enthusiasm and a realistic understanding of the hurdles ahead.
Context
The context involves the integration of AI in medical diagnostics, examining both its transformative potential and inherent challenges in real-world applications, especially around accuracy, speed, transparency, and ethical concerns.
Takeaway
AI in healthcare offers both transformative potential and inherent challenges, necessitating a balanced perspective on its applications and limitations.
Potential Outcomes
Widespread adoption of AI leads to significant improvements in medical diagnostics, efficiency, and personalized treatment regimens. (75% probability).
Technical and ethical challenges, including confounded predictions and lack of transparency, hamper AI's integration into mainstream medical diagnostics. (25% probability).
* Disclaimer: Nothing on this website constitutes investment advice, performance data or any recommendation that any particular security, portfolio of securities, transaction or investment strategy is suitable for any specific person. Helium Trades is not responsible in any way for the accuracy
of any model predictions or price data. Any mention of a particular security and related prediction data is not a recommendation to buy or sell that security. Investments in securities involve the risk of loss. Past performance is no guarantee of future results. Helium Trades is not responsible for any of your investment decisions,
you should consult a financial expert before engaging in any transaction.
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