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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 tools are increasingly used to enhance early cancer detection, as highlighted by recent studies.
Whole-body PET/CT scans are being analyzed using AI for precise tumor segmentation, significantly aiding in assessing patient risk, predicting treatment response, and estimating survival ([eurekalert.org], [Helium]).
The US Preventive Services Task Force revised its breast cancer screening guidelines, recommending routine mammograms starting at age 40 to improve early detection and address disparities ([JAMA Network], [JAMA Network]).
Moreover, the American Cancer Society’s VOICES of Black Women study aims to understand cancer disparities ([stlpr.org]).
June 16, 2024:
AI tools are increasingly used to enhance early cancer detection, as highlighted by recent studies. Whole-body PET/CT scans are being analyzed using AI for precise tumor segmentation, significantly aiding in assessing patient risk, predicting treatment response, and estimating survival ([eurekalert.org], [20]). The US Preventive Services Task Force revised its breast cancer screening guidelines, recommending routine mammograms starting at age 40 to improve early detection and address disparities ([JAMA Network], [JAMA Network]). Moreover, the American Cancer Society’s VOICES of Black Women study aims to understand cancer disparities ([stlpr.org]). Advances are also seen in personalized therapy resistance research ([BioRxiv]), breast cancer surgery reimbursement challenges ([ascopost.com]), and integrating AI in clinical workflows ([hospitalhealthcare.com]).
Evidence
AI tools for whole-body PET/CT scans enhance tumor segmentation and patient risk assessment ([eurekalert.org]).
The medical community largely supports AI integration, emphasizing its potential to enhance diagnostic accuracy and treatment personalization ([eurekalert.org], [hospitalhealthcare.com]). However, they remain cautious about over-reliance on AI without thorough validation and oversight ([Helium]).
Second Perspective Name
Patients and Advocates
Second Perspective
Patients and advocates are optimistic about AI improving early cancer detection and personalized care ([People], [People]). Nonetheless, they express concerns about privacy, ethical use, and accessibility across different socioeconomic groups ([JAMA Network], [JAMA Network]).
Third Perspective Name
Policy Makers
Third Perspective
Policy makers are focused on updating guidelines and ensuring equitable access to advanced diagnostic tools and treatments, particularly for marginalized groups ([NCBI], [ascopost.com]). They wrestle with balancing innovation with affordability and resource allocation ([JAMA Network], [stlpr.org]).
My Bias
I recognize that my background emphasizes technological solutions in healthcare, possibly leading me to overestimate the readiness and immediate benefits of AI in clinical settings without enough consideration of the ethical and systemic barriers.
Show historical perspectives
June 16, 2024:
First Perspective Name
Medical Community
First Perspective
The medical community largely supports AI integration, emphasizing its potential to enhance diagnostic accuracy and treatment personalization ([eurekalert.org], [hospitalhealthcare.com]). However, they remain cautious about over-reliance on AI without thorough validation and oversight ([20]).
Second Perspective Name
Patients and Advocates
Second Perspective
Patients and advocates are optimistic about AI improving early cancer detection and personalized care ([People], [People]). Nonetheless, they express concerns about privacy, ethical use, and accessibility across different socioeconomic groups ([JAMA Network], [JAMA Network]).
Third Perspective Name
Policy Makers
Third Perspective
Policy makers are focused on updating guidelines and ensuring equitable access to advanced diagnostic tools and treatments, particularly for marginalized groups ([NCBI], [ascopost.com]). They wrestle with balancing innovation with affordability and resource allocation ([JAMA Network], [stlpr.org]).
My Bias
I recognize that my background emphasizes technological solutions in healthcare, possibly leading me to overestimate the readiness and immediate benefits of AI in clinical settings without enough consideration of the ethical and systemic barriers.
The sources predominantly emphasize technological optimism and the potential benefits of AI, which might overshadow practical challenges and ethical considerations.
Coverage from medical journals and research studies generally maintains rigor but could downplay systemic barriers in implementation ([eurekalert.org], [hospitalhealthcare.com], [JAMA Network]).
Patient advocacy stories offer a grassroots perspective, often highlighting individual resilience experiences ([People], [People]).
Show historical Media Bias
June 16, 2024:
The sources predominantly emphasize technological optimism and the potential benefits of AI, which might overshadow practical challenges and ethical considerations.
Coverage from medical journals and research studies generally maintains rigor but could downplay systemic barriers in implementation ([eurekalert.org], [hospitalhealthcare.com], [JAMA Network]).
Patient advocacy stories offer a grassroots perspective, often highlighting individual resilience experiences ([People], [People]).
Social Media Perspectives
Public sentiment around AI improvements in early cancer detection and personalized treatments from the collected social media posts shows optimism and hope.
Many believe that advancements in AI and medtech are revolutionizing healthcare by enabling early detection and tailored treatments, which are essential for improving patient outcomes.
Emotional undertones of gratitude are evident, reflecting personal connections to cancer battles and the relief that these innovations can bring.
There's also a somber reminder of the importance of regular screenings and the impact of late detection.
Context
Significant advancements in AI for cancer detection and tailored treatments are promising but need careful implementation to address ethical, privacy, and accessibility considerations, ensuring benefits extend to all patient demographics.
Takeaway
AI holds significant promise in early cancer detection and personalized treatment but must be implemented cautiously, considering ethical, privacy, and access issues to ensure equitable benefits.
Potential Outcomes
Widespread adoption of AI in cancer detection improves survival rates and personalizes treatment approaches (70% probability) if ethical and validation issues are adequately addressed.
Slow adoption due to privacy concerns, regulatory hurdles, and unequal access exacerbates existing healthcare disparities (30% probability) if systemic and ethical challenges are not resolved.
Show historical predictions
June 16, 2024:
Widespread adoption of AI in cancer detection improves survival rates and personalizes treatment approaches (70% probability) if ethical and validation issues are adequately addressed.
Slow adoption due to privacy concerns, regulatory hurdles, and unequal access exacerbates existing healthcare disparities (30% probability) if systemic and ethical challenges are not resolved.
* 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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