Thanks to Jen-Hsun best decade AI collection- 7000 cases improving peoples communal computation, data & brains - year 74 of Neumann & Economist briefings- : last 100 days of AI we LOVE OR FEAR
Fear Media Fake AI Love AIearth2 AI & Green Ag AIDoubt General AI exists

 AHealthI - all 3 of most massive engineering connectors love health ai ( Jensen Huang, Demis Hassabis, Fei-Fei Li); biggest leap yet is billion man years work which Hassabis AI 250 million proteins database Alphafold2 Jewel in Crown of King Charles AI summit Cambridge/London/Mountain View _ some in silico links Hassabis-UK - Isomorrhic Labs; Huang Recursion/Siemens Healthineers; genentech; Valley AI women Daphne Koller & Priscilla Chan CZI, .. Melinda Gates, Nobel Jennifer Doudna - more at & 1

Friday, June 30, 2023

alpha4 last mile health ai paulo freire, abed, chen, grant, paul farmer, jim kim, soros, gates, guterres, lila ibrahim

Intelligence geared to human development in rural areas (without electricity grids and so without telephone grids) was a challenge right up to the 1990s for about half of humans including the majority of the two thirds living in asia, and majority in africa.

Crucial was bringing up life expectancy to the 60s or above instead of the 40s. Families lose a whole generation until that progress is made, and consequentially education is truncated particularly for girls; and cultures in many rural areas have historically expccted village girls lives spent on breeding huge families

Whilst the 1990s start to see villages leapfrog to solar and mobile, before that the greatest leaps in human development of tae poorest came where solutions were found to maximising rural communities health capacities on basics - typically those that dont kill infants or mothers elsewhere but in humid or isolated villages kill these vulnerable populations 

in the late 1960s the chinese barefoot medical system led the way but it was also able to rely on transferring trained people from cities to villages

the most extraordinary solutions came in villages where once illiterate village mothers were trained to provide basic services- as far as our research shows all scaled examples of this type refer to the education practice of paulo freire

certainly the essential beginnings of bangladesh women empowerment depend on this culture as ngo founders abed and yunus have clarified and martha chen family as first employee of brac and subsequent careers in global public health clarify;  bangladesh did not have enough trained doctors to serve the villages- 2 miracles innovated by fazle abed - were mass training of village mothers in provision of oral rehydration, and designing a village business around village mothers door dashing about 10 basic medicines- brac effectively became the nation's largest wholesaler of these medics; as brac evolved, nearly 100000 village mothers made a living by doordashing basic meds, and over time one mother per 300 families also became most trusted connector to additional health services

UNICEF James Grant saw Oral Rehydration Therapy as infant health's greatest leap forward; unicef funded nationwide training of village mothers of ORT and then nationwide child innoculations- both systems of action education abed blt inspired by Paulo Freire; paul farmer and jim kim founded partners in health while students at harvard; one of their rural solution needs was treating tuberculosisis; abed head desgnied to most effective village process for doing this; in the early 2000s gates and soros foundations together with the global fund united around these bottom up health networks; it is clear that until a nation has a last mile health system economic growth is not practical - in other words health societies may generate strong economies but not vice versa

ai can offer huge sdg and good opportunities; but we need to celebrate ai for sdgs particularly on last mile health, (human energy ie food/water), in turn this depends on ai education and ai empowerment of womens and everyone's safety 

while we know of 2001 on as a very sad time due to 9/11, the connections made between the rural practitioners abed , chens, farmer kim grant/ and leaders gates, soros and other were critical to human development; fortunately guterres was by now participating in this having progressed his career from PM of Portugal to portuguese red cross (then un refugees) before 2017 onwards UN leadership

today guterres is the most central massive partner in how much ai can be prioritised for sdgs and if enough isnt will the broken system legacy from the 20th c extinguish us all; please note the question any responsible journalist needs to prioritise now is what ai solutions can turn sdg crises round

back in the 1880s stanford was founded when the gov of california lost their only child - teen to water based disease caught while family was on a tour of Rome. From that moment on - the stanford's declared responsibility fir dev of every califoirnian chikd with stanfird university the platform they created to do do that. Go stafod HAI Alpha go in ever possible SDG way with Guterres

we've put lila ibrahim's story as transfer from eg silicon valley and coursera to deep mind up here;  moreover attending geneva's july aifirgoodsummit , 3 big thinks:

lila ibrahim keynote with new head of itu

itu, who, wipo announcing they are taking world health ai to a higher level than other itu sdg focus grouos

gutterres announcement of new hi level ai advisory around him

arguably lila ibrahim's connection with top engineers of human and generative ai are second among women hi-tech only to dei-fei li - yes i realise there are big donors including melinda gaets and priscilla chen but since 2010 no i duo has transformed more than hassabis and fei-fei and effectively lila is demis hassabis own guide to the valley -soo also this interview -put another way ibrahim ability to draw in disciplines that can be changes by ai may be similar to role etchemedy plays with fei-fei li


we asked bard july 2023 for top nomination of ai and health data

  • AI in Healthcare: A Roadmap for the Future by the National Academies of Sciences, Engineering, and Medicine.

This report provides a comprehensive overview of the potential of AI to improve healthcare. It discusses the many ways that AI is being used in healthcare today, and it identifies the challenges that need to be addressed in order to realize the full potential of AI in healthcare.

The report also provides a roadmap for the future of AI in healthcare, identifying the key areas where AI is likely to have the greatest impact. These areas include:

  • Personalized medicine: AI can be used to develop personalized treatment plans for patients by taking into account their individual genetic makeup, medical history, and other factors.
  • Early detection of disease: AI can be used to develop early detection methods for diseases, such as cancer, that can lead to earlier diagnosis and treatment.
  • Virtual care: AI can be used to provide virtual care to patients, such as through telehealth consultations.
  • Robotics: AI can be used to develop robots that can perform surgery or other medical procedures.
  • Drug discovery: AI can be used to accelerate the discovery of new drugs.

The report concludes by calling for a coordinated effort to accelerate the development and adoption of AI in healthcare. It identifies the need for increased funding for AI research, the development of ethical guidelines for AI in healthcare, and the training of healthcare professionals in AI.

I believe that this report is an essential resource for anyone who wants to learn more about the potential of AI to improve healthcare.

(in searh of bard citation, at a nontech level these sources worth a 3 thumbs up imo, Data Science, and BigData Approaches to Accelerate/Expand Research and Evaluation nov 2022 interesting reminder how much changed being published before deepmind protein database and nlp innovation lit review covid

whilst not on health , may be relevant to see may 2023 whitehouse update

here are 3 partly random cases which hopefully will convince anyone still in doubt that the llms are the gateway to web3 we need if media is to celebrate sustainability- augmented reality as main metaverse platform to web3 look to be suffering from monty pythos dead parrit syndrome

the 3 sources I see as top of AI and health data:

  1. EMR recording via LLMs

This source explores the potential of large language models (LLMs) to improve the efficiency of electronic medical record (EMR) data entry. LLMs are a type of artificial intelligence (AI) that can be trained on large amounts of text data to generate text that is similar to human-written text. This makes them well-suited for tasks such as summarizing clinical notes or generating patient discharge summaries.

The source discusses how LLMs could be used to automate the process of EMR data entry, which could free up physicians and other healthcare professionals to focus on more patient-facing tasks. The source also discusses the challenges of using LLMs for EMR data entry, such as the need for large amounts of training data and the potential for bias in the data.

  1. ChatGPT in PV

This source explores the potential of ChatGPT, a large language model developed by OpenAI, to improve the diagnosis and treatment of patients with pneumonia. ChatGPT is a chatbot that can be used to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.

The source discusses how ChatGPT could be used to help patients with pneumonia communicate with their healthcare providers, answer questions about their condition, and track their progress. The source also discusses how ChatGPT could be used to help healthcare providers diagnose and treat pneumonia more effectively.

  1. T2DM prediction models

This source reviews 40 papers that explore the use of artificial intelligence (AI) to predict type 2 diabetes mellitus (T2DM). The source finds that AI models can be used to predict T2DM with a high degree of accuracy, especially when they are trained on large datasets of clinical data.

The source also discusses the challenges of using AI to predict T2DM, such as the need for large datasets of clinical data and the need to develop AI models that can account for the complex factors that contribute to T2DM

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