AI News, Artificial intelligence for global health
Artificial intelligence in healthcare
Artificial intelligence (AI) in healthcare is the use of complex algorithms and software to emulate human cognition in the analysis of complicated medical data.
What distinguishes AI technology from traditional technologies in health care is the ability to gain information, process it and give a well-defined output to the end-user.
AI algorithms behave differently from humans in two ways: (1) algorithms are literal: if you set a goal, the algorithm can't adjust itself and only understand what it has been told explicitly, (2) and algorithms are black boxes;
AI programs have been developed and applied to practices such as diagnosis processes, treatment protocol development, drug development, personalized medicine, and patient monitoring and care.
to support operational initiatives that increase cost saving, improve patient satisfaction, and satisfy their staffing and workforce needs.
that help healthcare managers improve business operations through increasing utilization, decreasing patient boarding, reducing length of stay and optimizing staffing levels.
During this time, there was a recognition by researchers and developers that AI systems in healthcare must be designed to accommodate the absence of perfect data and build on the expertise of physicians.
The ability to interpret imaging results with radiology may aid clinicians in detecting a minute change in an image that a clinician might accidentally miss.
A study at Stanford created an algorithm that could detect pneumonia at that specific site, in those patients involved, with a better average F1 metric (a statistical metric based on accuracy and recall), than the radiologists involved in that trial.
The emergence of AI technology in radiology is perceived as a threat by some specialists, as the technology can achieve improvements in certain statistical metrics in isolated cases, as opposed to specialists.
Recent advances have suggested the use of AI to describe and evaluate the outcome of maxillo-facial surgery or the assessment of cleft palate therapy in regard to facial attractiveness or age appearance.
In 2018, a paper published in the journal Annals of Oncology mentioned that skin cancer could be detected more accurately by an artificial intelligence system (which used a deep learning convolutional neural network) than by dermatologists.
On average, the human dermatologists accurately detected 86.6% of skin cancers from the images, compared to 95% for the CNN machine.
One study conducted by the Centerstone research institute found that predictive modeling of EHR data has achieved 70–72% accuracy in predicting individualized treatment response at baseline.
To address the difficulty of tracking all known or suspected drug-drug interactions, machine learning algorithms have been created to extract information on interacting drugs and their possible effects from medical literature.
Efforts were consolidated in 2013 in the DDIExtraction Challenge, in which a team of researchers at Carlos III University assembled a corpus of literature on drug-drug interactions to form a standardized test for such algorithms.
Other algorithms identify drug-drug interactions from patterns in user-generated content, especially electronic health records and/or adverse event reports.
The subsequent motive of large based health companies merging with other health companies, allow for greater health data accessibility.
A second project with the NHS involves analysis of medical images collected from NHS patients to develop computer vision algorithms to detect cancerous tissues.
Intel's venture capital arm Intel Capital recently invested in startup Lumiata which uses AI to identify at-risk patients and develop care options.
team associated with the University of Arizona and backed by BPU Holdings began collaborating on a practical tool to monitor anxiety and delirium in hospital patients, particularly those with Dementia.
The AI utilized in the new technology – Senior's Virtual Assistant – goes a step beyond and is programmed to simulate and understand human emotions (artificial emotional intelligence).
Doctors working on the project have suggested that in addition to judging emotional states, the application can be used to provide companionship to patients in the form of small talk, soothing music, and even lighting adjustments to control anxiety.
Virtual nursing assistants are predicted to become more common and these will use AI to answer patient's questions and help reduce unnecessary hospital visits.
Overall, as Quan-Haase (2018) says, technology “extends to the accomplishment of societal goals, including higher levels of security, better means of communication over time and space, improved health care, and increased autonomy” (p. 43).
While research on the use of AI in healthcare aims to validate its efficacy in improving patient outcomes before its broader adoption, its use may nonetheless introduce several new types of risk to patients and healthcare providers, such as algorithmic bias, Do not resuscitate implications, and other machine morality issues.
We already have some scientists who know artificial intelligence and machine learning, but we want complementary people who can look forward and see how this technology will evolve.”
As of November 2018, eight use cases are being benchmarked, including assessing breast cancer risk from histopathological imagery, guiding anti-venom selection from snake images, and diagnosing skin lesions.
34 Pharma Companies Using Artificial Intelligence in Drug Discovery
If you read my list of startups using artificial intelligence to drug discovery, you may have wondered: how much traction do these companies actually have?
Here are relevant partnerships, memberships, and investments I'm aware of for Astellas: Here are relevant partnerships, memberships, and investments I'm aware of for AstraZeneca: Here are relevant partnerships, memberships, and investments I'm aware of for BASF: Here are relevant partnerships, memberships, and investments I'm aware of for Bayer: Here are relevant partnerships, memberships, and investments I'm aware of for Boehringer Ingelheim: Here are relevant partnerships, memberships, and investments I'm aware of for Bristol-Myers Squibb (BMS): Bristol-Myers Squibb (BMS) acquired Celgene, so look there for future information.
Here are relevant partnerships, memberships, and investments I'm aware of for GSK: In addition to what I've outlined below, in September 2019, Genentech and parent Roche disclosed a predictive analytics project with a paper in Nature on using deep learning to predict which patients with diabetic retinopathy will progress the fastest.
Here are relevant partnerships, memberships, and investments I'm aware of for Genentech: Here are relevant partnerships, memberships, and investments I'm aware of for Gilead: Here are relevant partnerships, memberships, and investments I'm aware of for Ipsen: Here are relevant partnerships, memberships, and investments I'm aware of for Janssen: Here are relevant partnerships, memberships, and investments I'm aware of for Merck Group: Here are relevant partnerships, memberships, and investments I'm aware of for Mitsubishi Tanabe Pharma: While not a traditional pharmaceutical company, Nestlé has a health science division.
Here are relevant partnerships, memberships, and investments I'm aware of for Roche: Here are relevant partnerships, memberships, and investments I'm aware of for SK Biopharmaceuticals: Here are relevant partnerships, memberships, and investments I'm aware of for Sanofi: Here are relevant partnerships, memberships, and investments I'm aware of for Santen: Here are relevant partnerships, memberships, and investments I'm aware of for Servier: Here are relevant partnerships, memberships, and investments I'm aware of for Sumitomo Dainippon Pharma: Here are relevant partnerships, memberships, and investments I'm aware of for Sunovion: Here are relevant partnerships, memberships, and investments I'm aware of for Takeda: Here are relevant partnerships, memberships, and investments I'm aware of for Wave Life Sciences: Yuhan is a South Korean pharmaceutical and chemical company.
Using Artificial Intelligence to Determine Whether Immunotherapy Is Working
Scientists from the Case Western Reserve University digital imaging lab, already pioneering the use of Artificial Intelligence (AI) to predict whether chemotherapy will be successful, can now determine which lung-cancer patients will benefit from expensive immunotherapy.
And, once again, they’re doing it by teaching a computer to find previously unseen changes in patterns in CT scans taken when the lung cancer is first diagnosed compared to scans taken after the first 2-3 cycles of immunotherapy treatment.
Currently, only about 20% of all cancer patients will actually benefit from immunotherapy, a treatment that differs from chemotherapy in that it uses drugs to help your immune system fight cancer, while chemotherapy uses drugs to directly kill cancer cells, according to the National Cancer Institute.
Having a tool based on the research being done now by his lab would go a long way toward “doing a better job of matching up which patients will respond to immunotherapy instead of throwing $800,000 down the drain,”
Additionally, Madabhushi said, researchers were able show that the patterns on the CT scans which were most associated with a positive response to treatment and with overall patient survival were also later found to be closely associated with the arrangement of immune cells on the original diagnostic biopsies of those patients.
This suggests that those CT scans actually appear to capturing the immune response elicited by the tumors against the invasion of the cancer–and that the ones with the strongest immune response were showing the most significant textural change and most importantly, would best respond to the immunotherapy, he said.
Artificial intelligence and algorithmic bias: implications for health systems
The most tangible form of AI is machine learning, which includes a family of techniques called deep learning that rely on multiple layers of representation of data and are thus able to represent complex relationships between inputs and outputs.
Second, whilst there are encouraging research findings in the use of AI in health care, little of this work has been applied in practice, rigorously evaluated or exposed to peer-reviewed publications, while widely publicised positive findings have been challenged .
We define, for the first time, algorithmic bias in the context of AI and health systems as: “the instances when the application of an algorithm compounds existing inequities in socioeconomic status, race, ethnic background, religion, gender, disability or sexual orientation to amplify them and adversely impact inequities in health systems.”
- On 10. april 2021
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