The primary focus of these initiatives is on health care providers, helping them develop treatment approaches that are most effective for individual patients. One consortium of hospitals, researchers, and a startup, for example, is conducting "Project Survival" to identify effective biomarkers for pancreatic cancer.3 In other firms, real-world data sources are being used to identify molecules that might be particularly effective (or ineffective) in clinical trials. Another long-term challenge to be addressed by the life sciences and health care industry is collaboration and integration of data. Project Survival, for example--an effort to find a pancreatic cancer biomarker--involves collaboration among a big data drug development startup (Berg Health), an academic medical center (Beth Israel Deaconess in Boston), a nonprofit (Cancer Research and Biostatistics), and a network of oncology clinicians and researchers (the Pancreatic Research Team).
Machines would look at data, understand, reason over it, and they continue to learn: understand, reason and learn, not program, in my simple definition. There would be two big differences between business and consumer AI. It leads me to the second big difference between consumer and business AI. That gives you a long, long, long answer, but this is why I'm so positive this world will have more really tough problems solved with AI.
The treatment plan that helped Krista Jones beat a rare form of cancer was developed using machine learning algorithms and big data. Today's most commonly-used surgical robot, the da Vinci system, is operated by a human surgeon through a console. By eliminating the risk of human error, Kim argues that autonomous surgical robots could dramatically decrease the risk of medical complications. But not everyone is convinced that existing surgical robots, including the popular da Vinci system, have proven their worth -- including Marty Makary, a surgical oncologist at Johns Hopkins.
Remove training samples from the overrepresented classes so that the number of training samples for all classes is the same. Therefore, we should look for a model that achieves a good tradeoff between specificity and sensitivity. If you build a photo-based skin cancer detection app, then a high sensitivity is probably more important than a high specificity, since you want to cause people who might have cancer to get themselves checked by a doctor. Now suppose that our desired tradeoff between sensitivity and specificity is given by a number $t \in [0, 1]$ where $t 1$ means that we only pay attention to sensitivity, $t 0$ means we only pay attention to specificity and $t 0.5$ means that we regard both to be equally important.
IBM's supercomputer Watson has been applying machine learning to personalized cancer treatment for some time. After pouring over 600,000 medical reports and 1.5 million anonymized patient records and clinical trials, the data should help define the clearest path forward for doctors. Google's DeepMind is learning how to better apply radiotherapy to cancer patients. To transform this process, DeepMind will analyze 700 anonymized scans from former patients suffering from head and neck cancers.
Researchers at Makerere University in Kampala, Uganda, have teamed up with plant disease experts to develop an automated system aimed at combating cassava diseases. "Some of these diseases are really hard to recognise and require different action," explains Ernest Mwebaze, a computer technology researcher leading the project. Cancer causes more than 8.8 million deaths worldwide and 14 million people are diagnosed with some form of cancer every year. DeepMind has teamed up with UK National Health Service doctors at University College London Hospitals to train its AI help plan treatments for cancer by identifying areas of healthy tissue from tumours in head and neck scans.
Doctors are beginning to use AI to assess the state of a patient's health, even how long they'll live, by the state of their organs and other data points. As it becomes more integrated into health care systems, AI technology could collect and consolidate data on patients from the moment they're born, allowing physicians to get ahead of diseases that often present in middle to older age. For now, Carroll is doing well -- she's been cancer-free for a year -- but her continued health will remain in an unsettling gray area for as long as the majority of her body's data points remain unexplored. The bigger question is whether AI will replace physicians, though Dudley believes that no matter how much AI will reign superior in diagnosing health problems, we should rest assured that nothing -- no matter how powerful -- will replace human contact.
A new study, in which IBM Watson took just 10 minutes to analyze a brain-cancer patient's genome and suggest a treatment plan, demonstrates the potential of artificially intelligent medicine to improve patient care. Both the NYGC clinicians and Watson identified mutations in genes that weren't checked in the panel test but which nonetheless suggested potentially beneficial drugs and clinical trials. Both Watson and the expert team received the patient's genome information and identified genes that showed mutations, went through the medical literature to see if those mutations had figured in other cancer cases, looked for reports of successful treatment with drugs, and checked for clinical trials that the patient might be eligible for. IBM's Parida notes that the cost of sequencing an entire genome has plummeted in recent years, opening up the possibility that whole-genome sequencing will soon be a routine part of cancer care.
A novel imaging approach using artificial intelligence was associated with improved detection of parameters associated with melanoma, according to results presented at the International Conference on Image Analysis and Recognition. The current study employed "machine-learning" software, which records abstract quantitative features on images and can model physiological traits of the patients, according to study background. "Artificial intelligence [AI] can be a key ingredient in the battle against not just melanoma, but also skin cancer globally," he said. This will allow them "to not only more readily diagnose and treat skin cancer, such as melanoma, more accurately and consistently, but also do it at a much earlier stage by enabling general practitioners and nurse practitioners ... to better screen for skin cancer before they reach specialized dermatologists and biopsies, thus reducing health care costs as well as wait times to diagnosis," he said.
Sophia Genetics, a big data analytics company that's using artificial intelligence (AI) to help medical professionals diagnose and treat patients through genomic analysis, has raised $30 million in a round of funding led by Balderton Capital, with participation from 360 Capital Partners, Invoke Capital, and Alychlo. Its platform learns from thousands of patients' genomic profiles to improve and expedite patient diagnosis across oncology, hereditary cancer, metabolic disorders, pediatrics, and cardiology. Elsewhere, cancer screening firm Guardant Health recently raised $360 million to further develop technology that helps patients avoid risky and expensive biopsies, using genomic tests that pair cancer patients with targeted therapies and clinical trials. "Sophia Genetics is a company at the forefront of two rapidly changing technologies: genomic medicine and artificial intelligence," added Balderton Capital partner James Wise.