TECHNOLOGY

ARTIFICIAL INTELLIGENCE: MEDICINE

INTRODUCTION

Technology is in the process of completely transforming all aspects of 4 industries – construction, medicine, retail, and transport – with a significant reduction in the need for human labor. Here is the impact on Medicine, where, within 10-20 years (or less), it is reasonable to expect that:

  • Most diseases will be eradicated (although some new ones may develop).
  • We will be wearing sensors that constantly monitor our health, and immediately start a remedial process when something is wrong (from ordering medications, to booking an appointment with a medical specialist, or dispatching an ambulance).
  • Medication dosage and anesthetics will be customized for our personal situation (weight, age, medical history, genetic makeup, etc.)
  • Medication will be delivered to the most effective part of our bodies at the required frequency and dosage, using automatic dispensers, attached externally, or internally using nanobots.
  • All medical testing (bloodwork, imaging, etc.) will be automated, with analyses confirmed (at least for a while) by specialists who may reside remotely.
  • All surgery will be minimally invasive or replaced by the use of nanobots. Some surgery will be performed remotely (as the Da Vinci Surgical System has been doing since 2000).
  • Defective organs will be replaced by transplants built from our own stem cells.
  • Limbs and eyes will be replaced by brain-controlled prosthetics that operate more effectively than human versions. (The $6 Million Dollar Man and the Bionic Woman may no longer be fiction!)
  • Living to an age of 130-150 years will be normal with a good quality of life.

The technologies, which will make this vision a reality, include AI, Robotics, Nanotechnology, and Biotechnology (gene editing via CRISPR-Cas9 with its enormous potential and considerable dangers).

The impact of these technologies on the medical profession is considerable. As with all professions, lower-level functions will be replaced by automation, but so will many specialists. There is already a suggestion that medical schools stop training radiologists (see below) as the ability of AI routines to analyze medical images is starting to match that of specialists, and will soon exceed it. As the above vision starts to be implemented, the need for general practitioners will reduce. (From as far back as 1979, studies have shown that people are more honest in responding to computer terminals – or robots – than to nurses or GPs.)

The jobs in the medical profession that will likely last longer are those requiring direct patient contact. So psychiatrists and psychologists will be around for the foreseeable future, and may be more in demand as society learns to cope with a world without paid work. (On the other hand, a client of Nick’s was developing a computer-based program to provide cognitive behavioral therapy about 15 years ago.). While the need for nurses and administrators in doctors’ offices will disappear, nurses will still be needed to provide hospital and community patient care.

LEARN FROM THESE YOUTUBE VIDEOS

... AND LEARN MORE FROM THESE ADDITIONAL VIDEOS

Frontiers of AI in Medical Imaging for Clinical Decision Making (AI – 2018-02 – Stanford)

A perfect storm is coming (AI – 2018-02 – Doc AI)

Doctor in a box – Telehealth access (AI – 2018-01 – ZDNet)

 

REVIEW THESE INFORMATIVE ARTICLES - AND READ THOSE THAT INTEREST YOU

AI and the NHS: How AI will change everything for patients and doctors (AI/Dermatology - 2018-11 - ZDNet)

The UK government’s vision for AI use in the NHS involves transforming the prevention, early diagnosis and treatment of chronic diseases by 2030. AI could become the first point of contact for the sick, could help healthcare professionals to diagnose medical conditions, and monitor individuals’ health by analysing data from their wearable devices or smart-home sensors.

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Healthcare: 5 digital trends for 2019 and beyond (AI/Dermatology - 2018-11 - Technology.org)

Five progressive digital trends for healthcare in 2019 and beyond: 1. Artificial Intelligence (Medical diagnosis, Pharmaceutical product development, Workflow optimization); 2. Big Data & Analytics; 3. The Internet of Medical Things; 4. Telemedicine; 5. VR/AR (Emergency response, Prevention and diagnostics, Surgery, Education, Rehabilitation and emotional recovery).

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Stanford researchers create algorithm to interpret chest x-rays (AI/Dermatology - 2018-11 - Technology.org)

A new AI algorithm can reliably screen chest X-rays for 14 different pathologies: For 10, the algorithm performed just as well as radiologists; for 3, it underperformed them; and for 1, it outdid them. In all cases, the algorithm took <1% time. Show full article

Google AI claims 99% accuracy in metastatic breast cancer detection (AI/Dermatology - 2018-10 - VentureBeat)

Of the 500,000 deaths worldwide caused by breast cancer, an estimated 90% are the result of metastasis. Researchers at the Naval Medical Center San Diego and Google AI have developed algorithms that autonomously evaluate lymph node biopsies, achieving 99% detection accuracy (compared with human pathologists, who may miss small metastases as much as 62% when under time constraints).

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A new wave of chatbots are replacing physicians and providing frontline medical advice (AI/Dermatology - 2018-10 - MIT Technology Review)

AI chatbot apps, such as Babylon Health, a London-based digital-first health-care provider that is working within the National Health Service, are designed to reduce unnecessary visits to general practitioners, while providing immediate medical advice. Babylon’s AI scored 81% on a version of the final exam of the UK Royal College of General Practitioners (UK), 9% higher than the average grade achieved by UK medical students.

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AI algorithm used to adjust treatment dosages for metastatic cancer (AI/Dermatology - 2018-10 - Technology.org)

National University of Singapore researchers used the CURATE.platform to deliver optimal doses of medication and halt the progression of a patient’s advanced prostate cancer.

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Machine learning outperforms clinicians in predicting outcomes for people at risk of psychosis and depression (AI/Dermatology - 2018-09 - Technology.org)

An Australian research study used a cmbination of machine learning algorithms to accurately predict social outcomes one year later in up to 83% of patients at high risk of psychosis and 70% of patients with recent-onset depression.

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New AI system detects hard-to-spot cancerous lesions (AI/Dermatology - 2018-08 - Technology.org)

A team of engineering and medicine researchers at the University of Central Florida has recently developed a new AI system to spot often-missed cancerous tumours on computerised tomography scans. It was 95% successful (compared with 65% for radiologists).

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Big data and Deep Learning used to predict the fate of inpatients (AI/Dermatology - 2018-08 - ZDNet)

Researchers from Google Brain and Stanford University are using big data and deep-learning methods to predict the fate of inpatients, including death; readmissions to measure quality of care; a patient’s length of stay to measure of resource utilization; and a prediction of a patient’s diagnoses to see how well clinicians understood a patient’s problems.

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AI Neural network matches human cardiologists in detecting heart attacks (AI/Dermatology - 2018-07 - MIT Technology Review)

German researchers at the Fraunhofer Heinrich Hertz Institute (Berlin) and the University Medical Center Schleswig-Holstein (Kiel) have developed a neural network that can spot the signs of myocardial infarction, matching the performance of human cardiologists for the first time.

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AI is better than dermatologists at diagnosing skin cancer (AI/Dermatology - 2018-05 - ScienceBlog)

Researchers in Germany, the USA, and France trained a deep learning convolutional neural network to identify skin cancer by showing it more than 100,000 images of malignant melanomas, as well as benign moles. Its performance was better than that of 58 international dermatologists.

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Diagnostic imaging computers outperform human counterparts (AI/Diagnosis - 2018-04 - Case Western Daily)

‘Deep learning’ computers in Case Western Reserve university’s diagnostic imaging lab routinely defeat their human counterparts in detecting various cancers and predicting their strength. Case studies:
• Diagnosing heart failure: 97% accuracy c.f. 74% for two pathologists.
• Distinguishing benign from malignant lung nodules on CAT scans: 5-8% superior to two human experts.
• Prostate cancer scans: computational imaging algorithms detected cancer in an MRI scan in >70% of cases where radiologists missed and correctly detected no cancer in 50% of cases where radiologists reported cancer.

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AI is quicker and more effective than humans in analyzing heart scans (AI/Imaging - 2018-03 - Technology.org)

UC San Francisco research showed that advanced machine learning can classify essential views from heart ultrasound tests faster, more accurately, and with less data than board-certified echocardiographers.180,000 real-world echocardiogram images were used to train a computer to assess the most common echocardiogram views. Both the computer and skilled human technicians were tested on new samples. The computers accurately assessed images 91.7-97.8% of the time, versus 70.2-83.5% for the technicians.

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AI can diagnose prostate cancer as well as a pathologist (AI/Pathology - 2018-03 - Science Business)

Confirmation of a prostate cancer diagnosis normally requires a biopsy sample to be examined by a pathologist. Chinese researchers have developed an AI system with similar levels of accuracy to pathologists, while accurately classifying the level of malignancy of cancer, eliminating the variability which can creep into human diagnoses.

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AI diagnoses eye diseases within 30 seconds with 95% accuracy (AI/Opthamology - 2018-02 - Technology.org)

Researchers at UC San Diego, with colleagues in China, Germany, and Texas, have developed a new computational tool to screen patients with possible macular degeneration and diabetic macular edema. Machine-derived diagnoses were compared with diagnoses from 5 ophthalmologists who reviewed the same scans. With simple training, the machine performed similar to the ophthalmologists, generating a decision on whether or not the patient should be referred for treatment within 30 seconds, with more than 95 percent accuracy.

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AI shown reliable in recognizing and classifying 3 major eye diseases (AI/Opthamology - 2018-01 - Futurism)

A recent study from researchers at the Singapore National Eye Center showed that deep learning software, built to recognize and classify retinal images, was reliable in recognizing diabetic retinopathy, glaucoma, and age-related macular degeneration. This can potentially reduce 80 percent of the workload of graders and optometrists, freeing up their time for treatment.

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A prominent AI researcher suggested that advances in AI mean that medical schools “should stop training radiologists now.” (AI/Radiology - 2018-01 - MIT Technology Review)

Stanford researchers trained a convolutional neural network to detect abnormalities (like fractures, or bone degeneration) better than radiologists in finger and wrist radiographs. (However, radiologists were still better at spotting issues in elbows, forearms, hands, upper arms, and shoulders.) Geoffrey Hinton, a prominent AI researcher, told the New Yorker that advances in AI mean that medical schools “should stop training radiologists now.”

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