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.
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By 2030, everything you know about being human will change (2019-06 - Casey Research)
A forecast of technological change in what being human means, including tiny wireless brain implants called neurograins; CRISPR genetic editing; the elimination of death; and the associated ethical questions.
Stanford’s latest AI helps doctors diagnose brain aneurysms more accurately (Brain - 2019-06 - Extreme Tech)
Researchers at Stanford University have created predictive AI to detect the likelihood of aneurysms in three-dimensional brain scans with high accuracy, although this advance will not be available for years. The search for an aneurysm is one of the most labor-intensive and critical tasks radiologists undertake.
Google shows how AI might detect lung cancer faster and more reliably (Radiology - 2019-05 - MIT Technology Review)
Danial Tse, a researcher at Google, and colleagues trained a deep-learning algorithm to detect malignant lung nodules in more than 42,000 CT scans. The resulting algorithms turned up 11% fewer false positives and 5% fewer false negatives than their human counterparts. The work is described in a paper published in Nature.
Bioengineers clear major hurdle on path to 3D printing replacement organs (Medicine - 2019-05 - Technology.org)
Bioengineers from Rice University and the University of Washington, with other collaborators, have cleared a major hurdle on the path to 3D-printing replacement organs with a breakthrough technique for bioprinting tissues. The new innovation allows scientists to create exquisitely entangled vascular networks that mimic the body’s natural passageways for blood, air, lymph and other vital fluids.
Brain signals translated into speech using artificial intelligence (AI/Brain Speech Translation - 2019-04 - Nature)
Neuroscientists have designed a device that can transform brain signals into speech, modelling the vocal system. Making the leap from single syllables to sentences is technically quite challenging and makes the device impressive. The device transforms brain signals into estimated movements of the vocal tract, and turns these movements into synthetic speech. People who listened to 101 synthesized sentences could understand 70% of the words.
DeepMind has made a prototype product that can diagnose eye diseases (AI/Ophthalmology - 2019-04 - MIT Technology Review)
The device scans a patient’s retina to diagnose potential issues in real time. The images are analyzed by DeepMind’s algorithms, which return a detailed diagnosis in about 30 seconds. The prototype system can detect a range of diseases, including diabetic retinopathy, glaucoma, and age-related macular degeneration – as accurately as top eye specialists. It may be several years before it is widely available.
AI cuts lung cancer false positives (AI/Radiology - 2019-03 - Technology.org)
Lung cancer is the leading cause of cancer deaths worldwide. Screening has a 96% false positive rate. Researchers at the University of Pittsburgh and its Hillman Cancer Center used a machine learning algorithm to substantially reduce false positives without missing a single case of cancer.
The pediatric AI that outperformed junior doctors (AI/Pediatrics - 2019-02 - Singularity Hub)
New research from Guangzhou, China, created a natural-language processing AI that is capable of out-performing rookie pediatricians in diagnosing common childhood ailments, using the same deductive reasoning that the doctors use. Currently, experienced pediatricians out-performed the AI.
AI approach outperformed human experts in identifying cervical pre-cancer (AI/Image Processing - 2019-01 - Technology.org)
National Institutes of Health and Global Good researchers have developed a deep-learning algorithm that analyzes digital images of a woman’s cervix, and accurately identifies pre-cancerous changes that require medical attention. The algorithm was better at identifying pre-cancer than a human expert reviewer of Pap tests under the microscope.
AI and the NHS: How AI will change everything for patients and doctors (AI/Dermatology - 2018-11 - ZDNet)
Healthcare: 5 digital trends for 2019 and beyond (AI/Dermatology - 2018-11 - Technology.org)
Stanford researchers create algorithm to interpret chest x-rays (AI/Dermatology - 2018-11 - Technology.org)
Google AI claims 99% accuracy in metastatic breast cancer detection (AI/Dermatology - 2018-10 - VentureBeat)
A new wave of chatbots are replacing physicians and providing frontline medical advice (AI/Dermatology - 2018-10 - MIT Technology Review)
AI algorithm used to adjust treatment dosages for metastatic cancer (AI/Dermatology - 2018-10 - Technology.org)
Machine learning outperforms clinicians in predicting outcomes for people at risk of psychosis and depression (AI/Dermatology - 2018-09 - Technology.org)
New AI system detects hard-to-spot cancerous lesions (AI/Dermatology - 2018-08 - Technology.org)
Big data and Deep Learning used to predict the fate of inpatients (AI/Dermatology - 2018-08 - ZDNet)
AI Neural network matches human cardiologists in detecting heart attacks (AI/Dermatology - 2018-07 - MIT Technology Review)
AI is better than dermatologists at diagnosing skin cancer (AI/Dermatology - 2018-05 - ScienceBlog)
Diagnostic imaging computers outperform human counterparts (AI/Diagnosis - 2018-04 - Case Western Daily)
• 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.