Artificial Intelligence Makes Bad Medicine Even Worse

Google researchers made headlines early this month for a study that claimed their artificial intelligence system could outperform human experts at finding breast cancers on mammograms. It sounded like a big win, and yet another example of how AI will soon transform health care: More cancers found! Fewer false positives! A better, cheaper way to provide high-quality medical care!

Hold on to your exclamation points. Machine-enabled health care may bring us many benefits in the years to come, but those will be contingent on the ways in which it’s used. If doctors ask the wrong questions to begin with—if they put AI to work pursuing faulty premises—then the technology will be a bust. It could even serve to amplify our earlier mistakes.

The full WIRED article can be viewed at this link.  

The 2016 California policy to eliminate nonmedical vaccine exemptions and changes in vaccine coverage: An empirical policy analysis

Vaccine hesitancy, the reluctance or refusal to receive vaccination, is a growing public health problem in the United States and globally. State policies that eliminate nonmedical (“personal belief”) exemptions to childhood vaccination requirements are controversial, and their effectiveness to improve vaccination coverage remains unclear given limited rigorous policy analysis. In 2016, a California policy (Senate Bill 277) eliminated nonmedical exemptions from school entry requirements. The objective of this study was to estimate the association between California’s 2016 policy and changes in vaccine coverage.

The full article can be downloaded below.  

 The Rise of the Data-Driven Physician

Since its inception, the Stanford Medicine Health Trends Report has examined the most consequential developments and technologies that are changing health care delivery. Our 2020 report describes a health care sector that is undergoing seismic shifts, fueled by a maturing digital health market, new health laws that accelerate data sharing, and regulatory traction for artificial intelligence in medicine.

The full report can be downloaded below.  

Cancer Statistics, 2020

Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on population-based cancer occurrence. Incidence data (through 2016) were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2017) were collected by the National Center for Health Statistics. In 2020, 1,806,590 new cancer cases and 606,520 cancer deaths are projected to occur in the United States. The cancer death rate rose until 1991, then fell continuously through 2017, resulting in an overall decline of 29% that translates into an estimated 2.9 million fewer cancer deaths than would have occurred if peak rates had persisted. This progress is driven by long-term declines in death rates for the 4 leading cancers (lung, colorectal, breast, prostate); however, over the past decade (2008-2017), reductions slowed for female breast and colorectal cancers, and halted for prostate cancer. In contrast, declines accelerated for lung cancer, from 3% annually during 2008 through 2013 to 5% during 2013 through 2017 in men and from 2% to almost 4% in women, spurring the largest ever single-year drop in overall cancer mortality of 2.2% from 2016 to 2017. Yet lung cancer still caused more deaths in 2017 than breast, prostate, colorectal, and brain cancers combined. Recent mortality declines were also dramatic for melanoma of the skin in the wake of US Food and Drug Administration approval of new therapies for metastatic disease, escalating to 7% annually during 2013 through 2017 from 1% during 2006 through 2010 in men and women aged 50 to 64 years and from 2% to 3% in those aged 20 to 49 years; annual declines of 5% to 6% in individuals aged 65 years and older are particularly striking because rates in this age group were increasing prior to 2013. It is also notable that long-term rapid increases in liver cancer mortality have attenuated in women and stabilized in men. In summary, slowing momentum for some cancers amenable to early detection is juxtaposed with notable gains for other common cancers. 

The full report can be downloaded below.  

2020 Predictions: The Health IT Year Ahead​

When I started my career almost 40 years ago, I had no idea just how gratifying it would be to work in an industry devoted to helping people get, feel and stay better. Focusing on the IT side of healthcare has been nothing short of inspiring, always pushing forward, proactively paving the way to safer, more secure patient care.

I’ve learned a lot along the way, and look forward to what’s next. My bet is that safety and security will be at the wheel in 2020. I recently shared my top predictions of what we can expect in the year ahead with MedCity News. Check them out below:

1. Trust emerges as a major competitive differentiator
2. Information blocking rules will matter
3. The real-world impact of price transparency becomes evident
4. Time to therapy for specialty medications decreases with better information flow
5. Artificial intelligence chips away at the cognitive burden on physicians
6. Opioid prescribing becomes smarter
7. Pharmacies become sites of care delivery
8. Consumer-oriented digital health services are challenged to mature

The full Surescripts article can be viewed at this link.  

Deep Learning in Medical Imaging

The artificial neural network (ANN), one of the machine learning (ML) algorithms, inspired by the human brain system, was developed by connecting layers with artificial neurons. However, due to the low computing power and insufficient learnable data, ANN has suffered from overfitting and vanishing gradient problems for training deep networks. The advancement of computing power with graphics processing units and the availability of large data acquisition, deep neural network outperforms human or other ML capabilities in computer vision and speech recognition tasks. These potentials are recently applied to healthcare problems, including computer-aided detection/diagnosis, disease prediction, image segmentation, image generation, etc. In this review article, we will explain the history, development, and applications in medical imaging.

The full article can be downloaded below.  

Epigenetics Analysis and Integrated Analysis of Multiomics Data, Including Epigenetic Data, Using Artificial Intelligence in the Era of Precision Medicine

To clarify the mechanisms of diseases, such as cancer, studies analyzing genetic mutations have been actively conducted for a long time, and a large number of achievements have already been reported. Indeed, genomic medicine is considered the core discipline of precision medicine, and currently, the clinical application of cutting-edge genomic medicine aimed at improving the prevention, diagnosis and treatment of a wide range of diseases is promoted. However, although the Human Genome Project was completed in 2003 and large-scale genetic analyses have since been accomplished worldwide with the development of next-generation sequencing (NGS), explaining the mechanism of disease onset only using genetic variation has been recognized as difficult. Meanwhile, the importance of epigenetics, which describes inheritance by mechanisms other than the genomic DNA sequence, has recently attracted attention, and, in particular, many studies have reported the involvement of epigenetic deregulation in human cancer. So far, given that genetic and epigenetic studies tend to be accomplished independently, physiological relationships between genetics and epigenetics in diseases remain almost unknown. Since this situation may be a disadvantage to developing precision medicine, the integrated understanding of genetic variation and epigenetic deregulation appears to be now critical. Importantly, the current progress of artificial intelligence (AI) technologies, such as machine learning and deep learning, is remarkable and enables multimodal analyses of big omics data. In this regard, it is important to develop a platform that can conduct multimodal analysis of medical big data using AI as this may accelerate the realization of precision medicine. In this review, we discuss the importance of genome-wide epigenetic and multiomics analyses using AI in the era of precision medicine.

The full article can be downloaded below.  

Artificial intelligence has come to medicine. Are patients being put at risk?

Health products powered by artificial intelligence are streaming into our lives, from virtual doctor apps to wearable sensors and drugstore chatbots.

IBM boasted that its AI could “outthink cancer.” Others say computer systems that read X-rays will make radiologists obsolete. AI can help doctors interpret MRIs of the heart, CT scans of the head and photographs of the back of the eye, and could potentially take over many mundane medical chores, freeing doctors to spend more time talking to patients, said Dr. Eric Topol, a cardiologist and executive vice president of Scripps Research in La Jolla.

The full Los Angeles Times article can be viewed at this link.  

Money Can’t Buy You Health: Disconnection In U.S. Between Healthcare Spending And Population Health

Let me begin this sobering post by saying there are aspects of the U.S. healthcare system that are admirable, especially regarding the use of cutting-edge innovative medicines and medical procedures. In cancer care, in particular, the U.S. has been at the forefront of a number of advances which have delivered miraculous benefits to patients. Yet, on the whole, America’s healthcare system appears “woefully dysfunctional.”

The U.S.  spends about twice as much on healthcare as other Organization for Economic Cooperation and Development (OECD) countries, but ranks near the bottom in terms of life expectancy, and that gap has widened sharply in recent years.

The full Forbes article can be viewed at this link.  

What will 2020 bring for medicine and science? We asked 16 leaders for predictions

Last year, when we asked science and health care soothsayers to peek ahead to 2019, they told us that methamphetamine use would rise (it did), tumor organoids would near clinical use for personalizing cancer treatment and better targeting clinical trials (that’s happening), and price transparency wouldn’t bring lower health spending (that’s true, too). But nobody predicted the outbreak of lung injuries tied to vaping, the failure and attempted resurrection of Biogen’s Alzheimer’s drug aducanumab, or the restoration of cellular functions in pig brains after death.

We’re back with a new set of predictions for 2020. Let’s see how our experts do this time.

The full STAT article can be viewed at this link.