Machine Learning Viewpoint

AI will never replace the doctor. Or will it?

Let me start with the usual and widely accepted narrative: AI is emerging as a major technological disrupter in medicine, crunching lots of data, providing accurate diagnosis and treatment. But it will (and can) never replace a doctor even in specialties amenable to machine-driven automation such as radiology or dermatology​1​. However, these assumptions are based on the current paradigms of medicine constrained by the boundaries of current cognitive abilities. Are we oblivious to a paradigm shift happening in medicine?

The human genome mapping project​2​ and the subsequent democratization of the ‘omics’ fields promised the new paradigm of ‘personalized medicine’ which never really materialized (at least till now)​3​. AI (used here as an encompassing term including big data analytics and machine learning) can potentially take personalized medicine to the realm of holistic medicine. Time will tell whether this paradigm shift will materialize. But it is important to understand how some of the concepts that we take for granted may get redefined and reconceptualized in the new paradigm (if it happens), just as modern medicine emerged from natural and alternative medical traditions.

The major tenets of modern medicine are diagnosis, prognosis and therapeutics (treatment). Diagnosis is the process of bucketing a given case into a pattern of observations that has been previously characterized — often represented by a recognizable name. Diabetes, Hypertension and typhoid fever are examples. The prognosis and the treatment depend on the diagnostic label assigned. Patterns that do not fit into the list emerge from time to time. A pattern that resembled pneumonia that emerged recently in Wuhan, China, caused by a coronavirus was labelled SARS-Cov-2. A common use case of AI in medicine is to assign a given set of observations into one of these named entities (diagnostic decision support systems). The clinical community argues that AI can help a clinician in this process, but cannot replace him or her. One of the main reasons for the clinician’s self-belief in irreplaceability is the fact that AI learns from existing labels — the training data set — that the clinicians themselves prepare.

The process of making a diagnosis is to reduce the stochastic observations in the human body into a set of named patterns (diagnoses) that humans can comprehend, identify and utilize. In an AI-dominated world ‘diagnoses’ lose their relevance as the machines can recognize, identify and utilize a potentially infinite number of patterns and entities. Even if ‘diagnoses’ exist, their number is likely to be huge, much beyond the cognitive capabilities of humans.

Currently, the prognosis of any disease state is based on limited observations and limited data points. Big data will extend these limits thereby making prognostic predictions more accurate. Machine learning models that drive such predictions are likely to be at best partially explainable and at worst complete black boxes. However, explainable or not, such prognostic predictors are likely to improve health system optimizations. The role of clinicians is going to be identifying the variables to optimize.

In the therapeutics realm, AI may push us closer to the promised personalized medicine. Traditional clinical research relying mostly on the ‘rigorous’ randomized controlled trials (RCT) may lose its relevance in the new paradigm. Some argue that RCTs have already become unsustainable with long turnover times and mounting costs. With no two humans having the same omics profile — the level of abstraction introduced by a statistically significant difference between the ‘random’ treatment and control groups — is useful for humans, but not for AI. The emerging methods such as nanotechnology, nanorobotics and 3D printing, combined with advanced predictive analytics, molecular modelling and drug designing would lead to tailored interventions that are created ‘just-in-time’ for every individual according to his or her needs. This process is likely to be beyond the reach of human comprehension, but human intervention may be needed to maintain the flow of data through the system.

‘Health’ is another concept that is taken for granted as something that everybody can instinctively understand. Health is widely recognized as a state of absence of disease. As disease/diagnosis states become infinite, ‘health’ may need a reconceptualization too. Let us call it Health 3.0 for now. Medicine ceases to be the art of restoring health but optimizing Health 3.0. I do not attempt to provide a framework to define Health 3.0 here, but posit that it will include abstract concepts such as happiness and quality of life, paradoxically beyond the cognitive capabilities of AI.

Clinicians may still be irreplaceable, but in helping AI to define health!
Some of the changes that AI and allied technologies can bring are already visible. The omics fields have introduced several subcategories of existing diagnostic entities​4​. In most cases, clinicians ignore these subtypes, seeing things at a higher and manageable level. Reinforcement Learning (RL) algorithms can potentially learn from big data that are not labelled by clinicians​5​. RL is closer to cognitive computing — computerized models that simulate human thought — optimizing ‘reward’, a concept closer to Health 3.0. Computer-aided drug design is becoming increasingly popular supplemented by an enormous amount of data derived from electronic medical records​6​.

I am neither trying to predict the future impact of AI in medicine nor arguing for or against the role of ‘human’ clinicians. The media and the scientific literature are replete with stories of AI approaching and in some cases surpassing, the clinicians in certain tasks. AI may not be an incremental disrupter that may change the way we practice. As paradigms change, some of the questions that we ask today such as — Can AI make the correct diagnosis, Can AI choose the correct treatment — may lose relevance? AI may never replace doctors, but it may change what doctors do and may take us a step closer to holistic medicine!


  1. 1.
    Karches KE. Against the iDoctor: why artificial intelligence should not replace physician judgment. Theor Med Bioeth. Published online April 2018:91-110. doi:10.1007/s11017-018-9442-3
  2. 2.
    Collins FS. The Human Genome Project: Lessons from Large-Scale Biology. Science. Published online April 11, 2003:286-290. doi:10.1126/science.1084564
  3. 3.
    Chen R, Snyder M. Promise of personalized omics to precision medicine. WIREs Syst Biol Med. Published online November 26, 2012:73-82. doi:10.1002/wsbm.1198
  4. 4.
    Boyd S, Galli S, Schrijver I, Zehnder J, Ashley E, Merker J. A Balanced Look at the Implications of Genomic (and Other “Omics”) Testing for Disease Diagnosis and Clinical Care. Genes. Published online September 1, 2014:748-766. doi:10.3390/genes5030748
  5. 5.
    Chen M, Herrera F, Hwang K. Cognitive Computing: Architecture, Technologies and Intelligent Applications. IEEE Access. Published online 2018:19774-19783. doi:10.1109/access.2018.2791469
  6. 6.
    Qian T, Zhu S, Hoshida Y. Use of big data in drug development for precision medicine: an update. Expert Review of Precision Medicine and Drug Development. Published online May 4, 2019:189-200. doi:10.1080/23808993.2019.1617632
Cite this article as: Eapen BR. (July 7, 2021). - AI will never replace the doctor. Or will it?. Retrieved September 26, 2022, from

Death by beep? Bad sound design costs lives

This article was first published on Brighter World. Read the original article.

Medical alarms have appeared on the Emergency Care Research Institute’s list of top medical hazards four times — twice in the number one spot. According to a recent FDA survey, bad sound design for medical devices accounted for 566 deaths over four years, mostly because the sounds can be so annoying that they get turned down so doctors and nurses can concentrate, leading to potentially deadly consequences.

In this TEDx McMaster talk from February 2021, Michael Schutz, an associate professor of music cognition and percussion, explains how his research with the Music Acoustics Perception Learning (MAPLE) lab is helping to create better alarms — and better patient outcomes.

Read the papers related to this research

Healthcare Analytics Information Systems OpenSource


TL;DR Below is an open-source common-line tool for converting an OHDSI OMOP cohort (defined in ATLAS) to a FHIR bundle and vice versa.

Originally published by Bell Eapen at on July 22, 2020. If you have some feedback, reach out to the author on Twitter,  LinkedIn or  Github.

OHDSI OMOP CDM is one of the most popular clinical data models for health data warehouses. The simple, but clinically motivated data structure is intuitively appealing to clinicians leading to its good adoption. In this respect, it has overtaken HL7-V3 which is more robust but has a steeper learning curve, especially for clinicians. The OHDSI OMOP CDM is widely used in the pharmaceutical industry for drug monitoring.

FHIR is emerging as the defacto standard for health system interoperability, owing largely to its simplicity and the use of existing and popular standards such as REST. As NoSQL databases become more and popular in healthcare, FHIR can also be a good persistence schema. It aligns well with search technologies such as elasticsearch.

As both standards are popular, conversion from one to the other may be commonly required. Researchers at Georgia Tech have an open-source tool – GT-FHIR2 – for mapping an existing OHDSI OMOP CDM database as FHIR endpoint. However, conversion between existing systems may not be easy with a full-stack solution. 

I have a simpler solution that I believe will be useful in the following scenarios:

  • To export a cohort to a FHIR based analytics tool.
  • To load new resources to OMOP CDM databases for incremental ETL.

Omopfhirmap is a command-line tool for mapping a OHDSI cohort, defined in ATLAS, to a FHIR bundle that can be optionally submitted to a FHIR server for processing. Conversely, it can process a FHIR bundle and add resources to an existing CDM database ignoring duplicates. Unlike GT-FHIR2, the OMOP on FHIR Project at Georgia Tech omopfhirmap does not expose OMOP database as FHIR endpoints. 

I have used spring-boot and JPA for easy wiring of services and abstraction of database and the hapi-fhir as it is an obvious choice for any java based FHIR applications. It is still work in progress and any help will be appreciated (Refer to

Health Research Methodology Healthcare Analytics

OHDSI OMOP CDM ETL Tools in Python, .Net and Go

TL;DR Here are few OHDSI OMOP CDM tools that may save you time if you are developing ETL tools!

Originally published by Bell Eapen at on June 11, 2020. If you have some feedback, reach out to the author on Twitter,  LinkedIn or  Github.

Python: pyomop | pypi
.NET: omopcdmlib | NuGet
Golang: gocdm

The COVID-19 pandemic brought to light many of the vulnerabilities in our data collection and analytics workflows. Lack of uniform data models limits the analytical capabilities of public health organizations and many of them have to re-invent the wheel even for basic analysis. As many other sectors embrace big data and machine learning, many healthcare analysts are still stuck with the basic data wrenching with Excel.

The OHDSI OMOP CDM (Common data model) for observational data is a popular initiative for bringing data into a common format that allows for collaborative research, large-scale analytics, and sharing of sophisticated tools and methodologies. Though OHDSI OMOP CDM is primarily for patient-centred observational analysis, mostly for clinical research, it can be used with minor tweaks for public health and epidemiologic data as well. We have written about some of the technical details here.

The OHDSI OMOP CDM is relatively simple and intuitive for clinical teams than emerging standards such as FHIR. Though the relational database approach and some of the software tools associated with OHDSI OMOP CDM are archaic, the data model is clinically motivated. There is an ecosystem of software tools for many of the analytics tools that can be used out of the box. The Observational Medical Outcomes Partnership (OMOP) CDM, now in its version 6.0, has simple but powerful vocabulary management. OHDSI OMOP CDM is a good choice for healthcare organizations moving towards health data warehousing and OLAP.

One weakness of OHDSI is the lack of tools for efficient ETL from existing EHR and HIS. Converting existing EHR data to the CDM is still a complex task that requires technical expertise. During the additional “home time” during the COVID pandemic, I have created three software libraries for ETL tool developers. These libraries in Python, .NET and Golang encapsulated the V6.0 CDM and helps in writing and reading data from a variety of databases with the V6.0 tables. The libraries also support creating the CDM tables for new databases and loading the vocabulary files.

Python: pyomop | pypi
.NET: omopcdmlib | NuGet
Golang: gocdm

These libraries might save you some time if you are building scripts for ETL to CDM. They are all open-source and free to use in your tools. Do give me a shout if you find these libraries useful and please star the repositories on GitHub.

Health Research Methodology Healthcare Analytics OpenSource

DADpy: The swiss army knife for discharge abstract database

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