Artificial intelligence (AI), first described in 1955, is the science and engineering
of making intelligent computer programs. AI can be described as “an entity (or
collective set of cooperative entities), able to receive inputs from the environment,
interpret and learn from such inputs, and exhibit related and flexible behaviors and
actions that help the entity achieve a particular goal or objective over a period
of time.”  The ultimate goal of AI is to use machine simulation of human intelligence
processes such as learning, reasoning, and self-correction, to mimic human decision
process. AI is fast emerging as an omnipotent solution for diverse health-care
management problems. Worldwide spending for AI is expected to grow to $52.2 billion
in 2021. Pharmaceutical research and discovery, the second fasted growing discipline,
was estimated to grow at 70.5% CAGR between 2016 to 2021.
AI encompasses a variety of techniques: machine learning (ML), deep learning (DL),
natural language processing (NLP), and optical character recognition (OCR). ML, widely
used in pharma industry, creates data analytical algorithms and mathematical models
to extract features from sample data, with the objective of making predictions or
5] ML is divided into (1) unsupervised learning applied for data extraction and (2)
supervised learning employed for predictive modeling. DL is a class of ML methods
based on artificial neural networks that use multiple hidden layers to progressively
extract and handle complex data from raw input.[3
5] NLP, another area applied to drug development, is utilized to extract meaning from
textual information or natural language data.[3
5] OCR utilizes pattern recognition, and computational vision, with the objective
of electronically converting images of typed, handwritten, or printed text into machine-encoded
ML-based applications are utilized in diverse health-care domains – early disease
prediction, diagnosis, and treatment, outcome prediction and prognosis evaluation,
 personalized treatments, behavior modification, drug discovery, manufacturing,
clinical trial research, radiology and radiotherapy, smart electronic health records,
and epidemic outbreak prediction. Although AI had potential utility in the COVID-19
pandemic for tracking and prediction, diagnosis and prognosis, treatments and vaccines,
and social control, its value was limited by lack of data, and too much data, and
by constraints of data privacy.
In this issue of the journal, Karekar et al. have reported on 159 AI studies registered
in Clinicaltrials. Gov. The most common studies were in oncology, cardiology, ophthalmology,
psychiatry, and neurology. Majority studies were for devices and diagnostics. Although
this was an audit of registered studies, the authors have not discussed the quality
of studies. Liu et al., in a recent systematic review and meta-analysis of more than
30,000 AI-based diagnostic studies, found that diagnostic performance of DL models
was equivalent to that of health-care professionals. However, <1% of studies had
sufficiently high-quality design and reporting to be included in the meta-analysis.
Recent guidelines – SPIRIT-AI Extension and CONSORT-AI Extension – are expected to
promote transparency and completeness for clinical trial protocols for AI.[10
For pharma industry, AI is becoming a versatile tool, which can be applied in all
stages of drug development, such as identification and validation of drug targets,
designing new molecules, repurposing of old drugs, improving efficiency of clinical
trial conduct, and pharmacovigilance (PV).[3
13] AI is specifically tried in clinical drug development, which is plagued by high
costs and high failure rates. DL has exhibited remarkable success in identifying potential
new drug candidates and improving prediction of their properties and the possible
safety risks. AI can improve the efficiency of search for correlation between
indications and biomarkers and help in selecting lead compounds which could have a
higher chance of success during clinical development. DSP-1181, a molecule for
obsessive–compulsive disorder, created using AI, has entered a Phase I trial.
AI offers the promise of transforming crucial steps of clinical trial conduct-study
design, planning, and execution. ML, DL, NLP, and OCR can be used for linking big
and diverse datasets such as electronic medical records (EMRs), published medical
literature, and clinical trial databases to improve recruitment by matching patient
characteristics to selection criteria.
AI can help in enhancing patient selection by:
Reducing population heterogeneity. This could be done by harmonization of large EMR
data from diverse formats and different levels of accuracy and by leveraging electronic
By prognostic enrichment – selecting patients who have a higher probability of having
a measurable clinical endpoint. ML techniques, using key biomarkers of Alzheimer's
disease (AD), are deployed for prognostic enrichment 
By predictive enrichment – choosing a population with a better likelihood of responding
to a treatment. For early AD, a clinical trial simulation tool developed by modeling
drug, disease, and progression of disease, which helped in predictive enrichment,
has undergone regulatory review.
AI systems can be utilized for automatic analysis of EMR and clinical trial digital
eligibility databases and match these with recruiting clinical trials from trial announcement,
social media, or registries. It can also help patients become aware of clinical
trials of interest sooner and allow them to approach investigator sites for evaluation
of eligibility. AI-based clinical trial matching has facilitated an increase in
enrollment in a lung cancer trial by 58.4%.
AI techniques, in combination with wearable technology, are valuable in efficient,
real-time, and personalized monitoring of patients automatically and continuously
during the trial. This can improve compliance with protocol requirements and reliability
of assessment of endpoints. DL models, by analyzing data from wearable sensors
and video monitoring, can generate patient-specific disease diaries adapted to behavioral
changes and disease expression. Such dynamic disease diaries facilitate efficient
and reliable collection of compliance and endpoints. ML technologies, approved for
detection of medical images, would play an important role in image-based endpoint
detection. ML-based algorithms have been tried to determine the smallest and fewest
doses required to shrink brain tumor, while reducing chemotherapy adverse effects,
in simulated trials. This could reduce the risk of dropouts due to safety issues.
AiCure, an AI-based mobile application to measure medication adherence, increased
compliance by 25% in a Phase II trial of schizophrenia, compared to conventional modified
directly observed therapy.
AI-assisted patient-monitoring systems, employing images and videos from wearable
sensors, have been recently tested. Wearable device is a device which can perform
a measurement or data-processing activity, and which is fully functional while attached
to the human body directly or indirectly through clothing, but which does not have
a hardwired connection to any other nonwearable device. ML models, coupled with
wearable devices, have been applied in automatic detection of cognitive and emotional
states, in monitoring participants in Parkinson's disease trials, and in assessing
quality of sleep in neurology trials. ML, NLP, and OCR could help in analyzing
unstructured medical records in paper format for real-world evidence studies in Indian
ML and NLP have been used to automatically detect adverse events and drug–drug interactions.
Cognitive services, a combination of ML and NLP algorithms, have been identified and
developed to solve specific tasks in the PV process of Individual Case Safety Reports,
which would require human intelligence. Such AI techniques can reduce the cognitive
burden of PV professionals and improve efficiencies of various PV processes.
Despite rapid advances in AI technologies for clinical drug development, implementation
of AI is facing a variety of challenges.[3
13] Major hurdles for EMR data mining are accessibility, digitization, and data integrity.
Harmonization, interoperability of diverse formats, and standardization are common
issues for all technologies such as EMR and wearable devices. Difficulties of mining
large data sets of genomic data, past clinical studies, journal articles, and related
real-world data, potentially distributed across multiple institutions and geographies,
Regulatory environment on data privacy restricts access to individual patient data.
Similar legal barriers of data privacy and security impact clinical trial matching
The Food and Drug Administration (FDA) considers AI/ML-based software as a medical
device. FDA would expect the AI innovators to comply with requirements of clinical,
analytical, and technical validation, quality systems, good machine learning practice,
assurance of safety and effectiveness, transparency, and real-world performance monitoring.
Any new AI technology, which proposes to improve the efficiency of clinical trial
design and conduct, should be validated by testing alongside the existing technology
it claims to complement or substitute.
The regulatory agencies and the end users would expect that AI technology should be
understandable, ethical, replicable, and scalable. Finally, there are also personnel
issues such as availability of personnel with requisite technical skills  and
fear of job loss which may delay the adoption of AI technology.
AI is not a quick fix panacea which can improve efficiencies of clinical trials overnight.
Man and machine are still on the learning curve! Hence, pharma industry will have
to invest substantial effort, money, and time – 5–8 years to realize the benefits
of novel AI tools.